Quality Trumps Quantity: Exploring Relationships between Job Quality, Job Quantity, and Sleep

DOI: https://doi.org/10.21203/rs.3.rs-2778860/v1

Abstract

Background

Despite ample research on the relationship between work and sleep, little is known about the relative importance of each job quality dimension for sleep quality and whether the relationship in contingent on job quantity (i.e., working hours). Drawing on a unified analytic framework of job quality and job quantity, this study aims to investigate the interactive relationship between job quality and job quantity and their impact on sleep quality.

Method

Our analytical sample comprises of 11,066 men and 13,416 women, between the ages of 18 and 65, extracted from the 2015 European Working Conditions Survey. Cases with missing key values are removed from the sample to ensure data integrity. We apply country-level fixed effects models to examine the association between job quantity, job quality, and sleep quality.

Results

Our findings suggest that while working hours or working time mismatch have a weak association with sleep quality, job quality has a more significant impact on sleep quality, with different dimensions playing varying roles. Most favorable job characteristics (e.g., low work intensity, good physical environment, meaningful work) are linked to better sleep quality. In contrast, high skill and discretion is associated with poorer sleep quality. Furthermore, the importance of most job quality indices remains even when people work shorter hours or feel under-employed, highlighting the continued importance of job quality for wellbeing in the global trend of a shorter working week.

Conclusion

By adopting multidimensional concepts of job quantity and job quality, the findings have practical implications for labor market policies and the creation of a supportive workplace environment that fosters better sleep quality and mental health for employees. This may lead to more effective policies to address work-related stress and promote a favorable work-life balance.

Introduction

In recent years, the rise of neoliberal economic policies and technological advancements has not only led to significant labor market changes such as increasing job polarization and instability, but also stimulated extensive discussions about their implications for employees’ health and wellbeing (Autor et al., 2006; Fernández-Macías, 2012). As a critical component of well-being (Reid et al., 2006), sleep quality has gained increasing attention in the trend of labor market changes. Prior research has shown that adequate and high-quality sleep is not only necessary for optimal daily functioning and overall health (Henry et al., 2013; Watson et al., 2015), but also is closely related to employees’ work engagement and productivity (Swanson et al., 2011). 

In previous literature, ample research has focused on the negative impact of long working hours on sleep, while another stream of research has investigated the relationships between specific job quality indicators (e.g., job demands, job control, workload, and work-family conflict) and sleep quality (Linton et al., 2015; Magnavita & Garbarino, 2017; Van Laethem et al., 2013). However, there are important limitations of the previous research. First, while job quantity and job quality are important determinants of sleep, both streams of research have been largely running in separate tracks without engaging in a direct dialogue. Second, in the previous literature job quantity and job quality were often measured by a limited number of indicators, ignoring their multidimensionality. For example, while job quantity should include both objective (working time) and subjective (working time preference match) dimensions, job quality can also be measured from multiple disciplinary perspectives including economics, sociology, psychology etc. Third, previous research implicitly assumes that the effects of job quantity and job quality are independent from each other, overlooking their interactive effects on sleep. This could hinder a comprehensive understanding of the relationships between job quality, job quantity and sleep, thereby impeding the development of effective labor market policies. 

To address the research gaps, the present study aims to accomplish three objectives by adopting the sixth wave of the European Working Conditions Survey (2015). Firstly, the study aims to employ a unified theoretical framework of job quantity and job quality and examine the relative importance of both for sleep quality. Secondly, drawing on literature from various social science perspectives, this study aims to use a wide range of indicators to measure job quantity (including subjective and objective working time) and job quality (including eight job quality indicators from multidisciplinary theoretical perspectives). In doing this, we are able to compare the relative significance and impact of each job characteristic on sleep quality. Thirdly, the study seeks to understand the potential interplay between job quality and job quantity in shaping sleep quality. This will contribute to the discussion about the role of job quality in light of the trend of shorter working hours across the developed countries.

By accomplishing its three objectives, this study has significant theoretical and practical implications in three ways. Firstly, it contributes to the existing literature in the sociology of work by adopting multidimensional concepts of job quantity and job quality and providing a comprehensive understanding of how various job characteristics influence sleep quality. Second, this study offers a comprehensive investigation of the interaction between job quality, job quantity, and sleep quality. This deepens our understanding of the relative importance of job quantity and job quality, especially in the context of shorter working week across Europe. The findings shed light on further policy directions by providing a more holistic analytic framework of work and health. Third, the study’s findings have practical implications for labor market policies and the creation of a supportive workplace environment that fosters better sleep quality and mental health for employees. This may lead to more effective policies to address work-related stress and promote a favorable work-life balance.

