Sickness absence in female- and male-dominated occupations and workplaces

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Social Science & Medicine 60 (2005) 2261–2272 www.elsevier.com/locate/socscimed

Sickness absence in female- and male-dominated occupations and workplaces Arne Mastekaasa Faculty of Social Sciences, Department of Sociology and Human Geography, University of Oslo, P.O. Box 1096, Blindern, N-0317 Oslo, Norway Available online 8 December 2004

Abstract Previous research suggests that both men’s and women’s level of sickness absence may be systematically related to the gender composition of their workplace as well as of their occupational category. The number of studies is, however, low and the composition of the occupational category has often been used as a proxy for the composition of the workplace. This paper employs a large data set broadly representative of the employed population of Norway. The data make it possible to take workplace and occupation simultaneously into account. Thus, the relationship between the gender composition of the workplace and sickness absence is estimated with detailed control for differences between occupational categories. Likewise, the importance of the gender composition of the occupation is assessed with control for between workplace variation. Men’s sickness absence turns out to be largely unrelated to the gender composition of the workplace. For women the level of sickness absence tends to be higher in female-dominated workplaces, but the relationship is weak. These findings provide evidence against theories suggesting that the minority sex in the workplace faces special problems and is therefore more absent. They are to some extent consistent with the idea that femaledominated workplaces develop norms that are more tolerant towards sickness absence. The relationship of sickness absence to the gender composition of the occupational category is similar to the U-shaped pattern found in several previous studies (highest sickness absence both in strongly male-dominated and strongly female-dominated occupations), but again the relationship is weak. r 2004 Elsevier Ltd. All rights reserved. Keywords: Workplace health; Gender segregation; Sickness absence; Norway

Introduction A large amount of research shows that women most often have higher rates of sickness absence than men (see, e.g., Barmby, Ercolani, & Treble, 2002; Mastekaasa & Olsen, 1998). In Norway, this gender difference has tended to increase over time, but in the last 10 years it Fax: +47 22 85 52 53.

E-mail address: [email protected] (A. Mastekaasa).

has been quite stable with women 40–50% more absent than men.1 More limited evidence suggests that there may also be systematic differences in sickness absence between female- and male-dominated occupations and/ or workplaces. More specifically, several studies suggest 1 The development until 2000 is described (in Norwegian) at http://www.idebanken.org/ressurser/binaryfile.asp?filID=84 (accessed at July 14, 2004). Statistics for 2000–2003 (in English) are found at http://www.ssb.no/english/subjects/06/02/sykefratot_en/ (accessed at July 14, 2004).

0277-9536/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2004.10.003

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that men as well as women tend to have higher absence rates in occupations or workplaces numerically dominated by the opposite sex (Alexanderson, Leijon, A˚kerlind, Rydh, & Bjurulf, 1994; Evans & Steptoe, 2002; Hensing, Alexanderson, A˚kerlind, & Bjurulf, 1995; Tsui, Egan, & O’Reilly, 1992). Although the evidence on the relationship between gender segregation and sickness absence is quite limited, the more general issue of how gender segregation in the workplace affects men and women has received considerable attention in organizational research (for overviews, see, e.g., Williams & O’Reilly, 1998; Reskin, McBrier, & Kmec, 1999). Particularly influential has been Kanter’s (1977) theory of ‘‘tokenism’’. Kanter suggests that small minorities, like women in predominantly male workplaces, are faced with special problems. The basic issue is that members of small minorities are not perceived and treated as individuals but rather as representatives or ‘‘tokens’’ of their category. This may have a number of negative effects on social relationships in the workplace, and increase levels of stress (Hunt & Emslie, 1998). A related albeit different idea is that traditionally privileged majorities may feel that their advantaged position is threatened by the minority, and that the minority is therefore subject to various kinds of hostile behaviour (Blalock, 1967). Much of the relevant empirical literature both within the field of sickness absence research and in the organisational literature suffers from a number of weaknesses. As far as the present author is aware, all previous studies measure the gender composition either within occupational categories or within workplaces. In studies measuring the gender composition of occupational categories, the effects of working in a male- or female-dominated environment will easily be confounded with broader differences in working conditions between traditionally male and female jobs (Glass, 1990). This problem will also be present in studies measuring workplace gender composition unless detailed control for occupation or working conditions is introduced. In addition, most studies are based on small and often unrepresentative samples (with some exceptions, e.g., Alexanderson et al., 1994; Hensing et al., 1995). In this paper, I use a very large data set (N ¼ 156; 000) that is broadly representative of the population of employees in Norway. The data makes it possible to take workplace and occupation simultaneously into account. Thus, the relationship between the gender composition of the workplace and sickness absence is estimated with detailed control for differences between occupational categories. Likewise, the importance of the gender composition of the occupation will be assessed with control for between workplace variation.

