Gender, Time Use and Health - Bird y Fremont (1991)

Gender, Time Use, and HealthAuthor(s): Chloe E. Bird and Allen M. FremontSource: Journal of Health and Social Behavior, Vol. 32, No. 2 (Jun., 1991), pp. 114-129Published by: American Sociological AssociationStable URL: http://www.jstor.org/stable/2137147 .

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Gender, Time Use, and Health*

CHLOE E. BIRD ALLEN M. FREMONT University of Illinois

Journal of Health and Social Behavior 1991, Vol. 32 (June): 114-129

One of the continuing paradoxes facing social epidemiologists concerns sex differences in morbidity and mortality. Although women live longer than men, they apparently get sick more. We hypothesize that women's higher morbidity levels result from less paid work and lower wages combined with more hours spent in household labor, child care, and helping others, and fewer hours of leisure and sleep. Men and women hold different social roles; men hold most of the highly rewarding roles. We operationalize social roles as time commitments to various role-related activities. This approach provides interval-level measures such as time spent in caring for children instead of simple dichotomies such as parent! nonparent. We find that when gender differences in social roles are controlled, being male is associated with poorer health than being female. We conclude that if gender roles were more equal, women would experience better health than men, more consistent with their greater longevity.

Although much time and energy have been devoted to measuring sex differences in morbidity and mortality, until recently far less effort has been applied to explaining why these differences exist. Health statistics show that women have more illness and disabilities than men, including acute conditions such as respiratory infections and chronic conditions such as arthritis (National Center for Health Statistics 1983). In contrast, men have higher

* We are indebted to John Mirowsky and Catherine Ross for their advice and encourage- ment. We thank Lowell Hargens, Beth Anne Shelton, and Gray Swicegood for assistance in various phases of the work. We would also like to thank Alan Peshkin, Barbara Reskin, Gillian Stevens, and anonymous reviewers for helpful comments on earlier drafts.

Direct all correspondence to Chloe E. Bird, Department of Sociology, University of Illinois, 326 Lincoln Hall, 702 S. Wright St., Urbana, IL 61801. The data used in this article were collected by Thomas Juster et al. (1983), and were made available by the Inter-University Consortium for Political and Social Research.

An earlier version of this paper was presented at the 1989 annual meetings of the American Sociological Association. The authors are listed in alphabetical order.

rates of life-threatening diseases such as heart disease, which cause more permanent disabil- ity and earlier death (Verbrugge 1985; Wingard 1982). These differences persist even after illness related to reproductive physiology is excluded; moreover, they are not explained by differences in tendencies to visit physicians or by reporting bias (Cleary, Mechanic, and Greenly 1982; Gove 1984; Verbrugge 1985).

Sex differences in morbidity are consistent with a stress-illness model. Women have higher rates of psychological distress including anxiety, depression, worry, and demoral- ization (Gove and Tudor 1973; Kessler and McRae 1981; Mirowsky and Ross 1989). Social scientists find consistently that sex differences in psychological distress are caused by role stress, role conflict, and the degree of commitment to gender roles (Gove 1984). For example, women typically bear major responsibility for housework and child care even when they are employed (Ross, Mirowsky, and Huber 1983). Though the biochemical mechanisms are not well understood, a large body of evidence suggests that psychological distress and depression can lead to physical illness. For instance, research has shown that psychological distress and depres-

114



GENDER, TIME USE, AND HEALTH 115

sion suppress the immune system, making an individual more susceptible to disease (Jem- mott and Locke 1984; Lazarus and Folkman 1984; Solomon 1985).

Although the literature on psychological distress has developed more refined theories about the causal impact of social roles than has the physical health literature, crude and indirect measures of social roles hamper research in both areas. For example, the role of parent typically is measured as parental status (i.e., whether an individual has children). Similarly, measures of child care typically are based on questions such as "Who does most of the child care, you or your spouse?" Although such questions may yield reliable measures of whether the respondent is a parent, or which spouse is primarily responsible for child care, they, give little information on how much time and effort is actually spent in taking care of the children. In addition, such data are not comparable across individuals because different individuals or couples may devote different amounts of time to child care and housework, depending in part on the age and number of children.

We employ time-use measures as an alternative operationalization of social roles. For example, the extent to which an individual fulfills the role of "housewife" is operationalized as the amount of time that individual spends each week in activities related to the role (e.g., cleaning, doing laundry, cooking). In contrast to data typically used, data from time-use studies offer more precise measures and thus provide clearer information on how roles influence health and allow greater comparison of social roles across individuals. -

Our general hypothesis is that time spent in social roles explains the effects of gender on health (see Figure 1). We expect that women spend more time in housework, child care, and helping others, and less time in paid work, leisure, and sleep. In turn, we expect that spending more time in housework, child care, and helping others, and less time in sleep, worsens health, whereas spending more time in paid work and leisure improves or maintains health. In addition, we expect that men will have higher wages than women and that higher wages will be associated with better health.

PREVIOUS RESEARCH

How do social roles affect physical well-

FIGURE 1. Hypothesized Model of Gender, Time Use in Various Activities, Wages, and Health

Wages

+ Paid Work +

Housework

/// _ Child Care -

Male Health Helping Others

Active Leisure

Passive Leisure +

Sleep

being? Gove and Hughes (1979) argued that there are theoretical and empirical grounds for assuming that certain social roles are related to poor mental health, which in turn is linked to mild physical illness-the primary type of morbidity experienced by women. The authors reasoned that women typically have more role obligations which require con- stantly caring for others, such as children or spouses. These nurturant role obligations can interfere with women's ability to care for themselves properly and can affect their health negatively. When they controlled for nurturant role activities as well as for marital status, living arrangements, and psychiatric symptoms, Gove and Hughes found that health differences between men and women disappeared. In a related study, Kessler and McLeod (1984) found that women's tendency to be more emotionally involved in the lives of those around them made them more vulnerable than men to negative life events in their social network; other researchers have linked negative life events to physical illness (Holmes and Rahe 1967).

