The authors have declared that no competing interests exist.
Work demands often disrupt sleep. The stress of higher status theory posits that workers with greater resources often experience greater stress. We extend this theory to sleep and ask: do managers report more disrupted sleep and does this vary by gender and country context? Data come from the 2012 European Social Survey Programme and our sample comprised those currently employed in their prime working age (n = 27,616; age 25–64) in 29 countries. We include country level measures of the Gender Development Index (GDI) and gross domestic product (GDP). We find that workers sleep better, regardless of gender, in countries where women are empowered. For managers, women sleep better as GDI increases and men as GDP increases. Our results suggest that men experience a sleep premium from economic development and women from gender empowerment.
Sleep is increasingly understood as another form of inequality connected to our waking lives. The literature on sleep and work can be divided into two broad streams. The first identifies the ways in which work demands impinge upon sleep. Work time has a strong negative impact on sleep [
Another stream of sleep research focuses on how sleep differs by gender. The bulk of this work focuses on the gendered distribution of family demands and sleep. This literature shows that sleep is socially patterned by gender, with children and spouses more likely to disrupt women’s than men’s sleep [
To conceptualize these relationships, we draw upon the stress of higher status theory that posits workers in higher status, more demanding jobs experience greater stress and strain [
We apply data from the most recent wave (2012) of the European Social Survey (ESS) with a sleep quality measure. These data are paired with two country-level measures—the Gender Development Index (GDI; Source: United Nations Development Programme [
Sleep is intimately connected to structures of gender, work and family life. Employment can have a negative impact on sleep, whether through hours worked [
Competing expectations of work and family permeating into workers’ sleep is consistent with role strain theory [
In particular, this paper is concerned with examining the sleep impact of being in a management position at work and whether this differs for men and women. There are good reasons to think managers’ sleep would be different than non-managers with important gender differences. To understand these relationships, we draw upon the stress of higher status framework. The stress of higher status theory draws upon the job demands and resources (JDR) perspective [
Empirical evidence supports these claims, as employees in jobs with higher resources—managerial, and professional workers—report greater work-family strain, more stress, and poorer emotional well-being [
Globally, women are under-represented in management positions across most industries and sectors [
Regardless of gender egalitarianism in contemporary society, many sectors of employment around the world still abide by a simple ethos in “think manager, think male” [
The work demands of managers are significantly shaped by gendered expectations and cultural stereotypes surrounding family life. Blair-Loy [
Collectively, this literature suggests that women managers continue to face barriers tied to their gender. This differential experience may add stress to women managers’ workdays which may disrupt sleep. Here, we test these assumptions by looking at whether managers report worse sleep than those in lower positions paying careful attention to gender differences in these experiences. Our results indicate that women managers are disadvantaged in sleep but with important cultural differences by gender empowerment and economic development.
Drawing upon existing literatures, we expect workers to experience different sleep quality based on their managerial status and gender. Yet, these experiences may also be structured by resources available in their national contexts. We focus here on two dimensions—gender development and gross domestic product. Our expectations are outlined in more detail below.
For many European countries, elevating women’s status by reducing structural barriers has been a primary goal. The United Nations identified gender inequality as a key barrier to human development and has been measuring women’s status as a key component of the human development report for 25 years [
An emerging literature shows gender equality is also good for health. Dahlin and Harkonen [
Collectively, this literature indicates that gender equality at the institutional-level brings a range of health rewards to citizens, including more restful sleep. In this paper, we expand upon these literatures to understand how gender development at the country level structures sleep for a key group—the employed—paying careful attention to gender and managers who have the greater resources and demands.
Given our focus on the employed, we also examine the impact of economic development on employee’s sleep quality. Economic development could structure workers’ sleep in multiple ways. On the one hand, workers may sleep more restfully in countries with higher economic development as jobs and economic resources may be more bountiful and the economies may be more stable. On the other hand, living in a country with higher GDP may intensify strain and deteriorate sleep as pressures around economic performance may be greater. We directly test for these effects.