Theories and hypotheses The sickness absence concept Sickness absence is not identical with disease, sickness, or impaired health status. Rather, it is most reasonably regarded as an illness behaviour, being defined as ‘‘the manner in which persons monitor their bodies, define and interpret their symptoms, take remedial action, and utilise various sources of help as well as the more formal health care system’’ (Mechanic, 1986, p. 101). It follows that the gender composition of the work setting may influence sickness absence through its effects on an employee’s health, and also by affecting how people react and behave in the presence of a health problem. There is no clear boundary between sickness absence and other kinds of absence. What some may regard as mere unwillingness to work, e.g., others may regard as a psychiatric problem. At the operational level, sickness absence may be defined as absence certified by a physician or as reported as due to absence by the employee her or himself (self-certification). The present paper includes physician legitimated absences only. Gender composition and group relations The most prominent theoretical interpretations of why the gender composition may affect individual employees all focus on the relationship or interaction between the majority and the minority category. Kanter (1977) developed her theory in the context of a study of men and women in managerial positions in a large corporation. She argued that female managers face particular problems, not because they are women, but because they are in a small minority in traditionally male jobs. She suggested three psychological mechanisms by which the minority status may have adverse effects on the individual. In the first place, a woman in a work group consisting mainly of men will stand out and be especially visible. What she says and does will receive special attention, and she is therefore likely to experience more performance pressure than males do. The second mechanism is assimilation. A minority member is less likely to be perceived as an individual but more likely to be perceived as a representative of her category. Her actions will be more likely to be noticed and remembered to the extent that they fit in with the majority’s preconceived notions. Thus, she is more subject to stereotyping. The third mechanism is contrast or polarisation. The presence of one or a few minority members makes the commonalities among the majority members more obvious, and the minority members appear as a deviant contrast.

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Kanter (1977) does not specifically consider how the mechanisms she suggests may affect health or absence behaviour. As noted by Hunt and Emslie (1998), however, it seems likely that the processes described by Kanter will involve increased stresses for the individual and thereby negative effects both on health and on the motivation to go to work. In addition to increased stress, minority members may also have less access to social support in the workplace (cf. Hensing et al., 1995, p. 43). Blau (1977) suggested a theory of intergroup relations that is similar to Kanter’s in important respects. Blau expects the relationship between the members of two groups (e.g. men and women, or blacks and whites) to improve with the amount of interaction between them. The smaller a minority is the less likely majority members are to interact with its members. Both Kanter (1977) and Blau (1977) suggest that there are adverse effects associated with minority status as such. Thus, men will experience problems in strongly female-dominated settings to the same extent that women do in strongly male-dominated settings. Blalock’s (1967) theory, on the other hand, is asymmetric since he distinguishes between traditionally dominant and dominated groups. The theory was originally suggested to explain racial discrimination against black people. Blacks are believed to meet with problems and discrimination in settings dominated by whites, but whites do not suffer when blacks are in the majority. Thus, it may be more appropriate to talk about a dominant and a subordinate group rather than a majority and a minority (Blalock, 1967, p. 145). Blalock’s theory is also different, in that it does not assume that discrimination against the minority is greater the greater the numerical disparity between the two groups, but in fact expects the opposite relationship. This is based on the assumption that the majority does not feel threatened by and therefore tolerates a minority as long as it is sufficiently small. When the size of the minority category increases, majority members feel more threatened and are more motivated to act in a hostile way. Thus, the empirical prediction is more or less opposite to that of Kanter and Blau. There are a number of problems involved in applying Blalock’s theory to women’s sickness absence in particular and to gender relations more generally. In the first place, Blalock (1967) focuses on race relations only. Although many authors later have regarded Blalock’s theory as a general theory of group relations, and therefore also as applicable to gender relations, the validity of this generalisation is not obvious. Blalock’s theory was developed in a period of strongly antagonistic and violent relationships between blacks and whites in the US. The relationships between men and women in the workplace are typically much

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less hostile and possible antagonisms are likely to be more subtle. Given the problems in applying Blalock’s theory to the relationship between gender composition and sickness absence, I shall focus here on Kanter’s and Blau’s theories. Both these theories imply that women’s sickness absence should decline as one moves from strongly male-dominated towards gender-balanced workplaces. Similarly, men’s sickness absence should decline from strongly female-dominated to genderbalanced workplaces. Note that both these theories imply a non-linear effect: it is stressful to be in a small minority (and more stressful the smaller the minority is), but no difference is expected between gender-balanced workplaces and workplaces dominated by one’s own gender. Gender segregation and within-group processes The theories discussed so far focus on intergroup relations—between a majority and a minority. Minority members are believed to suffer in various ways because of the behaviour of majority members. Theories of absence culture focus instead on the processes within groups by which group-specific norms and standards develop (Chadwick-Jones, Nicholson, & Brown, 1982; Nicholson & Johns, 1985). The absence culture may include relatively explicit norms both about acceptable levels of absence and about legitimate reasons for absence. But it may also operate in a more indirect way as employees ‘‘observe the absence behaviour of others and the reactions of various constituencies to this behaviour and then adopt a pattern or level of absence that reflects these observations’’ (Nicholson & Johns, 1985, p. 396; also see Geurts, Buunk, & Schaufeli, 1994). Absence culture theory is relevant in the present context to the extent that male- and female-dominated groups may be expected to develop different norms and ideas about absence behaviour. Building on Gutek (1985), Konrad, Winter and Gutek (1992, p. 121) suggest that ‘‘[i]n female-dominated work groups, the female sex-role spills over to identify the nature and functioning of the group’’. A female-dominated work culture may therefore come to stress stereotypically feminine characteristics like nurturing, sensitivity, empathy, or sexual attractiveness. Traditionally male groups, on the other hand, may develop work cultures characterised by ‘‘stereotypically masculine traits such as aggressiveness, competitiveness, and emotional rationalityy’’ (Konrad et al., 1992, p. 121). To the extent that such processes are operating female-dominated workplaces may develop more tolerant attitudes towards sickness absence than will male-dominated ones, in which case women’s sickness absence will tend to increase with the proportion of women in the workplace.

