The authors have declared that no competing interests exist.
Paradoxically, despite their longer life expectancy, women report poorer health than men. Time devoted to differing social roles could be an explanation for the observed gender differences in health among the elderly. The objective of this study was to explain gender differences in self-reported health among the elderly by taking time use activities, socio-economic positions, family characteristics and cross-national differences into account.
Data from the Multinational Time Use Study (MTUS) on 13,223 men and 18,192 women from Germany, Italy, Spain, UK and the US were analyzed. Multiple binary logistic regression models were used to examine the association between social factors and health for men and women separately. We further identified the relative contribution of different factors to total gender inequality in health using the Blinder-Oaxaca decomposition method.
Whereas time allocated to paid work, housework and active leisure activities were positively associated with health, time devoted to passive leisure and personal activities were negatively associated with health among both men and women, but the magnitude of the association varied by gender and country. We found significant gender differences in health in Germany, Italy and Spain, but not in the other countries. The decomposition showed that differences in the time allocated to active leisure and level of educational attainment accounted for the largest health gap.
Our study represents a first step in understanding cross-national differences in the association between health status and time devoted to role-related activities among elderly men and women. The results, therefore, demonstrate the need of using an integrated framework of social factors in analyzing and explaining the gender and cross-national differences in the health of the elderly population.
Over the past decades, population ageing has been one of the major global demographic processes [
Apart from biological factors and SEP, social roles and activities may explain gender differences in health [
However, the extent of gender and cross-national differences in the distribution of time regarding role-related activities varies by social norms and national policies [
Several studies have explored the relationship between social roles and health [
In this study, we operationalized social roles as time allocation to the various role-related activities among older men and women based on Bird and Fremont [
So far, only four studies have examined the relationship between time allocation and health [
The following research questions will be addressed:
1a) How do time use activities, SEP and family characteristics impact the health among the elderly?
b) To what extent do these effects vary by gender and across countries.
c) To what extent do these social factors explain gender differences in health among the elderly.
We used data from the Multinational Time Use Study (MTUS, version W53). The MTUS data is a large cross-national, harmonized and comparative time-use database from 25 countries across six waves. This data collection has been organized by the Centre for Time Use Research, located in the Department of Sociology at the University of Oxford. The data set contains information on the socio-economic and demographic background of the respective diarist and the total time spent on 41 activities over a 24-hour period [
For the purpose of this study, we limited our sample set to respondents who were 65 years and above at the time of study. The minimum age was chosen based on the retirement age in most EU countries [
The study used self-reported health as a measure of health status (“How is your health in general; would you say that it is ….?” response options: zero (poor) to three (very good)). We created a dichotomous outcome as in [
All time use variables were measured in hours per day. We limited our study to respondents who reported all 1440 minutes (24 hours) of activities during the day in the diary, and hence adopted the broad categories suggested earlier by Gauthier and Smeeding [
Paid work (e.g. paid work, travel to and from work)
Housework (e.g. cooking, washing, gardening, shopping)
Active leisure Activities (e.g. walking, volunteer, sports, travel for pleasure)
Personal activities (e.g. sleep, eating, bathing, dressing, medical care)
Passive leisure activities (e.g. watching television, relaxing)
Socio-economic positions were measured by three indicators: Education, wealth and employment status. Education was categorized into three groups: less than secondary education, completed secondary education and above secondary education. Housing tenure (owner occupier vs. renting) and car ownership (no car, one car and two or more cars) were the two indicators used to measure wealth. Employment status in two categories was included in the model to examine the impact of paid employment at older ages. Family characteristics were measured by household size categorized into three groups: single person household, two person household, and three or more person household.
The analytic strategy included three separate steps. In the first step, the descriptive analysis provided information on distributional characteristics of all variables including the mean time allocated to the various activities across all countries. In the second step, we applied binary logistic regressions to examine the association between time use, SEP, family characteristics and self-reported health simultaneously. The analyses were done separately for men and women as well as pooled models. Estimates in the pooled models were derived from hierarchical modeling of self-reported health in which the variables were added sequentially.