Verbrugge (1989) analyzed sex differences in morbidity, controlling for an unusually wide variety of variables in addition to social roles. She found that stress, unhappiness, and low levels of employment were associated with poorer health, whereas participation in productive and personally fulfilling roles was associated with better health. Controlling for these and other social factors caused sex differences in health to narrow and often to vanish statistically. In fact, sex differences on a number of health measures (six out of 67) were reversed, albeit nonsignificantly. Ver- brugge concluded that these reversals indicate an underlying health disadvantage for men.



116 JOURNAL OF HEALTH AND SOCIAL BEHAVIOR

More generally, research on sex differences in physical health has focused on the impact of three major social roles: marriage, employment, and parenthood. Most research has found that married men and women have lower morbidity and mortality rates than unmarried persons, though men tend to benefit more from marriage than women (Sorensen and Verbrugge 1987; Verbrugge and Madans 1985). Some research, however, has found that divorced women are healthier than unhappily married women (Wingard 1982). Like marriage, employment tends to yield health benefits for both men and women (Verbrugge 1985). Research on the impact of parenthood on physical health has produced inconsistent findings (Wingard 1982), though in a review McLanahan and Adams (1987) conclude that children have a small negative impact on psychological well-being.

Research based on time-use data shows that women still do the vast majority of household tasks and child care, regardless of their educational level or employment status (Shel- ton 1989; Shelton and Coverman 1988). Hill and Stafford (1980) found that college- educated mothers who worked more than 20 hours a week spent less time in child-related activities; the reductions were surprisingly small, however. Further analysis by the authors revealed that a working mother's ability to work and to sustain time spent in child care was "financed" by reductions in her personal care time (including sleep) and in passive leisure, such as watching television. This observation is consistent with Berk and Berk's (1979) finding that women taking care of children have significantly fewer "pleasant minutes" than either their husbands or married women without children. Although Hill and Stafford did not examine the relationship between time use and health outcomes, they speculated that working mothers' reallocations of their time are achieved at the expense of their health and well-being.

METHOD

Data

We use data from the 1981 Study of Time Use, collected by the Institute for Social Research (Juster et al. 1983). The time-use data set consists of data from 620 respondents

and contains detailed information on how respondents spent their time in the home and the workplace, demographic information, and respondents' reports of their health.

The 1981 study is a follow-up of a 1976 study. In the original study, data were collected from a national probability sample of adults living in the contiguous United States (N= 1,519). Both studies used a panel design with four waves of data, collected in 1976 and again in 1981. The 1981 sample includes only respondents who were at least 18 years old at the time of the original study. In addition, only those respondents from whom at least three waves of data had been collected in 1976 were eligible for inclusion in the 1981 follow-up. From this group of 920 respondents, 620 persons were contacted and interviewed.

Attrition between the original and the follow-up study resulted in some dispropor- tionate loss of low-income and less-educated respondents. Even so, comparisons between the original and the 1981 sample on important demographic characteristics (e.g., mean educational level) revealed no large deviations (Shelton and Firestone 1988). Although the 1981 data may have lost some generalizability in comparison to the 1976 data, this loss is offset by a large and highly significant improvement in the quality of the 1981 data. Juster and Stafford (1985) report that changes in data collection techniques improved the quality of the 1981 data 20 percent over that of the 1976 data.'

As mentioned above, data were collected in four waves in the 1981 study. Each of the four waves of interviews was conducted during a different season; two of the interviews were conducted on weekdays, the other two on weekends. This approach greatly increased the validity of the time-use data by decreasing the possibility that the data reflected an atypical day. Although the use of four waves caused attrition, Juster and Stafford (1985) found that respondents who remained in the panel for all four waves produced higher- quality diaries on the initial wave than respondents who appeared in Wave 1 but subsequently dropped out. In addition, the quality of diaries for later waves was higher than for earlier waves when the authors standardized for attrition.

Time use was assessed by time diaries. Re- spondents were asked to recount in as much detail as possible how they had spent their




time during the previous 24-hour period. They were asked where they were, whom they were with, what they were doing, and whether they were doing more than one thing at a time. Robinson (1985) concludes that 24-hour recall diaries were as reliable as other diary-type meth- ods such as electronic "beeper" studies, and were clearly superior to respondents' general estimates of how much time they spent in various activities. (For further discussion of the validity and reliability of time diaries see Juster 1985, pp. 63-88 and Robinson 1985, pp. 33- 59.)