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Workplace or occupational category composition?

National context

The theories considered above focus on the actual interaction between individuals in a social setting, either between minority and majority members or internally within a given group. Thus, they seem more relevant for explaining the effect of the gender composition of the workplace than of the occupation. When empirical studies based on these theories have nevertheless measured the gender composition in occupational categories, the reason often seems to be that such data are more easily available (e.g., Wharton & Baron, 1987). This is not to say, however, that the relationship between occupational gender composition and sickness absence may not be of interest in its own right. A large number of studies have shown that female-dominated jobs and occupations tend to be characterised by relatively low earnings, few career opportunities, and a low degree of representation in authority positions (Reskin & Padavic, 1994; Wright, Baxter, & Birkelund, 1995). It is less clear whether women are also worse off in terms of job and employment conditions more generally and specifically with regard to the health effects of work. Some traditionally male jobs, in the construction and manufacturing industries in particular, are undoubtedly associated with relatively high health risks. Some economists argue that women tend to prefer jobs that are both less human capital intensive and less demanding and easier to combine with housework and care for children, while men on average choose more human capital intensive, demanding and/or hazardous jobs (Filer, 1985). A conflicting argument is that sex segregation results from discrimination of women, who thereby end up in jobs that are inferior not only in pecuniary terms but also with regard to working and employment conditions generally (Glass, 1990; Roxburgh, 1996). In line with the latter view, it has been argued that research on occupational hazards has suffered from a male bias and has to a large extent neglected health risks primarily found in female-dominated occupations (Doyal, 1995, p. 156ff.). Researchers have also pointed out that women tend to score low on health relevant dimensions like on-the-job control and the substantive complexity of the work (Karasek, Gardell, & Lindell, 1987). These arguments are not necessarily contradictory. One possibility is that both male-dominated and femaledominated jobs may be associated with relatively high sickness absence, and that sickness absence may be at its lowest in relatively gender-balanced occupations. Such a U-shaped relationship between these variables has in fact been found in several empirical studies (Alexanderson et al., 1994; Hensing et al., 1995). A U-shaped relationship is therefore a reasonable hypothesis for the present study.

Along with Sweden, Norway has comparatively high rates of sickness absence (Nyman, Bergendorff, & Palmer, 2002). A number of factors are undoubtedly involved in producing national differences, one of them probably being the high labour force participation rates in the Scandinavian countries, particularly for women. Another factor is that the Norwegian national sickness pay scheme is very generous, providing 100% wage compensation for nearly all employees. The implications of these factors for the relationship between the gender composition and sickness absence—if any—are not obvious. One possibility is that the high level of sickness absence compensation in Norway allows for a greater impact of non-economic factors—those who are ill or unable to cope with stresses at their workplace do not have to go to work to maintain their income. The level of gender segregation in the labour market is very high in Norway and other Scandinavian countries (Jarman, Blackburn, Brooks, & Dermott, 1999). This means that a relatively large part of the working population will be in strongly female- and maledominated workplaces and occupations. Whether the effects of a given level of segregation are also stronger in these countries is, however, an open question.

Data and statistical methods Sample I use a subsample from the Norwegian 1990 Census, which has been supplemented by data from various administrative registers. The 1990 Census was conducted as a sample survey of about 10% of the population. People in rural areas were oversampled. The analyses below include employees 18–64 yr of age who had been employed (4 h per week or more) for at least part of the 9 months observation period. Self-employed and state employees are excluded, the latter due to lack of information on sickness absence. I include only workplaces with more than five-sampled employees, since it makes little sense to estimate the gender composition in the workplace with a sample consisting of only a couple of employees. This means that in general only workplaces with more than 50 employees are included in the analyses. The resulting sample comprises 156,972 individuals. Variables Sickness absence The data provide information only on absence spells of more than 2 weeks duration. In the analyses, I measure sickness absence by a simple dichotomous

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variable distinguishing between at least one sickness absence spell on the one hand and no sickness absence on the other during the period of observation (9 months).2

Gender composition of the occupation Occupation is given by a detailed three-digit code (according to the Nordic Occupational Classification, NYK).3 307 different occupational codes are represented in the data. The measure of gender composition is the proportion of women in each of these 307 categories.