The binary logit model estimated the probability of the dependent variable (self-reported health) to be 1 (Y = 1), which is expressed mathematically as follows:
In the third step, a decomposition method was applied to identify the relative contribution of the different factors to total gender inequality in health. We used an extension of the Blinder-Oaxaca decomposition method proposed by Yun [
This decomposition method allows partitioning the health differences between men and women into two components, with men as the reference group [
The descriptive statistics for men and women for each country are shown in Tables
Germany | Italy | Spain | UK | USA | ||||||
---|---|---|---|---|---|---|---|---|---|---|
( |
( |
( |
( |
( |
||||||
Mean / % | SD | Mean/ % | SD | Mean/ % | SD | Mean/ % | SD | Mean/ % | SD | |
Good | 39.2% | 26.6% | 41.5% | 52.9% | 67.4% | |||||
Poor | 60.8% | 73.4% | 48.5% | 47.1% | 32.6% | |||||
Paid work hours/day | 0.39 | 1.66 | 0.38 | 1.68 | 0.22 | 1.22 | 0.24 | 1.30 | 0.83 | 2.43 |
Less than 1 | 92.8% | 94.4% | 96.0% | 95.5% | 87.4% | |||||
1or more | 7.2% | 5.6% | 4.0% | 4.5% | 12.7% | |||||
House work hours/day | 3.62 | 2.40 | 2.69 | 2.48 | 2.44 | 2.51 | 3.79 | 2.40 | 2.99 | 2.77 |
Less than 4 | 57.3% | 72.3% | 74.4% | 55.1% | 69.9% | |||||
4 to 6 | 25.8% | 17.2% | 15.6% | 26.1% | 14.5% | |||||
>6 | 16.9% | 10.5% | 9.9% | 18.9% | 15.6% | |||||
Active leisure hours/day | 4.55 | 2.68 | 4.26 | 2.69 | 4.17 | 2.72 | 3.93 | 2.90 | 3.62 | 3.22 |
Less than 2 | 16.3% | 20.6% | 22.8% | 27.5% | 37.5% | |||||
2 to 4 | 31.1% | 29.6% | 27.2% | 31.6% | 24.6% | |||||
>4 | 52.6% | 49.9% | 50.0% | 40.8% | 37.9% | |||||
Passive leisure hours/day | 3.66 | 2.03 | 4.33 | 2.17 | 4.51 | 2.25 | 4.57 | 2.50 | 5.81 | 3.77 |
Less than 3 | 37.9% | 26.9% | 25.1% | 26.9% | 23.9% | |||||
3 to 5 | 40.1% | 39.7% | 38.8% | 35.4% | 25.6% | |||||
>5 | 22.0% | 33.5% | 36.2% | 37.6% | 50.6% | |||||
Personal activity hours/day | 11.93 | 1.95 | 12.71 | 2.14 | 12.97 | 2.35 | 11.17 | 1.90 | 10.89 | 2.52 |
Less than 10 | 12.3% | 7.1% | 5.4% | 23.7% | 34.4% | |||||
10 to 12 | 44.9% | 34.0% | 30.9% | 49.1% | 39.9% | |||||
>12 | 42.8% | 59.0% | 63.7% | 27.2% | 25.7% | |||||
71.21 | 4.81 | 72.50 | 5.01 | 72.59 | 5.09 | 72.40 | 5.10 | 72.89 | 5.26 | |
65–69 | 44.1% | 35.0% | 34.7% | 35.4% | 33.8% | |||||
70–74 | 29.6% | 29.2% | 27.8% | 28.1% | 24.9% | |||||
75–79 | 17.1% | 19.6% | 19.6% | 21.3% | 20.7% | |||||
80+ | 9.3% | 16.1% | 18.0% | 15.2% | 20.7% | |||||
Incomplete Sec. or less | 10.7% | 67.5% | 68.3% | 63.3% | 21.5% | |||||
Secondary completed | 41.8% | 27.7% | 23.2% | 18.5% | 31.7% | |||||
Tertiary Completed or above | 47.5% | 4.8% | 8.5% | 18.3% | 46.9% | |||||
Land tenure | ||||||||||
Renting | 41.1% | 16.9% | 9.4% | 27.5% | 15.8% | |||||
Owner occupier | 58.9% | 83.1% | 90.7% | 72.6% | 84.3% | |||||
Car Ownership | ||||||||||
No car | 11.8% | 16.0% | 40.9% | 27.9% | - | - | ||||
1 Car | 72.3% | 48.4% | 42.0% | 57.2% | - | - | ||||
2+ Car | 16.0% | 35.6% | 17.1% | 14.9% | - | - | ||||
Not working for pay | 83.7% | 92.4% | 96.3% | 91.8% | 77.2% | |||||
Currently in paid employment | 16.3% | 7.6% | 3.7% | 8.2% | 22.8% | |||||
2.10 | 0.81 | 2.36 | 1.02 | 2.55 | 1.20 | 1.91 | 0.70 | 1.77 | 0.93 | |
1 Member | 14.0% | 12.9% | 10.5% | 21.9% | 40.6% | |||||
2 Members | 70.1% | 75.6% | 52.3% | 69.1% | 49.9% | |||||
3+ Members | 15.9% | 11.5% | 37.2% | 9.1% | 9.6% |
Germany | Italy | Spain | UK | USA | ||||||
---|---|---|---|---|---|---|---|---|---|---|
( |
( |
( |
( |
( |
||||||
Mean / % | SD | Mean / % | SD | Mean / % | SD | Mean / % | SD | Mean / % | SD | |
Good | 46.8% | 16.9% | 32.6% | 52.9% | 68.2% | |||||
Poor | 53.2% | 83.1% | 67.4% | 47.1% | 31.8% | |||||
Paid work hours/day | 0.09 | 0.60 | 0.07 | 0.71 | 0.07 | 0.67 | 0.09 | 0.78 | 0.46 | 1.80 |