Juster et al. (1983) combined and weighted time-use data collected during the four waves to compute a weekly average or synthetic week.2 The synthetic week offers a strong measure of how time is actually used over a whole week rather than on a given day (Robinson 1980). We use only respondents for whom a synthetic week could be con- structed (i.e., they completed at least three waves of interviews) and for whom there are no missing data on variables of interest. The 1981 data are limited to Whites only because of the small number of Blacks who remained in the study. Our final sample size is 469, with 186 men and 283 women (see appendix for a table comparing characteristics of respondents used with those of respondents excluded from the analysis).3

Measures

Dependent variable. The dependent variable is self-rated health as assessed by the question, "Compared to other people your age, would you say that your health is (1) poor, (2) fair, (3) good, or (4) excellent?" This measure of general health is both reliable and repro- ducible, and is correlated strongly with more "objective" measures such as physicians' assessments (Maddox and Douglass 1973; Mos- sey and Shapiro 1982; Okun and George 1984). We use a general health measure because we focus on respondents' overall health rather than on specific diseases.4 Research on stress and illness suggests that stress can increase an individual's susceptibility to disease. This increased susceptibility, however, does not necessarily produce specific patterns of disease across individuals (Jemmot and Locke 1984; Selye 1985). Consequently, specific indices of health or measures of particular diseases may be less valid than general measures. Further-

more, using such specific indices to examine causes of overall sex differences in morbidity may be confounded by differences between pathophysiological processes in men and in women (Waldron 1983).5

Although there are disadvantages inherent in using self-reports of health, they are probably no greater than those associated with using "objective" measures of health such as physicians' assessments. Indeed, for general measures of health, self ratings may be as valid as ratings by physicians. The assump- tion that physician-based measures are the standard against which self-reports should be evaluated is not well supported. Not only do clinical measures vary widely -in their speci- ficity, sensitivity, and ability to predict future health status, but research has demonstrated many biases in the way in which physicians assess and treat patients of different ages, sexes, incomes, physical appearances, and ethnic backgrounds (Eisenberg 1979, 1986; Kaplan and Camacho 1983). Moreover, research has shown that self-rated health is a stronger predictor of mortality than are physicians' assessments; only age predicts morality more strongly (Mossey and Shapiro 1982). Self-rated health is a significant predictor of mortality even when health status measures are controlled. For instance, Idler, Kasl, and Lemke (1990) found that poor self-rated health was a strong predictor of mortality over a four-year period despite extensive controls for baseline health status. (Hereafter, unless a distinction is made, we use the terms "self-rated health" and "health" interchangeably.)

Independent variables. Independent variables used in the analysis include sociodemographic variables and indices of time spent in activities related to various roles. Sex and marital status are dummy variables. Sex is coded 1 for males; marital status is coded 1 for married persons with spouse present. Age and education are measured in years. "Chil- dren" is the number of children aged 17 or under in the household. Wages, including salary and bonuses, are measured in thou- sands of dollars per year.

All time-use variables are measured in hours per week; they include indices of time spent in paid work (i.e., employment), housework, child care, and caring for others, as well as time spent in active and passive leisure and in sleep. Table 1 lists the activities included in each time-use variable. We adopt




TABLE 1. Activities Included in Time-Use Variablesa

Paid workb Employment Housework Meal preparation; meal cleanup; miscellaneous chores; indoor cleaning; laundry; indoor repair

and maintenance (e.g., painting a room, plumbing); appliance and furniture repair Child care Baby care; child care; helping/teaching children (e.g., making cookies); helping with homework;

giving child orders/instructions; disciplining, reading to children; conversations with; indoor and outdoor playing with; giving or obtaining medical care for; babysitting (unpaid) for nonhousehold children; coordinating/facilitating child's social or instructional activities; miscellaneous activities related to child care (e.g., making phone calls about child); child-related travel

Helping others Medical care to adults in household (HH); nonmedical care to adults in HH (e.g., ran a bath for husband); help care to relatives not living in HH; help and care to neighbors and friends; help and care to others (unrelated); watching personal care activities of others; travel related to helping others

Passive leisure Radio; television; listening to music; reading; conversations (including phone conversations); conversation with household members writing letters; reading mail; relaxing; thinking and planning; sitting; miscellaneous passive leisure

Active leisure Team sports; racquet sports; golfing; swimming; skating; skiing; bowling; pool; ping-pong; pinball; frisbee; catch; exercises; yoga; hunting; fishing; boating; sailing; walking for pleasure; hiking; jogging; running; bicycling; horseback riding; dance; gymnastics; lessons in sports, gymnastics, or dance; pleasure drives; rides with family; picnicking; photography; working on or repairing leisure equipment; collections or scrapbooks; carpentry and woodworking as hobby; preserving foodstuffs; knitting; needlework; sewing; animal care (if not farmer); art; writing literature or diary; playing instrument; singing; acting; playing cards; board games; social games (e.g., scavenger hunt); going camping or to the beach; puzzles; lessons in music; crafts; miscellaneous travel related to active leisure

Sleep Night sleep a All time-use variables are measured as the number of hours per week spent by respondents in a given activity or

set of activities. With the exception of paid work, values for all variables listed here are synthetic week estimates derived from time diaries.

b Hours of paid work are assessed by the question, "About how many hours do you work on your job in an average week, including both paid and unpaid overtime?" Because some respondents work part-time, we expect this measure of employment to be more reliable than a synthetic-week estimate.

Juster et al.'s (1985) measures of active and passive leisure. Active leisure includes a wide variety of recreational activities, all of which require physical or mental exertion, such as team sports, swimming, horseback riding, picnicking, and board games. Passive leisure includes recreational activities that do not require physical or mental exertion, such as watching television, listening to records or tapes, reading newspapers, and talking with others.

RESULTS

Table 2 shows the correlations of all measured variables. Self-rated health is associated positively with being male (r = .07), marriage (r = .10), education (r = .31), number of children (r = .07), paid work (r = .26), and wages (r = .26), and negatively with age (r = - .19) and time spent in household labor (r = - .15). Self-rated health is not related significantly to hours spent in child care, active leisure, or helping others. Surprisingly, health is associated

negatively with passive leisure (r = -. 15) and time spent in sleeping (r = - .2 1). We suspect that these negative associations are due in part to the cumulative nature of time use. For example, the alternative to time spent in passive leisure or sleeping is not merely being awake, but pursuing some other activity that may have greater health benefits. The benefits of passive leisure and sleep may diminish at high levels, indicating a nonlinear relationship.