Gender composition of the workplace In the public registers each establishment or workplace has a specific identification number. The gender composition variable is the proportion of women within each workplace. The total number of workplaces is 8695. The sample of employees per workplace ranges from 6 to 563 with a median of 10. Level of education is measured in years beyond compulsory school. The highest level (Ph.D.) is 12 yr. Annual earnings (i.e., personal income from gainful employment) is measured in units of the local currency (NOK; NOK 1Eh0.12). In the analyses, a log transformation of this variable is used. Part time work is measured by two dummy variables, one for 4–19 h per week and one for 20–29 h. Since the data do not contain more detailed information, working hours of 30 h or more per week are considered as full time jobs. Gender is coded 0 for men and 1 for women. Age is measured in years. Four variables are included to represent the effects of responsibility for children. Two dummy variables represent the presence of children below 7 and 7–16 yr in the household. Since a very high percentage of pregnant women have absence spells in connection with their pregnancies, I also include a dummy variable for having a child during the year of observation. A similar dummy variable for giving birth to a child in the preceding year is also included, since these women will generally have maternity leaves during part of the period of observation, which reduces their sickness absence. I control for time under observation measured in years. For most sample members, the observation period is slightly in excess of 0.75 yr (9 months). People who change jobs during the year will be under observation 2 A dichotomous measure was chosen since it simplifies the estimation of conditional regression models (see below). Dichotomisation has minimal impact on the results. I shall return to this in the discussion section. 3 Some information on how the NYK is related to the international occupational classification ISCO-88 can be found at http://www.cf.ac.uk/socsi/CAMSIS/occunits/nyktois88v1.sps (accessed at July 15, 2004).

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Table 1 Variable means by gender Men

Women

Sickness absence (1 ¼ yes; 0 ¼ no) Time under observation (years) Age (years) Child born year before Child born year of observation Children below 7 yr Children 7–16 yr Education (years beyond primary) Income (NOK) Short part-time (1 ¼ yes; 0 ¼ no) Long part-time (1 ¼ yes; 0 ¼ no)

0.17 0.77 37.76 —a —a 0.18 0.21 2.39 198,030 0.03 0.02

0.26 0.76 38.80 0.03 0.04 0.21 0.27 2.10 118,537 0.27 0.22

Proportion female in establishment: Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 and 6 (reference category) Decile 7 Decile 8 Decile 9 Decile 10

0.33 0.24 0.12 0.08 0.11 0.04 0.03 0.03 0.01

0.02 0.04 0.04 0.05 0.11 0.08 0.11 0.18 0.38

Proportion female in occupaitonal category: Decile 1 0.55 Decile 2 0.14 Decile 3 0.11 Decile 4 0.05 Decile 5 and 6 (reference category) 0.05 Decile 7 0.03 Decile 8 0.03 Decile 9 0.01 Decile 10 0.03

0.02 0.03 0.04 0.02 0.06 0.06 0.10 0.05 0.62

Number of observations

77,177

a

79,795

Variable not included for men.

for a shorter period, since I limit the analyses to one employment relationship per employee. Variable means are given in Table 1. Statistical methods Since the dependent variable is dichotomous, logistic regression is used. An important objective of the present paper is to distinguish the effects of the gender composition of the workplace from that of the occupation, and vice versa. This could be obtained by including both these measures in the same regression equation. However, it is also desirable to control for the individual’s work environment more generally. One way to do this would be to include direct measures of various dimensions of the work environment. Such measures are not available in the present data.

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Another possibility is to control for detailed occupational categories, thus, in effect estimating the effects of the workplace gender composition within relatively homogeneous occupational categories, and that is the solution implemented here. In linear regression, this would be obtained by including a dummy variable for each single occupational category. This is problematic in logistic regression. The same degree of control can, however, be obtained by using the so-called conditional or fixed-effects logistic regression (Chamberlain, 1980). The sample has a two-level structure with employees clustered within workplaces. In the ordinary logistic regressions, robust standard errors adjusted for clustering are computed. A similar adjustment procedure is not available for the conditional regression estimates. The standard errors in these analyses are therefore likely to be slightly underestimated.4 The general strategy is to start with a model in which only one of the gender composition variables (either workplace or occupational category) is included, controlling for time under observation, age, children, and gender, and using ordinary logistic regression. I then estimate the conditional logistic regression model including also other variables that may measure (or proxy) various job characteristics (education, earnings, working hours). In the regression analyses, the gender composition variables are represented by dummy variables, with 40–60% women as the reference category. The remaining dummy variables represent deciles (0–10% women, 10–20%, and so on). It turns out that the effects of the gender composition of the workplace are quite well approximated by a model assuming that these effects are linear. Some results from this linear effects model are therefore also presented. In all models, interaction terms of the individual’s gender with the gender composition of the workplace or the occupation are included to allow gender composition effects to differ for men and women. Similar interaction terms with gender are included for variables representing responsibility for children. Preliminary analyses revealed that the effects of the continuous independent variables were in most cases non-linear. Second-order terms are included in order to take this into account. Wald tests are used to test simultaneous hypotheses about several coefficients (e.g., the hypothesis that all gender composition dummies are equal to zero). 4 The presence of a two-level structure suggests that multilevel modelling might be advantageous (Guo & Zhao, 2000). The conditional regression model is used here since it provides more effective control for occupational category and for workplace.