The current literature is still limited and inconclusive. Among the existing studies, many focused on the sleep impact of economic crisis. For example, Asgeirsdottir, Corman, Noonan, and Reichman [
Others focused on the link between macro-level economic conditions and individual-level sleep outcomes. A study of American adults [
Although these literatures are mixed, we expect aggregated economic growth (GDP) to be positively associated with workers’ sleep as economies are more stable and economic resources more bountiful. We extend the findings of Niekamp [
From these literatures, we draw three hypotheses.
H1: Mangers will report poorer sleep quality than those in non-managerial positions
H1a: Women managers will report poorer sleep quality than men managers.
H2: The gendered sleep gap for employees and managers will narrow in countries with greater gender development.
H3: Employees in countries with higher GDP will sleep better than those in countries with weaker GDP.
H3a: The positive sleep benefits of GDP for employees will be stronger for men than women and men than women managers.
This study combined individual-level data from the 2012 European Social Survey (ESS) and 2012 macro-level data from the United Nation’s Human Development Data (1990–2018). Both datasets are publicly available and our data were downloaded from their websites (ESS:
We selected those who are employed in their prime working age (25 to 64) and who were complete on reports of occupation group, work time and sleep. As imputing data does not account for the nested structure of multilevel models and thus violates the assumptions of multilevel models at the country level [
Our final analytical sample comprised 18,116 observations (8,949 female and 9,167 male respondents) across 29 countries: Albania, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Italy, Ireland, Israel, Kosovo, Lithuania, Netherlands, Norway, Poland, Portugal, the Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, and the United Kingdom.
Sleep quality was assessed by a single item that specifically asked about sleep. Respondents were asked how much of the time during the past week their sleep was restless. They answered on a four-point ordinal scale ranging from 1 (none or almost none of the time) to 4 (all or almost all of the time). This measure was chosen for several reasons. First, it is the only question specifically asking about sleep in ESS. More importantly, such a simple Likert-style rating of sleep quality is a relatively standard measure of sleep quality in the public health literature [
The responses were skewed: 48% and 39% of the respondents reported that they had restless sleep none of the time or sometimes, while 10% and 4% of the respondents reported that they had restless sleep often or all of the time. Consistent with previous research [
A country’s level of gender equality is measured by the United Nation’s gender development index (GDI). The GDI measures gender gaps in life expectancy, education, and incomes by using the same component indicators as in the human development index (HDI). The GDI is the ratio of the HDIs calculated separately for women and men. It is a direct measure of gender gap showing the women HDI as a percentage of the men GDI. In line with previous research [
The primary interest of this study was to explore whether those with greater workplace resources report more restful sleep. The ESS classified respondents’ occupations based on the International Standard Classification of Occupations 2008 (ISCO-08). We include a measure for whether the respondent is a
In occupational literature, the definition of managers is broad including supervisors and team leaders. ISCO-08 highlights the distinction between managers and supervisors: while supervisors are responsible only for supervision of the activities of other workers, managers have overall responsibility for the operations of a business or an organizational unit. Further, managerial occupations are organized along functional rather than industrial lines. This means that managers with specialist functions are identifiable, irrespective of the industry in which they work [
Respondents were asked to estimate the
We included a number of sociodemographic controls in our models shown to be associated with sleep [
To control for family factors that could affect sleep, we included a binary measure of whether the respondent lives with a
Since sleep is significantly influenced by health, we also included two key health-related items.