Literature

Effects of job quantity on sleep quality

The latent deprivation model emphasizes the significance of employment in shaping an individual’s overall well-being, as it provides various latent functions such as time structure, purposefulness, participation, and shared experiences (Jahoda, 1982). Job quantity research builds on this model and proposes two directions to explore the relationship between employment and sleep quality/mental health. The first research direction focuses on the association between overemployment and poor sleep quality. Studies have demonstrated that long working hours increase the risk of sleep disturbances and inadequate sleep (Bannai & Tamakoshi, 2014; Barnes & Drake, 2015). Understanding the impact of job quantity on sleep quality is essential for developing effective labor policies and promoting employee well-being.

In contrast, another research strand aims to investigate the impact of underemployment on employees’ sleep quality/mental health. Studies have suggested that involuntary part-time work can have negative effects on the mental health of employees (Angrave & Charlwood, 2015; Heyes et al., 2017; Kamerāde & Richardson, 2018). This is because working fewer hours than desired may prevent employees from acquiring various financial and social benefits associated with employment, such as social status, identity, and interaction, which can lead to mental health problems. Additionally, underemployment may result in poverty, which has been associated with poor sleep quality. Previous research has demonstrated that individuals with low income and low socioeconomic status are more likely to experience poor sleep quality (Hale & Do, 2007).

Moreover, previous studies often adopt arbitrary standards to categorize work hours without considering the match and mismatch between actual and preferred work hours. This oversimplification of the relationship between job quantity and sleep quality limits the validity of previous findings. Thus, to address these gaps in the literature, the present study incorporates measures of work hours and work hour match simultaneously to provide a more comprehensive understanding of the relationship between job quantity and sleep quality. Moreover, the study also employs both subjective and objective measurements of job quantity to further investigate the interaction between job quantity, job quality, and sleep quality.

Effects of job quality on sleep quality

The emerging field of job quality has recently developed multidimensional indices based on a literature foundation from various social science disciplines. The present study employs a unified analytic framework of eight job quality dimensions, namely earnings, skill and discretion, physical environment, social environment, prospects, work intensity, working time quality, and job meaningfulness. Table A1 (in the appendix) summarizes the important dimensions of job quality related to mental health, and their theoretical backgrounds. Since sleep quality is a critical predictor of mental health, this study seeks to analyze the impact of the eight aforementioned dimensions of job quality on sleep quality drawing on relevant literature in the fields of job quality and mental health.

Earnings 

The economic approach emphasizes earnings as a critical aspect of job quality, affecting employees’ sleep quality for two reasons. Firstly, income levels have been found to be significantly correlated with sleep duration, with lower income levels associated with both shorter and longer sleep duration. Additionally, studies have shown that people with low socioeconomic status, such as the black minority group in the US, tend to have poorer sleep quality. Secondly, financial strain and earning deprivation can negatively impact people’s ability to control their lives and undermine their mental health (Fryer, 1986). Empirical evidence suggests that lower income, financial strain, and a lack of material possessions are associated with decreased mental health, as indicated by common mental disorders (Reading & Reynolds, 2001). Moreover, as sleep quality is a component of mental health, it is believed that earnings significantly affect sleep quality. Given the widely recognized importance of earnings for sleep quality, it is crucial to include earnings into the job quality framework and compare its relative significance.

Skill and discretion

The traditional sociological perspective emphasizes the importance of skill use and autonomy, which is one critical dimension of job quality. However, the relationship between skill and discretion and sleep quality is mixed. Some studies suggest that individuals who engage in regular intense physical training or practice music for extended periods may experience better sleep quality (Driver & Taylor, 2000; Lauderdale et al., 2008), and autonomy is believed to promote sleep quality (Knudsen et al., 2007). In contrast, individuals who engage in high levels of cognitive activity or creative work may experience poorer sleep quality due to work demands and work-family multitasking (Schieman & Young, 2010). The demands of creative work may offset the hypothesized resource benefits of creative work for work-family conflict, leading to a negative impact on mental health. Therefore, this study will revisit the relationship between skill and discretion and sleep quality based on the mixed results of previous studies.

Physical environment

The occupational medicine perspective highlights that the physical work environment is an important dimension of job quality. Environmental health science research has shown that the physical work environment can contribute to the deterioration of mental health (Briner, 2000; Smith, 1991). In addition, prolonged exposure to noise has been identified as a significant cause of hearing loss, which can negatively impact mental health (Basner et al., 2014; Fellinger et al., 2012). As sleep quality is a critical measure of mental health, it is reasonable to involve the physical environment as one job quality index and evaluate its importance with other indicators. 