Less than 1 | 97.7% | 98.9% | 98.6% | 98.2% | 92.6% | |||||
1or more | 2.3% | 1.2% | 1.4% | 1.8% | 7.4% | |||||
House work hours/day | 4.64 | 2.34 | 5.14 | 2.74 | 4.77 | 2.71 | 4.47 | 2.29 | 3.79 | 2.88 |
Less than 4 | 38.7% | 31.7% | 35.8% | 40.9% | 58.4% | |||||
4 to 6 | 35.4% | 30.3% | 32.6% | 35.1% | 20.4% | |||||
>6 | 25.8% | 38.0% | 31.7% | 24.1% | 21.2% | |||||
Active leisure hours/day | 4.15 | 2.59 | 2.97 | 2.21 | 2.86 | 2.28 | 3.63 | 2.54 | 3.84 | 3.09 |
Less than 2 | 22.1% | 34.3% | 37.5% | 27.7% | 33.1% | |||||
2 to 4 | 31.6% | 38.6% | 36.2% | 35.8% | 25.7% | |||||
>4 | 46.3% | 27.2% | 26.3% | 36.4% | 41.2% | |||||
Passive leisure hours/day | 3.39 | 1.88 | 3.78 | 2.10 | 4.14 | 2.22 | 4.22 | 2.31 | 4.85 | 3.35 |
Less than 3 | 41.9% | 37.5% | 31.1% | 30.3% | 32.9% | |||||
3 to 5 | 42.4% | 38.9% | 39.3% | 38.9% | 26.8% | |||||
>5 | 15.7% | 23.6% | 29.6% | 30.8% | 40.3% | |||||
Personal activity hours/day | 11.97 | 2.03 | 12.47 | 2.19 | 12.57 | 2.32 | 11.17 | 1.96 | 11.18 | 2.57 |
Less than 10 | 10.2% | 8.0% | 7.0% | 23.0% | 29.9% | |||||
10 to 12 | 47.1% | 39.1% | 39.6% | 48.9% | 39.8% | |||||
>12 | 42.7% | 53.0% | 53.4% | 28.0% | 30.3% | |||||
71.74 | 5.13 | 73.3 | 5.21 | 73.24 | 5.18 | 73.1 | 5.00 | 73.89 | 5.34 | |
65–69 | 41.7% | 29.5% | 30.4% | 30.6% | 28.0% | |||||
70–74 | 25.6% | 26.9% | 27.1% | 27.7% | 22.5% | |||||
75–79 | 18.8% | 20.7% | 19.8% | 23.0% | 20.8% | |||||
80+ | 13.9% | 22.9% | 22.6% | 18.7% | 28.7% | |||||
Incomplete Sec. or less | 28.9% | 80.1% | 77.7% | 76.5% | 21.3% | |||||
Secondary completed | 53.6% | 17.9% | 18.5% | 13.5% | 38.4% | |||||
Tertiary Completed or above | 17.5% | 2.1% | 3.9% | 10.0% | 40.3% | |||||
Land tenure | ||||||||||
Renting | 51.7% | 23.3% | 12.0% | 30.8% | 21.0% | |||||
Owner occupier | 48.3% | 76.7% | 88.0% | 69.2% | 79.0% | |||||
Car ownership | ||||||||||
No car | 32.4% | 38.3% | 55.4% | 48.6% | - | - | ||||
1 Car | 59.2% | 34.1% | 32.1% | 43.5% | - | - | ||||
2+ Car | 8.4% | 27.6% | 12.5% | 7.9% | - | - | ||||
Not working for pay | 92.4% | 98.4% | 98.4% | 95.7% | 86.4% | |||||
Currently in paid employment | 7.6% | 1.6% | 1.6% | 4.3% | 13.6% | |||||
1.78 | 1.00 | 2.00 | 1.10 | 2.31 | 1.30 | 1.62 | 0.72 | 1.52 | 0.87 | |
1 Member | 45.0% | 36.6% | 26.0% | 47.3% | 62.3% | |||||
2 Members | 42.2% | 42.2% | 43.2% | 46.8% | 30.2% | |||||
3+ Members | 12.8% | 21.3% | 30.8% | 5.9% | 7.5% |
Time use varied considerably between men and women and across countries. Overall, women allocated more time to housework activities compared to men. On the other hand, elderly men tended to devote more time to active leisure, passive leisure and paid work. The cross-country comparison revealed that women in Italy spent on average more time on housework activities (5.1 hours per day). US women spent remarkably fewer hours on housework activities (3.8 hours per day). Time devoted to active leisure did not vary much across countries among men but women. The most time devoted to active leisure was found in Germany (4.1 hours per day), the least active leisure time in Italy (3.0 hours per day) and Spain (2.9 hours per day). Allocation of time for paid work was highest in the US for men and women. Regarding the time allocated to passive leisure, men in the US devoted most hours to these activities (5.8 hours per day), while the lowest value was observed in Germany (3.7 hours per day). Women in all countries spent less time on passive leisure. Finally, the analysis of personal activities showed that men and women in Spain devoted the most time to personal activity (13 and 12.6 hours per day) while the least time spent on these activities was found in the UK and the US (approximately 11.2 hours per day).
The results of the multivariate logistic regression models are shown in Tables
Men and women 65+ years old.