Due to the nested structure of the data, we analyzed restless sleep in multilevel regression models. Since we converted the dependent variable into a binary variable, we applied multilevel logit regression models. As a robustness test, we also estimated the models as ordinal logit regression and the results are substantively equivalent (results are presented in
Country | Restless sleep, women | Restless sleep, men | z-values | GDI index | Per capita GDP, 2011 PPP |
---|---|---|---|---|---|
Albania | 0.29 | 0.27 | -0.36 | 0.972 | 10370 |
Belgium | 0.19 | 0.15 | -1.29 |
0.970 | 41125 |
Bulgaria | 0.12 | 0.08 | -1.81 |
0.991 | 15772 |
Switzerland | 0.14 | 0.10 | -1.60 |
0.964 | 56150 |
Cyprus | 0.20 | 0.10 | -2.76 |
0.974 | 31750 |
Czechia | 0.17 | 0.14 | -0.91 | 0.980 | 28527 |
Germany | 0.21 | 0.15 | -2.38 |
0.963 | 42822 |
Denmark | 0.14 | 0.10 | -1.32 |
0.981 | 44337 |
Estonia | 0.12 | 0.13 | 0.73 | 1.023 | 25692 |
Spain | 0.14 | 0.10 | -1.74 |
0.979 | 31109 |
Finland | 0.09 | 0.07 | -1.43 |
1.005 | 39913 |
France | 0.26 | 0.20 | -2.16 |
0.990 | 37377 |
United Kingdom | 0.24 | 0.16 | -2.68 |
0.964 | 37094 |
Hungary | 0.27 | 0.23 | -1.07 | 0.990 | 22582 |
Ireland | 0.10 | 0.09 | -0.55 | 0.977 | 44829 |
Israel | 0.11 | 0.12 | 0.47 | 0.971 | 30645 |
Iceland | 0.15 | 0.10 | -1.10 | 0.979 | 41077 |
Italy | 0.14 | 0.12 | -0.39 | 0.972 | 35228 |
Lithuania | 0.08 | 0.05 | -1.07 | 1.036 | 24049 |
Netherlands | 0.14 | 0.11 | -1.32 |
0.967 | 45949 |
Norway | 0.09 | 0.07 | -1.35 |
0.997 | 63003 |
Poland | 0.18 | 0.11 | -2.48 |
1.009 | 23218 |
Portugal | 0.09 | 0.06 | -1.07 | 0.989 | 25806 |
Russian Federation | 0.16 | 0.09 | -2.60 |
1.026 | 25156 |
Sweden | 0.12 | 0.07 | -2.65 |
1.000 | 43356 |
Slovenia | 0.13 | 0.10 | -0.84 | 1.006 | 27977 |
Slovakia | 0.09 | 0.09 | 0.20 | 0.987 | 26218 |
Ukraine | 0.22 | 0.17 | -1.33 |
1.000 | 8322 |
Kosovo | 0.06 | 0.06 | -0.15 | NA | NA |
Correlation with GDI | -0.32 |
-0.33 |
-- | -- | -- |
Correlation with GDP | -0.31 | -0.35 |
-- | -0.34 |
-- |
Note:
*** p < .01,
** p < .05,
* p < .1. 1-tailed tests for gender differences in restless sleep.
The last two columns of
The bottom two rows of
Min | Max | Proportion/mean for women | Proportion/mean for men | z-values | t-values | Chi-square | |
---|---|---|---|---|---|---|---|
Having restless sleep | 0 | 1 | 0.15 | 0.12 | -7.38 |
-- | -- |
Key independent variables | |||||||
Being a manager | 0 | 1 | 0.06 | 0.11 | 10.62 |
-- | -- |
Daily work control | 0 | 10 | 6.69 | 6.96 | -- | -- | 55.75 |
Workplace policy control | 0 | 10 | 4.55 | 5.03 | -- | -- | 150.43 |
Total hours worked in the past week | 0 | 80 | 37.53 | 43.79 | -- | 35.74 |
-- |
Sociodemographic controls | |||||||
Age | |||||||
Between 25 and 34 | 0 | 1 | 0.22 | 0.23 | 2.78 |
-- | -- |
Between 35 and 44 (ref.) | 0 | 1 | 0.29 | 0.29 | -0.63 | -- | -- |
Between 45 and 54 | 0 | 1 | 0.30 | 0.28 | -1.75 |
-- | -- |
Between 55 and 64 | 0 | 1 | 0.20 | 0.20 | -0.20 | -- | -- |
Education | |||||||
College or above | 0 | 1 | 0.36 | 0.29 | -11.27 |
-- | -- |
Sub-degree or upper secondary (ref.) | 0 | 1 | 0.52 | 0.57 | 7.40 |
-- | -- |
Lower secondary or below | 0 | 1 | 0.12 | 0.14 | 4.74 |
-- | -- |
Household’s total net income (deciles) | 1 | 10 | 6.11 | 6.42 | -- | -- | 92.69 |
Living with partner | 0 | 1 | 0.68 | 0.75 | 10.71 |
-- | -- |
Presence of child under six | 0 | 1 | 0.14 | 0.17 | 5.81 |
-- | -- |
Presence of child between six and seventeen | 0 | 1 | 0.35 | 0.31 | -5.54 |
-- | -- |
Health and well-being | |||||||
Poor physical health | 1 | 5 | 2.05 | 1.95 | -- | -- | 87.97 |
Poor emotional health | 1 | 4 | 1.85 | 1.74 | -- | -- | 261.50 |
Note:
*** p < .01,
** p < .05,
* p < .1. 1-tailed tests.