To what extent do conventional categorical measures of social roles explain sex differences in health? Figures 2 and 3 illustrate the effects of marriage and employment on men's and women's health without controlling for time use.6 Figure 2 shows the deviation from the overall mean health level for men and for women by marital status.7 Though marriage has positive effects for both sexes, men benefit more than women.

Figure 3 shows the deviation from the overall mean health level of men and for women who were employed full-time and for those who were not employed. Like marriage, employment has a positive effect on health for



z

TABLE 2. Correlations, All Measured Variables

Paid House- Child Passive Active Health Married Education Age Male Children Sleep Work work Care Leisure Wages Leisure > Health

=, i Married .099* Education .306*** .079* Age -.188*** -.330*** -.256*** Sex

(Male = 1) .069t .222*** .157*** -.051 Children .074* .349*** .023 -.534*** .029 Sleep - .209*** -.043 -.160*** .246*** -.102 -.112** Paid work .262*** .174*** .292*** -.531*** .378*** .182*** -.307*** Housework -. 153*** .005 -. 187*** .046 -.585*** .139*** .041 -.445*** Child care -.035 .167*** .002 -.395*** -.221*** .492*** -.039 -.122** .285*** Passive

leisure - .152*** - .158*** - .201*** .370*** .043 - .218*** .071 - .372*** - .024 - .134** Wages .259*** .158*** .356*** - .298*** .498*** .082* - .257*** .667*** - .446*** - .146*** - .265*** Active leisure -.039 .001 -.050 .100 -.008 -.063 .033 -.158*** .026 -.063 -.083* -.061 Helping others .028 -.026 -.140*** .138*** -.001 -.073 -.043 -.166*** .020 -.084* .026 -.120** -.030 tp<= .10; *p<= .05; **p<= .01; ***p <= .001; one-tailed tests.




FIGURE 2. Deviation from Overall Mean Health Level, by Marital Status and Sex

0.7

0.5 2 married not married

0.3

X 0.1

-0.1

-0.3

-0.5

-0.7 female male

Sex

FIGURE 3. Deviation from Overall Mean Health Level, by Employment Sta- tus and Sex

0.7 -

0.5 0 employed full-time not employed

0.3 -

- 0.1

-0.1

-0.3 -

-0.5-

-0.7 - female male

Sex

both sexes. Whereas marriage had relatively little positive impact on women, employed women and men report equally high levels of health. Women who were not employed had below-average health levels. Though men who were not employed appear to have the worst health of all groups, the men in our sample who were not employed are older than the women who were not employed and consequently may be sicker (x = 66.33 years old for men, x = 57.41 years old for women; p ' .000).

Can differences in social roles, as measured by time spent in various activities, explain the association among work, family, and men's and women's health? Table 3 shows that men spent nearly twice as many hours in the labor force as women and earned more than 3.5 times as much in annual wages. Women averaged 16.9 hours of household labor, compared to men's 4.4 hours. Among employed respondents who worked more than 30 hours a week, women averaged 12.3 hours of housework, whereas

men averaged only 3.1 hours. Although the males in the sample had slightly more children on average, women spent more than twice as many hours on child care (4.3 hours, compared to 1.7 for men). On average there was no significant difference in time spent in helping others, passive leisure, active leisure, or sleep.

Table 4 presents hierarchical regression analysis of self-rated health, in which variables were entered in steps. Step 1 includes sociodemographic characteristics. Step 2 adds in hours of paid work and wages. Step 3 adds in time spent in unpaid work, sleep, and leisure. Step 4 adds in sex interaction terms for active and passive leisure.

In Step 1, when we control for age, education, marital status, and children, the effect of gender on health becomes insignificant, although the coefficient remains positive. In addition, being older is associated with poorer health, whereas having more years of education is associated with better health. Education remains highly significant even when all other predictors are entered into the regression.

When we add in wages and hours of paid work in Step 2, the effect of being male becomes negative and significant; at the same level of paid work and wages, men report worse health than women, even when age is controlled. As hypothesized, both paid work and wages exert strong positive effects on self-rated health. Yet the independent effect of paid work on health becomes insignificant when time spent in other social roles is controlled.

In Step 3, we enter in the time-use variables. When time use is controlled, men have worse health than women. The negative effect of being male on health increases and becomes more significant. This increase shows that the positive effect of being male on health is due in part to gender differences in social roles. As anticipated, time spent in housework has a negative effect on health. This finding supports our hypothesis that female gender roles, particularly that of housewife, have a negative impact on health. In contrast to our hypothesis, however, time spent in child care is not significant. In addition, we found no sex difference in the amount of time spent in helping others, and found that such time is associated positively with health.