Results The gender composition of the workplace Table 2 provides results from the logistic regressions of sickness absence on the gender composition of the workplace. As regards the ordinary logistic regression model, the Wald tests show that both the effect of the gender composition and of the interaction of the gender composition with gender are strongly significant. Fig. 1 presents estimated sickness absence probabilities for men and women based on this model. (For calculating the probabilities, mean age, no children, and time of observation one year was assumed.) Fig. 1 shows that women’s sickness absence tends to increase somewhat with their representation in the workplace; the absence probability is 19% higher in the most female-dominated than in the most maledominated category. For men, the trend is similar, but stronger; the absence probability increases by about 26% from the most female-dominated to the most maledominated category. Thus, both men and women have the highest sickness absence probability in workplaces dominated by their own gender. There is one exception from these overall trends; men also have a relatively high sickness absence probability in the most female-dominated category (more than 90% women). Except for this most female-dominated category, Fig. 1 shows that the relationship between the gender composition of the workplace and the absence probability is approximately linear. I therefore also estimated a model assuming linear effects. Probabilities based on this model are also shown in Fig. 1, and results of the Wald test are included in Table 2 (detailed results available upon request). The Wald tests of the linear model confirm the results for the model with dummy variables: strongly significant relationships between gender composition and sickness absence for both men and women. The implication of these relationships between gender composition and sickness absence is that there is virtually no difference in sickness absence between men and women in very male-dominated workplaces. In female-dominated workplaces, on the other hand, there is a very large difference, apart from the 90–100% female category, where the difference is again somewhat smaller. Turning now to the conditional regression model that controls for differences between occupational categories, the relationship between gender composition and sickness absence remains virtually unchanged for women (see Fig. 2).5 The probability of sickness absence 5 In the calculation of probabilities, average education and earnings and full time job were assumed (in addition to the assumptions mentioned in connection with Fig. 1).

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Table 2 Ordinary and conditional logistic regression of sickness absence on proportion women in the workplace and control variables Ordinary

Intercept Prop. female in workplace, decile 1 Prop. female in workplace, decile 2 Prop. female in workplace, decile 3 Prop. female in workplace, decile 4 Prop. female in workplace, decile 7 Prop. female in workplace, decile 8 Prop. female in workplace, decile 9 Prop. female in workplace, decile 10 Gender* prop. female in workplace, decile 1 Gender* prop. female in workplace, decile 2 Gender* prop. female in workplace, decile 3 Gender* prop. female in workplace, decile 4 Gender* prop. female in workplace, decile 7 Gender* prop. female in workplace, decile 8 Gender* prop. female in workplace, decile 9 Gender* prop. female in workplace, decile 10 Time under observation Gender Age Age squared Child born year before Child born year of observation Children below 7 yr Children 7–16 yr Children below 7 yr* woman Children 7–16 yr* woman Level of education Level of education squared Below 20 h per week 20–30 h per week Ln(earnings) Ln(earnings) squared

Conditional (within detailed occupation)

b

s.e.

1.5130*** 0.4553*** 0.2813*** 0.1046 0.0709 0.0606 0.0609 0.1884* 0.1474 0.6920*** 0.4545*** 0.2322** 0.2116** 0.0908 0.0255 0.1366 0.1415

0.0521 0.0562 0.0598 0.0707 0.0769 0.0813 0.0997 0.0832 0.0923 0.0833 0.0668 0.0802 0.0657 0.0752 0.0827 0.0772 0.0904

1.3445*** 0.6202*** 0.2052*** 0.0096 0.2710*** 2.1737*** 0.1710*** 0.2604*** 0.2705*** 0.2182***

0.0540 0.0410 0.0081 0.0057 0.0566 0.0474 0.0289 0.0263 0.0400 0.0332

Wald, prop. female effect for men (8 d.f.) Wald, prop. female effect for women (8 d.f.) Wald, equal prop. female effect for men and women(8 d.f.) Wald, prop. female effect for men (linear, 1 d.f.) Wald, prop. female effect for women (linear, 1 d.f.) Wald, equal prop. female effect for men and women (linear, 1 d.f.) Log likelihood N Note: Significance probabilities are given as follows:

po0:001;

increases by 18% from workplaces with more than 90% men to workplaces with more than 90% women. For men, however, the relationship between gender



s.e.