Model A | Model B | Model C | Significant gender difference | ||||
---|---|---|---|---|---|---|---|
Women | Men | Women | Men | Women | Men | ||
N = 8,949 | N = 9,167 | N = 8,872 | N = 9,002 | N = 8,872 | N = 9,002 | ||
Key independent variables | |||||||
Being a manager | 0.30 |
-0.04 | 0.28 |
-0.07 | 0.25 |
-0.07 | A, B |
Total hours worked in the past week | -0.00 | 0.01 |
0.00 | 0.01 |
0.00 | 0.01 |
|
Daily work control | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | |
Workplace policy control | 0.01 | -0.02 | 0.007 | -0.02 | 0.01 | -0.02 | |
Sociodemographic controls | |||||||
Age: Between 25 and 34 | 0.19 |
0.01 | 0.20 |
-0.00 | 0.19 |
-0.01 | B |
Age: Between 45 and 54 | 0.04 | -0.03 | 0.05 | -0.04 | 0.05 | -0.04 | |
Age: Between 55 and 64 | 0.14 | -0.09 | 0.16 | -0.09 | 0.15 | -0.09 | |
Education: College or above | -0.34 |
-0.24 |
-0.34 |
-0.25 |
-0.33 |
-0.26 |
|
Education: Lower secondary or below | -0.06 | 0.18 | -0.09 | 0.15 | -0.09 | 0.15 | |
Household’s total net income | 0.00 | -0.01 | 0.004 | -0.00 | 0.00 | -0.00 | |
Living with partner | 0.05 | 0.20 |
0.05 | 0.20 |
0.05 | 0.20 |
|
Presence of child under six | 0.37 |
0.31 |
0.37 |
0.33 |
0.37 |
0.33 |
|
Presence of child between six and seventeen | -0.18 |
-0.06 | -0.17 |
-0.05 | -0.17 |
-0.05 | |
Health and well-being | |||||||
Poor physical health | 0.40 |
0.44 |
0.41 |
0.45 |
0.41 |
0.45 |
|
Poor emotional health | 1.73 |
1.72 |
1.72 |
1.72 |
1.72 |
1.72 |
|
Country equation, intercept | |||||||
General intercept | -6.32 |
-6.81 |
-6.31 |
-6.82 |
-6.30 |
-6.81 |
A, |
GDI | -- | -- | -0.14 |
-0.16 |
-0.13 |
-0.16 |
|
Logged per capita GDP | -- | -- | 0.007 | -0.07 | 0.03 | 0.02 | |
Variance component intercept | 0.02 |
0.02 |
0.01 |
0.01 |
0.01 |
0.01 |
|
Cross-level interactions | |||||||
Managers |
-- | -- | -- | -- | -0.09 |
-0.01 | |
Managers |
-- | -- | -- | -- | -0.35 | -0.73 |
Note:
*** p < .01,
** p < .05,
* p < .1. Sample size for Models B and C differ from Model A because there’s no GDI and GDP data for Kosovo in the United Nation’s database. Country predictors are centered on their grand means. Standard errors clustered at the country level. In the last column, bolded letters represent statistical gender significance at the 5% level while normal letters represent statistical gender difference at the 10% level.
We found evidence that being a manager had a strong positive association with women’s restless sleep. In Models A and B, the coefficients on managers for women were 0.30 and 0.28 respectively and both coefficients were statistically significant. In contrast, the coefficients for men were negative, smaller in magnitude and not statistically significant. Our gender interactions models suggest these gender differences were statistically significant lending support to our gendered
Model B tests our gender empowerment and economic development hypotheses (H2 and H3) for the entire sample. The results show that both men and women workers sleep better in countries with greater gender development, showing our gender empowerment hypothesis extends to men workers as well (H2). GDP, however, is not significant at the intercept, thus failing to support our economic development hypothesis (H3) for women and men workers.