Variables | Model 1 |
Model 2 |
Model 3 |
---|---|---|---|
aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | |
Paid work hours/day | |||
Less than 1 (ref) | |||
1or more | 0.38 (0.34–0.44) |
1.00 (0.84–1.19) | 0.75 (0.63–0.90) |
House work hours/day | |||
Less than 4 (ref) | |||
4 to 6 | 0.93 (0.87–0.99) |
0.86 (0.81–0.92) |
0.76 (0.71–0.81) |
>6 | 0.92 (0.86–1.00) |
0.83 (0.76–0.90) |
0.65 (0.60–0.71) |
Active leisure hours/day | |||
Less than 2 (ref) | |||
2 to 4 | 0.82 (0.77–0.87) |
0.86 (0.81–0.92) |
0.75 (0.70–0.81) |
>4 | 0.57 (0.53–0.61) |
0.66 (0.61–0.71) |
0.53 (0.49–0.58) |
Passive leisure hours/day | |||
Less than 3 (ref) | |||
3 to 5 | 1.24 (1.17–1.32) |
1.16 (1.09–1.23) |
1.14 (1.07–1.21) |
>5 | 1.38 (1.28–1.48) |
1.24 (1.15–1.34) |
1.31 (1.21–1.42) |
Personal activity hours/day | |||
Less than 10 (ref) | |||
10 to 12 | 1.54 (1.43–1.66) |
1.29 (1.19–1.39) |
1.01 (0.94–1.10) |
>12 | 2.84 (2.62–3.07) |
2.05 (1.89–2.23) |
1.43 (1.31–1.56) |
Men (ref) | |||
Women | 1.20 (1.14–1.27) |
1.32 (1.25–1.40) |
|
65–69 (ref) | |||
70–74 | 1.14 (1.07–1.22) |
1.15 (1.08–1.23) |
|
75–79 | 1.35 (1.26–1.45) |
1.41 (1.31–1.52) |
|
80+ | 1.32 (1.23–1.42) |
1.44 (1.33–1.55) |
|
Incomplete Sec. or less (ref) | |||
Secondary completed | 0.47 (0.45–0.50) |
0.58 (0.54–0.61) |
|
Tertiary completed or above | 0.27 (0.25–0.29) |
0.47 (0.43–0.51) |
|
Land tenure | |||
Renting (ref) | |||
Owner occupier | 0.81 (0.76–0.86) |
0.80 (0.75–0.86) |
|
Not working for pay (ref) | |||
Currently in paid employment | 0.42 (0.37–0.48) |
0.52 (0.45–0.59) |
|
1 member (ref) | |||
2 members | 1.18 (1.11–1.26) |
1.03 (0.97–1.10) | |
3+ members | 1.34 (1.24–1.44) |
1.03 (0.95–1.11) | |
Germany (ref) | |||
Italy | 2.85 (2.59–3.14) |
||
Spain | 1.19 (1.09–1.31) |
||
United Kingdom | 0.68 (0.61–0.76) |
||
United States | 0.47 (0.43–0.52) |
||
Observations | 31,425 | 31,425 | 31,425 |
Pseudo R2 | 0.052 | 0.107 | 0.152 |
Log Likelihood | -20263.22 | -19090.029 | -18128.073 |
aOR- adjusted Odd Ratio,
** p<0.01,
* p<0.05. Regression include day-of-week dummies.
1 Includes only time use activities
2 Includes time use activities, socio-economic position and family characteristics
3 Includes time use activities, socio-economic position, family characteristics and countries
Men, 65+ years old.
Variables | Germany | Italy | Spain | UK | USA |
---|---|---|---|---|---|
aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | |
Paid work hours/day | |||||
Less than 1 (ref) | |||||
1or more | 0.77 (0.42–1.41) | 0.72 (0.45–1.17) | 0.66 (0.42–1.03) |
0.70 (0.33–1.48) | 0.63 (0.40–1.00) |
House work hours/day | |||||
Less than 4 (ref) | |||||