We employed parabolic terms for sleep and




TABLE 3. Means and Standard Deviations of Total Sample and by Sex

Total Population Female Male N = 469 N = 283 N = 186

Demographics Health 3.26 (.79) 3.21 (.78) 3.32 (.79) t

Excellent 43.9% 40.6% 48.9% Good 40.7% 42.8% 37.6% Fair 12.4% 13.8% 10.2% Poor 3.0% 2.8% 3.2%

Sex Female 60.0% Male 40.0%

Age 48.60 (16.03) 49.25 (16.60) 47.60 (15.10) 25 to 39 39.2% 38.9% 39.8% 40 to 59 31.8% 30.7% 33.3% 60t 29.0% 30.4% 26.9%

Education 12.73 (3.04) 12.35 (2.80) 15.10 (13.32)*** < 12 years 20.0% 20.8% 18.8% 12 to 15 years 56.9% 62.9% 47.8% 16+ years 23.0% 16.3% 33.3%

Married .68 (.47) .60 (.49) .81 (.39)*** Children 1.15 (1.34) 1.11 (1.30) 1.19 (1.39)

0 47.1% 47.3% 46.8% 1 + 52.9% 52.7% 53.2%

Wages 11.78 (14.90) 5.77 (7.27) 20.94 (18.49)*** Time Use

Paid work 25.18 (22.12) 18.41 (19.61) 35.47 (21.79)*** Not employed 37.3% 47.0% 22.6% 1-20 hours 6.8% 9.9% 2.2% 21-39 hours 11.9% 14.5% 8.1% 40+ hours 43.9% 28.6% 67.2%

Housework 11.62 (10.34) 16.91 (10.11) 4.36 (5.22)*** Child care 3.30 (5.74) 4.33 (6.73) 1.74 (3.18)*** Helping others 1.88 (4.10) 1.88 (3.93) 1.88 (4.36) Passive leisure 22.31 (13.58) 21.85 (12.90) 23.02 (14.55) Active leisure 5.20 (6.82) 5.24 (7.07) 5.13 (6.44)

Mean comparison of females to males. tp <= .10; * p <= .05; ** p <= .01; *** p <= .001; one-tailed tests.

for passive and active leisure to test for decreasing returns at high levels. We found a decreasing positive effect of sleep on health. Above 85 hours of sleep per week, additional time spent in sleep has a negative impact on health. This parabolic effect relates to the constraints of time; an additional hour of sleep is financed by spending less time in some other activity. Furthermore, sicker people may sleep more.

Similarly, we found a decreasing positive effect of passive leisure on health. Passive leisure includes activities that require little or no mental or physical exertion (see Table 1). We found that passive leisure improves health up to a point; the marginal returns on passive leisure, however, decrease steadily and become negative (see Figure 4).8 In addition, in Step 4 we found sex differences in the effect of passive leisure on health. Men tend to

benefit more than women from passive leisure.

As anticipated, time spent in active leisure is also associated with better health (see Figure 5). As with passive leisure, spending time in active leisure improves health only up to a point, after which the marginal returns on active leisure become negative. We found sex differences in the effect of active leisure. In contrast to passive leisure, however, the benefits of active leisure are smaller for men than for women.

DISCUSSION

The findings largely support our model. When we control for time spent in gender- typed roles, men report poorer health than women. Men spend more time than women in




TABLE 4. Regression of Health on Sociodemographic Characteristics, Time Use, and Wages

Step 1 Step 2 Step 3 Step 4

b b b b (Se b) betaa (Se b) beta (Se b) beta (Se b) beta

Sex .019 .012 -.148* -.092 -.255** -.159 -.378** -.236 (Male= 1) (.073) (.084) (.096) (.169) Age - .005* -.105 .0002 .005 .0006 .012 .0004 .008

(.003) (.003) (.003) (.003) Education .071*** .274 .060*** .234 .052*** .200 .053*** .206

(.012) (.012) (.012) (.012) Married .068 .040 .087 .052 .100 .059 .089 .053

(.083) (.082) (.081) (.080) Children -.001 - .002 .013 .021 .029 .050 .038 .064

(.032) (.031) (.033) (.033) Paid work .005* .137 .002 .062 .003 .076

(.002) (.003) (.003) Wages .006* .122 .006* .111 .007* .135

(.003) (.003) (.003) Housework -.010* -.128 -.010* -.131

(.005) (.004) Child care -.006 -.046 -.008 -.060

(.008) (.008) Helping others .015* .076 .013* .070

(.008) (.008) Passive leisure .Ollt .195 .OlOt .165

(.007) (.007) Passive leisure2 -.0002* -.0002**

(.0001) (.0001) Active leisure .020* .173 .038*** .328

(.011) (.012) Active leisure2 .0009** -.0011**

(.0004) (.0004) Sleep .038t .420 .032t .352

(.026) (.026) Sleep2 - .0005* - .0004*

(.0002) (.0002) Sex x passive .011*

leisure (.005) Sex x active -.032***

leisure (.010) Constant 21.551 2.261 1.754 1.904 R 2 .108 .139 .189 .216

t p <= 10, one-tailed test b = unstandardized coefficient * p <=.05, one-tailed test Se b = standard error of b

** p <= .01, one-tailed test beta = standardized coefficient p <=.001, one-tailed test z = parabolic term

a Coefficients of nonlinear relationships were standardized according to Stolzenberg (1980). Standardized coefficients shown are calculated on the basis of the population mean.

paid work with higher wages, and higher wages are associated with better health. Women spend more time in housework, which is associated with poorer health. Although women spend more time in child care, child care is not related significantly to health. Contrary to our expectations, women do not spend more time than men in helping others. Moreover, time spent in helping others improves rather than worsens health. Neither sex spends much time in helping elderly parents, friends, or relatives (x =

1.88 hours per week). Men and women do not differ in time spent in sleeping or in passive leisure, both of which improve health up to a point. Nor do men and women differ in time spent in active leisure, although active leisure is more beneficial to women.