Turning to other individual characteristics, we find men’s sleep seems to be more influenced by work hours than that of women. In both Models A and B, the coefficients on work time were statistically significant only for men and the gender difference was statistically significant. This can perhaps be attributed to the fact that men on average worked longer and thus experience a greater sleep disruption (6 hours/week longer than women, see
We did not find evidence for stronger effects of the family-related variables on women’s sleep. In separate models, men reported more frequent restless sleep when living with a partner (b = 0.20, p < .05) while women tended to have better sleep with the presence of children aged 6 and 17 (b = -0.18, p < .1 in Model A and b = -0.17, p < .1 in Model B) compared to those without children. In addition, the sleep of both men and women was more restless with the presence of children aged under six. This finding differs from previous research [
The results of Model B affirmed that women and men alike slept better when they lived in a more gender-equal society. We further investigated this relationship by hypothesizing that the country-level predictors may condition the effects of being a manager in predicting restless sleep (H2 and H3a; Model C). The results are also shown in
Most results of Model C are similar to those of Models A and B. However, due to the inclusion of cross-level interactions, the interpretation of the coefficient on being a manager and country-level predictors changed. For women, the interaction between being a manager and GDI was negative and statistically significant, meaning that women managers reported more restful sleep in countries with higher GDI. For men, the interaction between being a manager and per capita GDP was negative and statistically significant, meaning that men managers were more likely to have better sleep in more economically developed countries.
To better interpret these results, the predicted probability of sleeping restlessly for managers and non-managers was estimated across the range of country-level predictors observed across Europe. To do this, we first calculated margins from predictions of Model C, at fixed centered values for minimum, average, and maximum values of GDI (minimum for Germany = -2.53 and maximum for Lithuania = 4.76). Then we estimated margins at fixed centered values for minimum, average, and maximum values of logged per capita GDP (minimum for Ukraine = -1.37 and maximum for Norway = 0.65). After calculating the predicted probabilities, we plotted the results in Figs
Figs
The interaction between being a manager and per capita GDP is visualized in
As a robustness check, we also tested whether the interaction of GDP and GDI structured sleep. The results were not significant, but we are wary of the impact of reduced statistical power through reduced degrees of freedom. Thus, we stress the importance of investigating these associations in future large surveys to provide clearer guidance on their interrelationship.
The broad consensus in the health sociology literature is that higher social status is associated with better mental health outcomes through access to greater work resources [
We investigated the relationship between sleep, managerial status and country-level economic and gender development. Our study found evidence corroborating the stress of higher status theory: compared to non-managers, managers were more likely to report restless sleep across Europe. However, we emphasize that this link between the managerial position and restless sleep is significantly structured by gender and economic development in ways that are distinctly gendered. Women managers report more restful sleep in countries with higher gender development. By contrast, men managers report more restful sleep in countries with stronger economies, or higher GDP. Collectively, our results indicate men managers experience a sleep premium from economic development and women managers from gender empowerment.
While we find good support for several of our hypothesis, there are a number of notable limitations to the study. Importantly, the cross-sectional design of ESS does not allow us to make causal claims. Although ESS conducts survey every two years, it selects new sample members each round. We see that economic development and gender empowerment play an essential role in shaping the sleep quality of working people but how this shift influences within-person change remains unclear. However, as no longitudinal panel on these topics exists for the population we studied, a cross-sectional investigation still makes a contribution by unveiling important patterns.
The use of a one item measure of sleep quality is also a limitation. Further, since the questionnaires were translated into different languages across countries, the wording of the item (sleep was restless) and interpretation may vary. However, these limitations are inevitable in large-scale cross-country surveys like the European Social Survey which employs advanced survey techniques to ensure validity across countries. Other measures of sleep may also be important, for example time spent in sleep, which is likely associated with time spent in work and managers often spend longer hours in work [
Our results present clear policy implications: economic growth alone is not enough—its sleep benefit is rather limited to men managers. By contrast, higher levels of gender development reduce the likelihood of having restless sleep, among men and women alike. In particular, a higher level of gender equality could improve women managers’ sleep, thereby reducing the stress of higher status experienced by women workers. In this way, our research adds to a previous study [
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