4 to 6 | 0.92 (0.70–1.22) | 0.72 (0.58–0.90) |
0.72 (0.60–0.88) |
0.62 (0.46–0.83) |
0.65 (0.49–0.85) |
>6 | 0.73 (0.50–1.07) | 0.98 (0.71–1.34) | 0.58 (0.44–0.76) |
0.46 (0.31–0.68) |
0.50 (0.36–0.69) |
Active leisure hours/day | |||||
Less than 2 (ref) | |||||
2 to 4 | 1.07 (0.76–1.50) | 1.07 (0.84–1.36) | 0.74 (0.61–0.89) |
0.59 (0.43–0.81) |
0.78 (0.62–0.99) |
>4 | 0.64 (0.43–0.94) |
0.77 (0.59–1.01) |
0.61(0.50–0.76) |
0.45 (0.31–0.65) |
0.47 (0.36–0.61) |
Passive leisure hours/day | |||||
Less than 3 (ref) | |||||
3 to 5 | 1.27 (0.98–1.65) | 1.12 (0.93–1.35) | 1.09 (0.92–1.28) | 1.26 (0.93–1.71) | 1.13 (0.85–1.50) |
>5 | 1.35 (0.96–1.90) | 1.35 (1.07–1.70) |
1.13 (0.93–1.37) | 1.16 (0.82–1.64) | 1.24 (0.92–1.68) |
Personal activity hours/day | |||||
Less than 10 (ref) | |||||
10 to 12 | 1.22 (0.84–1.78) | 0.94 (0.70–1.27) | 1.23 (0.91–1.65) | 0.80 (0.60–1.09) | 1.04 (0.83–1.31) |
>12 | 1.51 (1.01–2.25) |
1.19 (0.87–1.62) | 1.91 (1.42–2.58) |
0.99 (0.69–1.41) | 1.68 (1.30–2.16) |
65–69 (ref) | |||||
70–74 | 1.09 (0.84–1.42) | 1.77 (1.47–2.13) |
1.14 (0.97–1.34) | 0.89 (0.66–1.20) | 1.00 (0.78–1.28) |
75–79 | 1.43 (1.04–1.96) |
1.63 (1.32–2.02) |
1.33 (1.11–1.61) |
1.59 (1.15–2.21) |
1.09 (0.84–1.42) |
80+ | 1.23 (0.82–1.85) | 2.52 (1.92–3.31) |
1.39 (1.14–1.70) |
1.10 (0.76–1.60) | 1.03 (0.79–1.33) |
Incomplete Sec. or less (ref) | |||||
Secondary completed | 0.78 (0.54–1.14) | 0.64 (0.54–0.75) |
0.65 (0.55–0.75) |
0.96 (0.70–1.30) | 0.54 (0.43–0.69) |
|
0.90 (0.62–1.30) | 0.60 (0.43–0.84) |
0.48 (0.38–0.61) |
0.72 (0.51–1.01) |
0.40 (0.32–0.51) |
Land tenure | |||||
Renting (ref) | |||||
Owner occupier | 0.91 (0.72–1.15) | 0.92 (0.75–1.14) | 0.94 (0.75–1.18) | 0.69 (0.52–0.92) |
0.85 (0.66–1.10) |
Car ownership | |||||
No car (ref) | |||||
1 car | 0.75 (0.53–1.07) | 0.67 (0.51–0.88) |
0.72 (0.62–0.83) |
0.71 (0.53–0.95) |
- |
2+ cars | 1.10 (0.68–1.77) | 0.54 (0.40–0.71) |
0.68 (0.55–0.84) |
0.74 (0.47–1.16) | - |
Not working for pay (ref) | |||||
Currently in paid employment | 0.50 (0.34–0.74) |
0.70 (0.48–1.04) | 0.70 (0.44–1.11) | 0.38 (0.21–0.68) |
0.54 (0.39–0.75) |
1 member (ref) | |||||
2 members | 1.09 (0.78–1.51) | 0.88 (0.68–1.13) | 1.00 (0.80–1.25) | 0.94 (0.70–1.27) | 0.73 (0.60–0.89) |
3 members | 1.49 (0.96–2.30) |
0.94 (0.71–1.24) | 1.07 (0.84–1.36) | 1.95 (1.18–3.24) |
1.02 (0.74–1.41) |
Observations | 1,478 | 3,770 | 4,234 | 1,315 | 2,426 |
Pseudo R2 | 0.050 | 0.071 | 0.063 | 0.087 | 0.109 |
Log Likelihood | -940.8543 | -2026.5036 | -2693.9923 | -830.0513 | -1364.4081 |
aOR- adjusted Odd Ratio; Regression include day-of-week dummies
** p<0.01,
* p<0.05.
Women, 65+ years old.