0.0990* 0.0157 0.0159 0.0877 0.0441 0.0974 0.0050 0.1713 0.2757** 0.1199 0.0587 0.1342 0.1205 0.0891 0.0418 0.1225

0.0412 0.0415 0.0456 0.0495 0.0647 0.0697 0.0771 0.0971 0.0890 0.0647 0.0679 0.0700 0.0767 0.0784 0.0829 0.1007

0.9994*** 0.6185*** 0.1649*** 0.0188*** 0.2347*** 2.1872*** 0.0004 0.1101*** 0.1996*** 0.0495 1.2266*** 0.7222*** 0.2743*** 0.1979*** 0.1551*** 0.4816***

0.0511 0.0447 0.0077 0.0053 0.0585 0.0460 0.0283 0.0263 0.0391 0.0331 0.0528 0.1510 0.0249 0.0229 0.0272 0.0179

177.2*** 27.52*** 115.22***

21.90** 22.00** 15.07

139.95***

1.02

13.61***

12.21***

128.60***

11.29**

76870.519 156972 

b

70999.689 156376

po0:01;  po0:05:

composition and sickness absence is now considerably weaker and there is no longer any significant linear trend.

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2268 0.35 0.3

Women

0.25

Women, linear

0.2 0.15

Men

0.1

Men, linear

0.05 0 1

2

3 4 5 7 9 6 8 Proportion of women (deciles)

10

Fig. 1. Men’s and women’s probability of sickness absence as a function of the proportion of women in the workplace. Ordinary logistic regression model.

0.35

high sickness absence in gender-balanced occupations (as seen in the ordinary regression model) seems to be explained by workplace characteristics; in other words, gender-balanced occupations tend to be over represented in workplaces with high absence levels, and this is why these occupations have high absence probabilities when between workplace variation is not controlled. Although still clearly significant, the overall impression now is that there is relatively little variation in sickness absence along the male—female-dominated dimension. A slight U-shaped pattern may nevertheless be discerned at least for men. The Wald test for interaction of gender with the gender composition of the occupational category is not significantly different from zero. This implies that the difference in the probability of sickness absence between men and women does not seem to depend on the gender composition of the occupation.

0.3

Effects of other variables

0.25

Women

0.2

Women, linear

0.15

Men Men, linear

0.1 0.05 0 1

2

3 4 5 6 7 8 9 Proportion of women (deciles)

10

Fig. 2. Men’s and women’s probability of sickness absence as a function of the proportion of women in the workplace. Conditional logistic regression model.

The gender composition of the occupational category Results on the relationship between occupational gender segregation and sickness absence are presented in Table 3. In the ordinary logistic regression model the association between these variables is strongly significant. The interaction effect is also significantly different from zero, indicating that the association is different for men and women. Contrary to what was found in the analyses of the gender composition of the workplace, however, the relationship between occupational gender segregation and sickness absence deviates strongly from linearity and is in general very irregular (Fig. 3). In the conditional logistic regression model, the coefficients are now estimated based on comparisons between individuals working in the same workplace only. The elimination of variation between workplaces leads to a much less ‘‘noisy’’ picture of the relationship between the gender composition of the occupation and sickness absence (Fig. 4). In particular, the relatively

Even after detailed control for workplace and occupation, there is a large gender difference in sickness absence with women’s sickness absence probability being 30–70% higher than men’s. This is consistent with previous research showing that the gender difference in sickness absence remains even when comparing people who are both in the same workplace and in the same type of job (Mastekaasa & Olsen, 1998). Apart from absence in connection with pregnancy, children have little effect on women’s and men’s sickness absence. To the extent that children seem to be associated with higher sickness absence for women at all, the relationship more or less disappears with control for detailed occupational categories (conditional model in Table 1). Part time jobs reduce the probability of sickness absence. The probability of sickness absence increases with age and declines with level of education. The relationship with earnings follows an inverted U-shape with a low probability of sickness absence at both very low and very high earnings, a pattern which is consistent with previous Norwegian studies (Bratberg, Dahl, & Risa, 2002); at least in part it could reflect that the effect of short working hours (leading to both low earnings and low absence) is not completely picked up by the two working hours dummies. It could also be influenced by errors in the time under observation variable, since employers will not always report in time that an employment relationship is terminated.

Discussion The overall impression provided by the analyses above is that the probability of sickness absence is only

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Table 3 Ordinary and conditional logistic regression of sickness absence on proportion women in the occupational category and control variables Ordinary

Conditional (within workplace)

b Intercept Prop. female in occupation, decile 1 Prop. female in occupation, decile 2 Prop. female in occupation, decile 3 Prop. female in occupation, decile 4 Prop. female in occupation, decile 7 Prop. female in occupation, decile 8 Prop. female in occupation, decile 9 Prop. female in occupation, decile 10 Gender* prop. female in occupation, decile 1 Gender* prop. female in occupation, decile 2 Gender* prop. female in occupation, decile 3 Gender* prop. female in occupation, decile 4 Gender* prop. female in occupation, decile 7 Gender* prop. female in occupation, decile 8 Gender* prop. female in occupation, decile 9 Gender* prop. female in occupation, decile 10 Time under observation Gender Age Age squared Child born year berfore Child born year of observation Children below 7 yr Children 7–16 yr Children below 7 yr* woman Children 7–16 yr* woman Level of education Level of education squared Below 20 h per week 20–30 h per week Ln(income) Ln(income) squared

s.e.