Our findings contribute to a growing body of literature that identifies gender differences in social roles as a significant cause of women's higher morbidity. Our finding of a reversal in sex differences in morbidity when social roles are controlled is consistent with




FIGURE 4. Effects of Passive Leisure on Health, by Sex

4.0

women

3.5

co

men 3.0-

2.5 .. 0 10 20 30 40 50

Hours of Passive Leisure

FIGURE 5. Effects of Active Leisure on Health, by Sex

4.0 -

women 3.5 -

3.0 men

2.5 0 4 8 12 16 20

Hours of Active Leisure

Verbrugge's (1989) finding of nonsignificant reversals of sex differences on several different health status measures. Our study is distinguished from previous research by our use of time-use measures of social roles. Most research uses categorical or dichotomous measures of social roles, such as whether a respondent is a parent. In contrast, time-use measures provide more precise interval-level data and allow for more meaningful comparison of the impact of particular roles across individuals. We suspect that weak measures of social roles prevented previous research, such as Verbrugge's (1989) and Gove and Hughes's (1979), from finding a significant sex reversal in morbidity.

In contrast to our hypothesis, we found no significant effect of children or time spent in caring for children on health. In fact, most other research on the impact of children on parents' psychological or physical well-being finds either insignificant or inconsistent effects of children (Ross and Mirowsky 1988; Wingard 1982). The effect of children on health may vary with the age of children or

the difficulty in obtaining child care, factors for which we have no data. An additional explanation is that the potential positive effects of children on health are counterbal- anced by the negative effects of parents' increased obligations. Consequently, the impact of children and child care may be explained partially by other variables for which we controlled, such as housework.9 Dow and Juster (1985) found that although parents enjoyed time spent with their children, having children required that parents, particularly mothers, spend more time doing things they disliked. Hill and Stafford (1980) report that each additional child under age 5 added six to seven hours per week of housework for women. When husbands contributed to child care, they typically took part in direct care, such as playing with children or watching them, rather than indirect care, such as cleaning up after them.

The positive effect of helping others on health is surprising. According to Gove and Hughes (1979), women's nurturant roles should be associated with poorer health because women would have less time left to care for themselves. By separating the time spent in child care and household labor from time spent in helping others, we may have removed the more onerous and less rewarding tasks. Consequently, time spent in helping others reflects a more limited group of altruistic activities such as helping or caring for one's spouse, relatives, or unrelated individuals. Anecdotal evidence suggests that altruistic behavior can contribute to health and well-being (Justice 1987). Clearly, acting as a primary caregiver for a chronically ill or debilitated adult can be stressful and is not necessarily conducive to one's own health. Yet in view of our sample size and the low mean on time spent in helping others (x = 1.88 hours per week), we doubt that our sample includes a sufficient number of respondents who provide significant amounts of care for a chronically ill person to allow this effect to show up.

Because women's role obligations include housework, child care, and employment, we expected women to have less time than men for passive leisure. We found no sex difference in time spent in passive leisure, but we did find a significant interaction effect between sex and passive leisure on health (see Figure 4). Men's returns on passive leisure were nearly twice as high as women's,




possibly in part because of the differences between what men and women report as leisure time. Shaw (1986) found that earlier studies of leisure activities and leisure time allocations usually classified residual time (time left after subtracting paid work time, household labor time, and sleep) as leisure time. It is unclear, however, whether all of this time should be considered leisure. "Free time" conceals many management tasks associated with running a household (such as noting recipes from magazines for use in future meal preparation). As a result, time- budget categorizations may overstate women's leisure time. Also, spending leisure time around the house can be self-defeating for many women, particularly housewives; Oak- ley (1974) found that being at home created psychological pressure in women to do housework.

We also found significant sex differences in the impact of active leisure on health (see Figure 5). Active leisure has a decreasing positive effect on health for both men and women, but the positive effect for men is smaller. This finding is consistent with research on the effects of physical exercise on self-rated health. In her study of the impact of physical exercise on well-being, Hayes (1988) found that men and women are involved in different types of active leisure activities. Men tended to spend more time in team sports, such as basketball or football, while women tended to spend more time in individual sports, such as biking and aero- bics. Though physical exercise in general was associated positively with health, the effect of team sports on health was not significant. Hayes speculated that the benefits of physical exercise for health may have been offset by the relatively high potential for injury associated with team sports. We are unable to separate out the negative effects of some activities with a higher potential for injury. Hayes's interpretation explains only partially our finding of higher returns on active leisure for women. In contrast to Hayes's measures of active leisure, which includes only activities requiring physical exertion, such as team sports, our measure also includes activities requiring mental but not necessarily physical exertion, such as working on a hobby (see Table 1). Hence our finding of the effect of active leisure on health may be influenced by activities other than physical exercise.

Our explanation is understood most clearly

in light of the effects of passive leisure. As discussed above, the returns on passive leisure for health are lower for women than for men. We believe that structural factors may limit women's opportunity to enjoy passive leisure fully. Such leisure typically takes place in the home, where women may feel more pressure than men to do housework. Though we analyzed only respondents' primary activity, women who report that they were engaged in passive leisure (e.g., watching television) also may be doing housework (e.g., folding laundry) at the same time. Because active leisure, such as picnicking or painting, requires respondents to be focused on their activity, it may preclude secondary activities such as housework. In addition, many of the activities included in our active leisure measure take place away from the home, thus providing an escape from household labor. Whereas the positive effects of passive leisure for women are offset by pressure to do housework simultaneously, participating in active leisure allows women to reap more benefits from leisure time.

In interpreting our findings, readers should take account of several possible limitations of our study. These limitations, discussed below, include the possibility that the sex differences in morbidity are merely artifacts of sex differences in the tendency to seek medical care; that there are significant sex differences in individuals' responses to questions about their health; and that respondents' preference for or satisfaction with a given role may mediate the effects of those roles on health.