Variables | Germany | Italy | Spain | UK | USA |
---|---|---|---|---|---|
aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | |
Paid work hours/day | |||||
Less than 1 (ref) | |||||
1or more | 0.49 (0.22–1.05) |
1.48 (0.63–3.48) | 0.95(0.48–1.87) | 0.86 (0.33–2.20) | 1.20 (0.76–1.89) |
House work hours/day | |||||
Less than 4 (ref) | |||||
4 to 6 | 0.85 (0.66–1.08) | 1.02 (0.81–1.28) | 0.69(0.59–0.81) |
0.85 (0.67–1.10) | 0.69 (0.57–0.84) |
>6 | 0.93 (0.67–1.28) | 0.75 (0.57–0.98) |
0.57(0.46–0.69) |
0.70 (0.51–0.96) |
0.62 (0.49–0.79) |
Active leisure hours/day | |||||
Less than 2 (ref) | |||||
2 to 4 | 0.83 (0.62–1.11) | 0.95 (0.78–1.16) | 0.63(0.54–0.73) |
0.97 (0.74–1.26) | 0.64 (0.53–0.77) |
>4 | 0.42 (0.30–0.60) |
0.74 (0.57–0.96) |
0.40(0.33–0.49) |
0.86 (0.62–1.19) | 0.59 (0.48–0.73) |
Passive leisure hours/day | |||||
Less than 3 (ref) | |||||
3 to 5 | 1.13 (0.90–1.42) | 1.22 (1.02–1.46) |
0.99(0.86–1.14) | 1.31 (1.02–1.70) |
1.22 (1.00–1.49) |
>5 | 1.07 (0.76–1.52) | 1.39 (1.06–1.82) |
1.06(0.87–1.28) | 2.26 (1.65–3.08) |
1.45 (1.17–1.80) |
Personal activity hours/day | |||||
Less than 10 (ref) | |||||
10 to 12 | 0.64 (0.46–0.90) |
1.00 (0.76–1.32) | 1.07(0.85–1.35) | 0.91 (0.69–1.19) | 1.17 (0.97–1.41) |
>12 | 0.85 (0.58–1.24) | 1.33 (0.98–1.81) |
1.37(1.06–1.76) |
1.44 (1.04–1.99) |
2.05 (1.66–2.53) |
65–69 (ref) | |||||
70–74 | 0.82 (0.64–1.05) | 1.34 (1.11–1.62) |
1.03(0.89–1.20) | 1.21 (0.92–1.59) | 1.06 (0.85–1.31) |
75–79 | 1.23 (0.93–1.62) | 2.33 (1.84–2.96) |
1.23(1.04–1.47) |
1.34 (1.00–1.79) |
1.16 (0.93–1.44) |
80+ | 2.04 (1.46–2.84) |
2.64 (2.02–3.46) |
1.14(0.95–1.37) | 1.36 (0.99–1.88) |
1.03 (0.84–1.27) |
Incomplete Sec. or less (ref) | |||||
Secondary completed | 0.89 (0.70–1.11) | 0.66 (0.55–0.79) |
0.47(0.41–0.54) |
1.00 (0.73–1.37) | 0.39 (0.32–0.47) |
Tertiary completed or above | 0.86 (0.63–1.17) | 0.44 (0.28–0.69) |
0.41(0.31–0.55) |
1.35 (0.93–1.95) | 0.27 (0.22–0.32) |
Land tenure | |||||
Renting (ref) | |||||
Owner occupier | 0.79 (0.63–0.97) |
0.98 (0.80–1.18) | 1.06(0.88–1.27) | 0.68 (0.53–0.87) |
0.62 (0.52–0.74) |
Car ownership | |||||
No car (ref) | |||||
1 car | 0.68 (0.53–0.87) |
0.65 (0.52–0.81) |
0.76(0.66–0.88) |
0.53 (0.41–0.69) |
- |
2+ cars | 0.69 (0.42–1.13) | 0.61 (0.49–0.78) |
0.68(0.55–0.85) |
0.23 (0.14–0.39) |
- |
Not working for pay (ref) | |||||
Currently in paid employment | 0.75 (0.50–1.13) | 0.34 (0.17–0.67) |
0.43(0.23–0.81) |
0.64 (0.35–1.18) | 0.32 (0.23–0.46) |
1 member (ref) | |||||
2 members | 2.34 (1.83–3.00) |
1.13 (0.92–1.40) | 1.05(0.89–1.23) | 1.63 (1.27–2.10) |
1.27 (1.07–1.50) |
3 members | 1.97 (1.31–2.96) |
1.25 (0.97–1.61) |
1.15(0.94–1.39) | 3.83 (2.20–6.69) |
1.04 (0.78–1.39) |
Observations | 1,848 | 4,939 | 5,659 | 1,694 | 4,052 |
Pseudo R2 | 0.079 | 0.075 | 0.071 | 0.089 | 0.131 |
Log Likelihood | -1176.0733 | -2077.1254 | -3318.7431 | -1067.6131 | -2201.9978 |
aOR- adjusted Odd Ratio; Regression include day-of-week dummies
** p<0.01,
* p<0.05.
The pooled model shows that all time use activities were related to health in the crude and the fully adjusted model (
Regarding the other factors, many patterns were similar to results from other reports. Women were more likely to report poor health than men (OR = 1.32; 95% CI = 1.25–1.40). Educational attainment was significantly associated with health status. We found a negative gradient with the prevalence of poor health increasing with decreasing educational level. Odds of reporting poor health increased with age. Furthermore, the odds of reporting poor health status was lower among homeowners than renters (OR = 0.80; 95% CI = 0.75–0.86). Respondents who were currently in paid employment were less likely to report poor health as compared to those not working for pay (OR = 0.52; 95% CI = 0.45–0.59). Surprisingly, larger household size was positively associated with poor health status in model 2, but this association disappeared in model 3. Compared to Germany, elderly people in Italy and Spain had higher odds of reporting poor health (OR = 2.85; 95% CI = 2.59–3.14 and OR = 1.19; 95% CI = 1.09–1.31), and elderly people in the UK and the US had lower odds of reporting poor health (OR = 0.68; 95% CI = 0.61–0.76 and OR = 0.47; 95% CI = 0.43–0.52).
Tables
Among men, time devoted to paid work activities was significantly associated with health in Spain and the US, but not in Germany, Italy and the UK (
Among women, only in Germany, paid work was positively associated with health. In contrast, in all countries but Germany, time spent on housework activities was positively associated with health. Time allocated to active leisure was positively associated with health in all countries except the UK. More time spent on passive leisure activities increases the likelihood of reporting poor health among women in Italy, UK and the US, but the effects were not statistically significant in Germany and Spain.