1.2599*** 0.0827 0.0803 0.2328** 0.4850*** 0.5857*** 0.3130*** 0.0762 0.0710 0.2290** 0.0040 0.1466 0.0540 0.0464 0.1449 0.1791 0.2093*

0.0659 0.0670 0.0737 0.0874 0.0866 0.0942 0.0886 0.1227 0.0883 0.0869 0.0817 0.0979 0.1028 0.0987 0.0898 0.1234 0.0864

1.3515*** 0.6024*** 0.2055*** 0.0097 0.2735*** 2.1771*** 0.1666*** 0.2583*** 0.2735*** 0.2216***

0.0538 0.0618 0.0081 0.0057 0.0566 0.0474 0.0292 0.0264 0.0399 0.0333

Wald, prop. female effect for men (7 d.f.) Wald, prop. female effect for women (7 d.f.) Wald, equal prop. female effect for men and women (7 d.f.) Log likelihood N Note: Significance probabilities are given as follows:

189.16*** 148.53*** 20.22** 76824.000 156972 

po0:001;

weakly related to the gender composition of both the workplace and the occupational category. After control for occupational differences there is a weak positive relationship between the proportion of women in the workplace and women’s sickness absence. For men there is no clear relationship between the gender composition of the workplace and sickness absence. These findings imply that there is no support for the type of gender composition effects suggested by



b

s.e.

0.2856*** 0.3001*** 0.1354* 0.0425 0.1082 0.0654 0.1115 0.1698* 0.0799 0.0262 0.1184 0.0534 0.0036 0.0761 0.0893 0.1442

0.0533 0.0584 0.0628 0.0748 0.0878 0.0804 0.1199 0.0767 0.0845 0.0835 0.0840 0.1010 0.1003 0.0913 0.1281 0.0832

1.0788*** 0.5635*** 0.1666*** 0.0220*** 0.2650*** 2.2262*** 0.0429 0.1131*** 0.1211** 0.0448 1.4000*** 0.7082*** 0.2397*** 0.1668*** 0.4002*** 0.5899***

0.0560 0.0597 0.0080 0.0055 0.0596 0.0475 0.0291 0.0270 0.0399 0.0339 0.0482 0.1351 0.0257 0.0238 0.0292 0.0190 75.04*** 47.66*** 6.83 62650.578 147861

po0:01;  po0:05:

Kanter’s and Blau’s theories. There is no tendency for sickness absence to be particularly high in workplaces numerically dominated by the opposite gender. The results are more consistent with the absence culture hypothesis. Women’s sickness absence increases with the proportion of women in the workplace but the relationship is weak. Although consistent with the absence culture argument, the positive relationship between the proportion

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2270 0.4 0.35 0.3 0.25 0.2 0.15 0.1

Women Men

0.05 0 1

2

3 4 5 6 7 8 Proportion of women (deciles)

9

10

Fig. 3. Men’s and women’s probability of sickness absence as a function of the proportion of women in the occupation. Ordinary logistic regression model.

0.4 0.35 0.3 0.25 0.2 0.15 0.1

Women

0.05

Men

0 1

2

3 4 5 6 7 8 Proportion of women (deciles)

9

10

Fig. 4. Men’s and women’s probability of sickness absence as a function of the proportion of women in the occupation. Conditional logistic regression model.

of women in the workplace and women’s sickness absence could also have other causes. The association between these variables would also be consistent with (a generalisation of) Blalock’s theory of intergroup conflict, but as argued above, I find the application of this theory to the relationship between men and women in the workplace somewhat far-fetched. In addition to the arguments mentioned, this theory is primarily concerned with one group’s motivation to discriminate against another, and it is this motivation that is assumed to increase as the relative size of the subordinate group increases. The link between the dominant group’s motivation to discriminate and the actual stresses and strains experienced by the subordinate group is not, however, obvious. In the first place, there is not necessarily a one-to-one relationship between dominant group members’ motivation to discriminate and their actual behaviour (Blalock, 1967, p. 176ff.). Second, not only the level of discrimination but also the ability of the