Are sex differences in morbidity explained by the difference between men's and women's propensities to seek medical care? A number of researchers have argued that sex differences in morbidity are artifactual, caused by women's greater tendency to utilize health services. According to this view, women are socialized to take better care of themselves when ill and thus are more likely to seek care. Also, taking time out to see the doctor is easier for women because women are less involved in the labor force and because they face fewer time constraints and financial consequences than men (Mechanic 1976). Although it is true that women are employed less than men (in our sample 67.2% of the men and only 28.6% of the women worked full-time), our research and other research on time use suggest that women face




considerable constraints on their free time, often greater than the constraints faced by men (see Berk and Berk 1979; Firestone and Shelton 1988; Juster and Stafford 1985). This view is supported by Cleary et al. (1982), who found that women are as likely as men to report inconvenience in arranging medical appointments.

Our results do not address the argument that sex differences in morbidity are explained by women's greater tendency to seek help for a given health problem. The evidence from empirical research is clear, however, and provides no support for this view. Rather, clinical and epidemiological studies have found consistently that even after reproductive conditions are taken into account, women experience an excess in certain types of diseases, including gallbladder conditions, liver problems, diseases of the urinary system, hemorrhoids, thyroid conditions, diabetes, allergies, varicose veins, arthritis, and cancers (Cleary et al. 1982; Verbrugge 1985; Waldron 1983; Wingard 1982). In their study of factors predicting use of health services, Cleary and his colleagues (1982) concluded that sex differences in health are real and not simply differences in help seeking.

Some authors also have argued that women report morbidity more readily than men. According to this view, women are more likely than men to perceive symptoms and are more willing to articulate them. Conse- quently, comparing men's and women's responses to questions about their health is not valid because women's reports may be inflated in relation to men's (Mechanic 1976; Phillips and Segal 1969; Verbrugge 1976). As in the help-seeking explanation, empirical research does not support differences in reporting as an explanation of sex differences in morbidity. Studies find no general sex differences in predisposition to report morbidity. Instead, sex differences in reporting vary depending on the particular morbidity measure considered (Cleary et al. 1982; Waldron 1983). Research that specifically examines sex differences in response to the self-rated health question shows mixed results. After reviewing relevant studies, Waldron (1983) concluded that women's self ratings of health are more pessimistic than men's. Other researchers, however, have reported that women tend to be more optimistic in evaluating their health than men; even women

who rated their health as good or excellent tended to report more conditions than men (Fillenbaum 1979; Mossey and Shapiro 1982). 10

Our view is that response differences between men and women-regardless of the direction-are minor and do not affect our results significantly. We believe that the inconsistent findings of studies which have examined response differences are due largely to the use of different measures of health, ranging from self-reports of symptoms, dis- ability days, and medical utilization to assessments by physicians. All of these measures have problems as valid and reliable measures of health status; all may be biased somewhat by the sex of the respondent." As noted earlier, self-rated health has proved to be a remarkably strong predictor of subse- quent mortality. The significance of self-rated health in predicting mortality-perhaps the only objective measure of health status-is the same whether the respondent is male or female. In fact, the significance of this measure is the same whether one is in excellent or poor objective health, old or young (Mossey and Shapiro 1982).

Finally, though time-use measures provide an accurate estimate of the extent to which an individual occupies a given role, the use of these measures assumes that time spent in these roles is equally desirable for all individuals. Most individuals find activities such as leisure pursuits intrinsically satisfying, at least to a point (Robinson 1985). However, the satisfaction associated with other activities, such as housework, may vary depending on a respondent's expectations. Research employing time use could be improved by examining respondents' preferences for or satisfaction with time spent in different roles. In addition, research on the impact of preferences for different gender roles on psychological well-being suggests that the preferences of a respondent's spouse should be considered as well. Ross, Mirowsky, and Huber (1984) found that the effect of a wife's employment on her depression level (and that of her husband) depends on the relationship between the two partners' preferences re- garding her employment, and on whether the husband helped with the housework.

CONCLUSION

Our results show that women receive




slightly less education than men, earn lower wages, spend less time in paid work, and spend more time doing housework and caring for children. These differences favor men's health because higher education, higher wages, and more hours of paid work improve health, whereas housework worsens it. Even women who work full-time do a dispropor- tionate amount of housework and child care. In contrast, men benefit from their primary role as breadwinner both directly through higher wages and indirectly through less time spent in more onerous roles. Just as men benefit economically from the gender division of social roles, they also accrue health benefits from greater participation in more rewarding roles.'2 Although these data were collected a decade ago, we believe that time use has changed little since 1981.

If men's and women's social roles were the same- if men and women spent their time in the same way and were paid equally for the work they do-women would have better self-rated health than men. The disadvantage that women face in morbidity is due to social roles. We conclude that if gender roles were more equal, women would experience better health than men, more consistent with their greater longevity.

NOTES

1. Because the 1981 study is a follow-up to the 1976 study, it would be possible to examine whether changes in time use predicted changes in health. Yet in view of the small size of the 1981 sample, we believe the 1976 data would yield measures of change inadequate for examining the effects of change in time use on health.

2. For example, if two weekday diaries were collected, they were multiplied by 2.5 and added to the weekend diaries; if one weekday diary was collected, it was multiplied by 5 and added to the weekend diaries.

3. We found only minor differences when we compared the 151 cases excluded from the analysis to the 469 cases analyzed. These two groups showed no significant differences in respondents' health, sex, age, number of children, or wages. Respondents who were excluded from the sample, however, tended to be less educated (p <= .05) and were less likely to be married (p <= .001). Although the groups differed significantly on the mean values of two of the six time-use variables- housework and child care-the excluded group consisted of only 18 valid cases on one

variable and 38 on the other. Hence the value of comparing the means is doubtful on these variables.