Inequality contributions in terms of differences in group characteristics (by variables) & group processes | Germany | Italy | Spain | UK |
USA |
|||
---|---|---|---|---|---|---|---|---|
Absolute (95% CI) | Percent | Absolute (95% CI) | Percent | Absolute (95% CI) | Percent | Absolute (95% CI) | Absolute (95% CI) | |
Predicted mean in women | 0.532 (0.510–0.555) | 0.831 (0.820–0.841) | 0.674 (0.662–0.686) | 0.470 (0.447–0.494) | 0.318 (0.304–0.332) | |||
Predicted mean in men | 0.392 (0.368–0.417) | 0.734 (0.720–0.749) | 0.585 (0.570–0.599) | 0.471 (0.444–0.498) | 0.326 (0.307–0.344) | |||
Age | 0.003 (0.001–0.006) | 2.30% | 0.011 (0.008–0.014) | 11.10% | 0.003 (0.001–0.004) | 2.80% | 0.003 (-0.001–0.007) | 0.000 (-0.001–0.001) |
Education | 0.016 (0.005–0.028) | 11.50% | 0.010 (0.007–0.013) | 10.10% | 0.016 (0.012–0.019) | 17.50% | 0.002 (-0.004–0.009) | 0.003 (-0.004–0.010) |
Land tenure | 0.010 (0.000–0.021) | 7.50% | 0.013 (0.009–0.017) | 13.40% | 0.009 (0.006–0.013) | 10.50% | 0.026 (0.003–0.049) | 0.002 (-0.002–0.005) |
Car | 0.005 (0.001–0.009) | 3.40% | 0.001 (-0.001–0.002) | 0.80% | 0.000 (-0.001–0.001) | 0.20% | 0.003 (-0.001–0.008) | - |
Employment status | 0.010 (0.004–0.016) | 7.20% | 0.006 (0.002–0.009) | 5.80% | 0.002 (-0.000–0.004) | 2.10% | 0.006 (-0.002–0.013) | 0.007 (-0.006–0.020) |
Household Size | -0.021 (-0.031–-0.010) | -14.70% | 0.001 (-0.004–0.005) | 0.80% | -0.001 (-0.005–0.003) | -1.00% | -0.024 (-0.042–-0.007) | -0.001 (-0.003–0.001) |
Paidwork | 0.009 (0.001–0.017) | 6.70% | 0.001 (-0.006–0.007) | 0.60% | 0.001 (-0.003–0.004) | 0.80% | 0.003 (-0.002–0.008) | 0.001 (-0.002–0.004) |
Housework | -0.009 (-0.026–0.009) | -6.10% | -0.000 (-0.045–0.044) | -0.50% | 0.002 (-0.040–0.044) | 2.20% | -0.022 (-0.038–-0.006) | -0.007 (-0.020–0.005) |
Active leisure | 0.013 (0.004–0.022) | 9.10% | 0.013 (-0.011–0.037) | 13.40% | 0.017 (-0.007–0.041) | 18.80% | 0.007 (-0.001–0.015) | -0.002 (-0.005–0.001) |
Passive leisure | -0.000 (-0.005–0.004) | -0.20% | -0.006 (-0.016–0.005) | -5.80% | -0.007 (-0.014–0.001) | -7.30% | -0.007 (-0.013–-0.001) | -0.002 (-0.007–0.003) |
Personal activity | 0.000 (-0.001–0.002) | 0.30% | -0.003 (-0.007–0.002) | -2.60% | -0.015 (-0.023–-0.007) | -17.00% | 0.000 (-0.001–0.001) | 0.002 (-0.002–0.006) |
Contribution to that part of inequality due differences in group characteristics |
0.038 (0.020–0.056) | 0.045 (0.035–0.056) | 0.026 (0.016–0.037) | -0.003 (-0.019–0.013) | 0.003 (-0.007–0.014) | |||
Contribution to that part of inequality due differences in group processes |
0.102 (0.073–0.131) | 73.00% | 0.051 (0.037–0.065) | 52.80% | 0.063 (0.047–0.079) | 70.30% | 0.003 (-0.029–0.035) | -0.011 (-0.032–0.010) |
* No female excess in the probability of reporting poor health
CI: 95 percent confidence interval.
In absolute terms, Germany reported the lowest and Italy the highest predicted probability in poor health. In contrast, Germany reported the highest female excess (0.140; 95% CI = 0.106–0.174) in the probability of reporting poor health followed by Italy (0.096; 95% CI = 0.079–0.114) and Spain (0.089; 95% CI = 0.070–0.108) while no female excess was found in the UK and the US.