subordinate group to withstand discrimination is likely to be affected by this group’s relative size—it is hard to believe that the subordinate group is equally vulnerable to discriminatory behaviour when it makes up 90% of the workplace as when it makes up only 10%. Another possible explanation of women’s higher absence probability in female-dominated workplaces is that women have poorer working conditions in such workplaces. This possibility cannot be ruled out, but it should be noted that we control both for detailed occupational category, working hours, and earnings; thus, there would have to be differences in women’s working conditions in different workplaces beyond what is reflected in these variables. With control for between-workplace variation in sickness absence, there is a weak U-shaped relationship between the gender composition of the occupation and the probability of sickness absence, particularly for men. This is to some extent in line with previous studies. In particular, two Swedish studies have found a Ushaped relationship between gender composition and sickness absence (Alexanderson et al., 1994; Hensing et al., 1995). Even a previous Norwegian study found some evidence for such a pattern (Mastekaasa, 1993). A U-shaped pattern is also found in the present analyses, but the relationship is much weaker than in the previous studies. A possible explanation for this difference in the strength of the relationship between the gender composition of the occupation and sickness absence is that it may be different for absences of different duration. The Swedish studies and the earlier Norwegian study include absence spells exceeding 7 days. The data used in this paper includes only spells exceeding 2 weeks. In previous research, the gender composition of the occupation has sometimes been used as a proxy for the gender composition of the workplace, or empirical results have at least been interpreted as if the gender composition of the occupation reflects the conditions at the individual’s workplace (e.g., Hunt & Emslie, 1998; Wharton & Baron, 1987, 1991). The present results indicate that this may be highly problematic. In general, the results are very different, depending on whether gender composition is measured within the workplace or within occupational categories. For example, women’s sickness absence is lowest in male-dominated workplaces, but relatively high in male-dominated occupations. The findings also indicate that it is highly problematic to estimate gender composition effects without appropriate control for other variations in sickness absence between occupations and workplaces. Without detailed control for occupational categories, the relationship between the gender composition of the workplace and men’s sickness absence is grossly overestimated, and without control for variation between workplaces an

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impression of large albeit irregular occupational differences is created. In the discussion, so far it has been assumed that the gender composition in the workplace is a better measure of an individual’s actual work setting than is the gender composition of the occupational category. Although this assumption seems reasonable, it is not entirely nonproblematic. Particularly, in large workplaces most of the actual interaction may take place in smaller subunits or workgroups. This introduces some amount of presumably random error, which is likely to attenuate the estimated effects of the gender composition. Another potential methodological problem is that the gender composition of each workplace is estimated from a 10% sample. In the analyses, workplaces with 5 or less sampled employees have been excluded. It may be argued that a sample of 5 employees as used here is too small to estimate the gender composition with acceptable accuracy. I therefore also tried 10 and 15 sampled employees as cut-off points. The results were almost identical (results available upon request). I have used a simple dichotomous absence measure of sickness absence. To assess the robustness of the results with regard to this choice, I also estimated ordinal regression models using a four-category frequency measure (0, 1, 2, and 3 or more absence spells) and a five-category working days lost measure (0, 1–20, 21–40, 41–80, and 81 or more days). The coefficients were extremely similar; the average absolute difference for the gender composition dummies compared to the ordinary regression models in Tables 2 and 3 was 0.02, and the maximum deviation was 0.07. (I am not aware of methods for estimating conditional ordinal models, so this exercise was carried out only with ordinary logistic regression.) The interpretations discussed so far assume that characteristics of the workplace or of the occupation have a causal impact on sickness absence. The relationship between these variables, however, may also to some extent be the result of selection effects. In particular, it has been suggested that relatively high sickness absence (Hensing et al., 1995) and risk of myocardial infarctation (O¨stlin, Alfredsson, Hammar, & Reuterwall, 1998) among men in strongly female-dominated occupations may be due to such effects, in that men vulnerable to disease are selected into these occupations. Selection effects could, however, also work in the opposite direction to the extent that people with health problems have difficulties in finding alternative jobs and therefore are stuck in unsatisfactory conditions.

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dominated occupations. The present analyses also provide some indications of such a U-shaped relationship, but it is very weak, particularly for women. A possible explanation for this difference in findings is that the present study includes only relatively long absence spells, but this needs to be investigated further in future research. The analyses provide no support for the idea that being in a small minority in the workplace increases the probability of sickness absence. On the contrary, women’s sickness absence increases monotonically as the proportion of women in the workplace increases. This is consistent with the idea that female-dominated groups tend to develop more lenient norms and standards with regard to sickness absence than are found in more male-dominated contexts, but other explanations cannot be ruled out. It should also be underscored that the relationship between workplace gender composition and the probability of sickness absence is weak. The analyses presented here represent several improvements in relation to previous research. The most important strength of this study is probably that the distribution of men and women across both workplaces and occupations has been taken into account simultaneously. As we have seen, workplace and occupational gender composition relate to sickness absence in very different ways. A weakness of the present study is that I have data only for a sample of individuals in each workplace. This reduces the reliability of the measures of gender composition, and also leads to the exclusion of small workplaces. Future research will also benefit from more information on the organisational structure of workplaces, so that more precise measures of the gender composition of each individual’s job environment may be developed. For a more definitive test of the gendered absence culture idea, more direct measurement of absence norms will also be essential.

Acknowledgements A previous version of this paper was presented at the 11th Annual Meeting of the European Public Health Association, Rome, November 20–22, 2003. I am grateful to other participants for helpful comments. The paper has also benefited from very useful comments by SS&M’s anonymous reviewers.

References Conclusion Several previous studies have found sickness absence to be highest in very male-dominated and very female-

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