4. The self-rated health question was asked during the second wave of interviews. The second wave, for all practical purposes, was the halfway point in the data collection because respondents had to complete only three waves to allow a synthetic week to be computed.

5. In the case of rheumatoid arthritis, for example, women show an excess for medi- cally evaluated joint swelling but not for rheumatoid factor or X-ray evidence; all three signs are medical indicators of rheumatoid arthritis (Waldron 1983).

6. The bivariate association between parenthood and good health is spurious because of strong associations of marriage and age with parenthood and health. Consequently we do not display a figure illustrating the effect of parenthood on health.

7. Figures 2 and 3 are based on mean reported health levels by sex. Figure 2 shows mean health by marital status and sex. Respondents were not considered married unless they were married with spouse present. Figure 3 com- pares the mean health of full-time and nonemployed respondents by sex. Full-time employment was measured as 40 or more house of paid work per week; respondents were considered not employed if they had no hours of paid work per week.

8. The decreasing returns on leisure may be a result of selection as well as causation. Sicker people may spend less time in work and other activities and more time in various forms of passive leisure.

9. The- lack of significance also can be attributed in part to our relatively small sample size. When we controlled for time use, the coefficient for child care became negative but was not large enough to be significant. With a larger sample, and thus more parents with children, particularly young children, we believe the coefficient for child care would be significant.

10. On the one hand, if Waldron is correct in concluding that women rate their health as poorer than do men even when "objective" measures show their health status to be equal, then our results are strengthened because it would be more difficult to obtain the significant reversal in sex differences. On the other hand, if women inflate their ratings of health relative to men's our results are more questionable. The sex reversal in morbidity could result from women's positive response bias. Given the results of previous studies, we believe that if there are system- atic differences between the ways in which




men and women respond to self-rated health questions, the former scenario is more plausible. In these studies (e.g., Gove and Hughes 1979; Verbrugge 1989) a wide variety of variables, including social roles, were controlled. Yet no significant reversal in morbidity levels occurred: men were still predicted to heave self-rated health levels equal to or higher than women's. If women do rate their health significantly higher than do men with the same actual health, previous studies would have been more likely to find a significant reversal.

11. Our view is supported by Cleary, Mechanic, and Greenly's (1982) observation that the

strongest sex difference occurred on more "objective" and more specific indicators, whereas the more subjective and more general measures of health showed the smallest sex differences.

12. Our findings support differential role theory of sex difference in health rather than a differential vulnerability explanation. Only active and passive leisure had significantly different impacts on men and on women. In both cases, we attribute this difference to sex differences in the type of activities reported as leisure. We suggest that women's primary responsibility for domestic work affects the quality of their leisure time.

APPENDIX. Comparison of Means and Counts of Sample Subsets

Total Population Sample Analyzed Sample Excluded

Demographics Health 3.26 (N=542) 3.26 (N=469) 3.29 (N=73) Sex (N=620) (N=469) (N= 151)

Female 59.5% 60.3% 57.0% Male 40.5% 39.7% 43.0%

Age 48.80 (N=620) 48.59 (N=469) 49.44 (N= 151) 25 to 39 37.6% 39.2% 32.5% 40 to 59 34.2% 31.8% 41.7% 60+ 28.2% 29.0% 25.8%

Education 12.57 (N=618) 12.73 (N=469) 12.05 (N= 149)* <12 years 23.1% 20.0% 32.9% 12 to 15 years 54.5% 56.9% 47.0% 16+ years 22.3% 23.0% 20.1%

Marital status (N = 620) (N =469) (N= 151)*** Married 63.1% 68.4% 46.4% Not married 36.9% 31.6% 53.6%

Children 1.16 (N=620) 1.15 (N=469) 1.19 (N=151) 0 46.3% 47.1% 43.7% 1 + 53.7% 52.9% 56.3%

Wages 11.52 (N=584) 11.78 (N=469) 10.43 (N=115) Time Use

Paid work 25.90 (N=620) 25.18 (N=469) 28.17 (N= 151) Not employed 36.1% 37.3% 32.5% 1-20 hours 6.6% 6.8% 6.0% 21-39 hours 11.6% 11.9% 10.6% 40+ hours 45.6% 43.9% 51.0%

Housework 11.32 (N = 507) 11.62 (N = 469) 13.56 (N = 18)** Child care 3.14 (N=507) 3.30 (N=469) 1.15 (N=38)*** Helping others 1.94 (N= 507) 1.88 (N =469) 2.75 (N=38) Passive leisure 22.26 (N=507) 22.31 (N=469) 21.64 (N=38) Active leisure 5.07 (N=507) 5.20 (N=469) 3.55 (N=38)

Mean comparison of females to males. * p <=.05; ** p <=.01; *** p <.001; two-tailed tests.




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Chloe E. Bird is a Ph.D. candidate in sociology at the University of Illinois, Urbana-Champaign. Her main research interests focus on gender differences in health and gender stratification in the labor force. Her dissertation examines women's gains in three medical professions: dentistry, medicine, and veterinary medicine.

Allen M. Fremont is a medical student and a Ph.D. candidate in sociology in the Medical Scholars Program at the University of Illinois, Urbana-Champaign. His primary research interests include the impact of social psychological factors on health and the influence of organizational factors on physicians' decision making. His dissertation examines factors affecting physicians' practice styles in a large multispecialty group practice.



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