Italy reported the highest total gender gap (approximately 47%) attributed to differences in group characteristics, followed by Spain (approximately 30%) and Germany (approximately 27%). The two largest contributing factors to this component of gender inequality in health among elderly people across all countries are education and active leisure. If women were to allocate the same time to active leisure activities as men, the female excess in the probability of reporting poor health would be reduced by approximately 18% in Spain and approximately 13% in Italy. In Germany, education is the largest contributor to the part of the inequality deriving from differences in group characteristics. The gender gap would be reduced by approximately 12% if women had the same educational attainment as men. Passive leisure contributed negatively to this part of inequality in all countries, and personal activities showed mixed contributions in different countries.
As far as we know, this is the first study to analyze simultaneously the relationship between time use activities, SEP, household characteristics and health among elderly men and women in four European countries and the US. Our study also examined gender and cross-country differences in patterns of time use among the elderly. On the descriptive level, our study showed that elderly women allocate more time to housework activities as compared to men. Elderly men tended to devote more time to active leisure, passive leisure and paid work with some cross-country variations. All time use activities were related to health with paid work, housework and active leisure activities positively and passive leisure and personal activities negatively associated with health. However, the magnitude of the association varied by gender and across countries. We found gender differences in health, but these differences vary visibly across countries with no gender gap in health observable in the UK and the US. Decomposing the gap in health, the study showed that differences in the time allocated to active leisure and level of educational attainment accounted for the largest share of the health gap.
Our findings could only partially confirm the conventional view on the health paradox in contemporary welfare countries [
Explanations for the existence or the non-existence of the gender gap in health refer to differences in labor force participation, education, recreational activities, and domestic activities among men and women [
Prior evidence demonstrated that health inequality based on social class exists among the elderly [
In all the countries, the labour force participation among the elderly was lower for women than for men (Tables
Housing tenure and car ownership were used as proxy indicators for measuring wealth. These two indicators were significantly associated with health among men and women, but not in all countries observed. For example, we found a strong positive association with health among male owner occupiers in the UK, which could not be observed in the other countries. Meanwhile, among women, this strong association persisted not only in the UK but also Germany and the US. This results concurred with the findings of Dalstra et al. [
Surprisingly, our findings regarding household size suggest that living in a larger household is associated with poor health among elderly men and men. However, the association was stronger among women than men, except in Italy. Among men, the association was not significant in the southern European countries. A common explanation for this phenomenon is that people living in larger households, especially women, suffer more stress [
Regarding time spent on housework activities, the results showed that there were gender and cross-country differences. Women spent more time than men in housework activities, consistent with prior evidence [
The few studies that examined the effects of housework on the health among elderly men have given inconsistent results [
Paid work at older ages was associated with good health among men, as found in previous studies [
Active leisure was positively associated with health status for both elderly men and women in all countries, consistent with prior evidence [
Nevertheless, it seems that regardless of cultural or social norms time devoted to active leisure activities is positively related to health of the elderly. Previous studies have also stressed the importance of older adult’s participation in active leisure activities for social, psychological and physical health benefit. For example, a study by Kim et al. [
In contrast to active leisure activities, passive leisure activities such as listening to radio and tapes, watching television and relaxing were negatively associated with health among both men and women. Similar to our study, television viewing has been linked to poor health, cognitive decline, depressive symptoms and anxiety in older men and women [
Similar to passive leisure activities, more time devoted to personal activities such as sleep, meals and personal services was negatively associated with health among elderly in all countries. No difference in time devoted to personal activities was found between men and women within-countries, but there were cross-national variations. Our results also showed that elderly men and women spent more time on personal activities than those observed for the other activities but more of this time was devoted to sleep. This is not surprising because the increasing incidence of health conditions at older age restrict daily activities among the elderly [
Our findings provide evidence of the relationship between social roles (time allocated to role-related activities) and health among the elderly in a gender-specific and country-comparative context. We compared data from Germany, Spain, Italy, UK and the US. These countries represent different institutional settings and differ on national policies and social norms [
The cross-sectional design of this study prevents us to conclude any causal association. Because the association between time use activities and health may be reciprocal, conclusions such as “older people allocate less time to certain time use activities due to poor health” (and vice versa) cannot be drawn. Again, this study relied on self-reports of time use activities and general health status. Nonetheless estimates from time use surveys have been found to be more accurate and reliable than survey estimates [
Another possible limitation is that only primary activities were considered in the analysis due to data limitations, although it has been shown that performing secondary activities like care activities and watching television simultaneously with primary activities may provide some detailed information about time use. Thus, eliminating parallel activities may distort the picture of the time devoted to the various task of life. However, in practice, secondary activities are usually ignored in time-use analysis [
Despite these limitations, this study provides the first overview of time use activities and their relationship with health using a large-scale and comparative set of time use data across Europe and the US of the elderly population.
The overall goal of this study was to explain the gender differences in health among the elderly by taking time use activities and other social factors into account. We conclude that education and time spent on active leisure are the largest contributors to gender differences in health among the elderly. The evidence provided in this study demonstrates the need for and usefulness of an integrated framework of social factors in analyzing and explaining the gender and cross-national differences in health among the elderly.
(DOCX)
The data used for this study comes from the Multinational Time Use Study (MTUS). The authors thank Ingeborg Jahn and Elisabeth Jonas for their useful comments and suggestions.