Social environment

The radical and behavioral approach emphasizes industrial democracy, participation, and work organizations, and hence the social environment is another important dimension of job quality for employees’ sleep quality. Studies have consistently shown that social support and positive workplace relationships are associated with better sleep quality, which may be due to the positive emotional and physiological effects of social support (Ong et al., 2006; Troxel et al., 2010). In contrast, social isolation and negative stressors in the workplace can lead to poorer sleep quality and related health problems (Cacioppo et al., 2002). Therefore, the social environment should be included into the job quality framework when assessing the effects on sleep quality and its comparative significance can be indicated in this study.

Prospects

The institutional approach argues that contract status, employment stability, and career prospects are critical indicators of job quality. From the perspective of occupational medicine, job security is a significant source of stress that can have severe impacts on employees’ sleep quality (T. J. Kim & von dem Knesebeck, 2016; Mai et al., 2019). In addition, career development is a key component of an individual’s mental health and well-being. One study found that individuals who perceived that they had control over their careers experienced lower levels of anxiety and depression (Miller & Rottinghaus, 2014). Since sleep quality is a critical component of mental health, it is sensible to include prospects when predicting job quality’s impacts on sleep quality.

Work intensity

The work-life balance approach highlights work intensity as one critical dimension of job quality that is assumed to be associated with employees’ sleep quality. Studies have consistently shown that work intensity and job demands have a negative impact on employees’ sleep quality, where low job control and a lack of social support further exacerbate this relationship (Berset et al., 2011; Knudsen et al., 2007). Work-family conflicts and role conflicts also contribute to poor sleep quality, highlighting the importance of balancing work and family responsibilities (Burgard & Ailshire, 2009; Williams et al., 2006). Therefore, this study will consider work intensity as one crucial dimension of job quality and compare its significance with other indicators.

Working time quality

Working time quality is another important dimension of job quality based on the work-life balance approach. Working time quality, including shift work and non-standard work schedules, has been shown to significantly affect workers’ sleep quality (J. Kim et al., 2020). Several studies have demonstrated that shift work and night-time work are associated with shorter sleep duration, more sleep disturbances, insomnia, excessive sleepiness, and other sleep disorders (Drake et al., 2004). These effects are particularly prominent in healthcare workers, police officers, firefighters, and other professionals who work in emergency response systems and high-stress environments (Cropley et al., 2006; Garbarino et al., 2019). Acute job demands and job stress are also linked to poor sleep quality in these groups. Thus, working time quality is an important component of the present study’s framework of job quality, whose relative importance can also be assessed. 

Meaningfulness

The classical sociology perspective emphasizes job meaningfulness as another important dimension of job quality indices, which may affect employees’ mental health and well-being. For example, Bakker et al. (2014) showed that a sense of fulfilment and work engagement, as a form of job resource, predicted better mental health. In addition, another qualitative study also demonstrated that work meaningfulness was associated with improved well-being and strengthened self-identification (Leufstadius et al., 2009). Since sleep quality is a critical component of mental health, work meaningfulness should be an important dimension of job quality and its relative significance is of great interest.

Interaction effects between job quantity and job quality on sleep quality

The impact of job quantity and job quality on employees’ sleep quality is a crucial area of investigation, given its implications for employee well-being and productivity. To gain a better understanding of this relationship, researchers have drawn upon the Vitamin Model (1987), which provides a comprehensive framework for examining the influence of job characteristics on employee well-being. The Vitamin Model identifies twelve job characteristics that affect employees’ satisfaction and well-being, including personal control, interpersonal contact, externally generated goals, skill use opportunities, variety, social position, and supportive supervision. Among these characteristics, interpersonal contact is particularly relevant to the impact of job quantity and job quality on sleep quality. Previous research has demonstrated that positive interpersonal relationships at work can enhance sleep quality and well-being, while conflict-ridden relationships have adverse effects. Moreover, job demands and job resources also play a role in sleep quality. For instance, high workload can have a negative impact on sleep quality, while social support from colleagues and supervisors can have a positive effect.

The concept of workplace “vitamins” has been introduced to describe job characteristics that are essential for maintaining good sleep quality and their optimal dosages. Similar to vitamins in the human body, the right amount of each workplace vitamin is crucial, as an excess or deficiency of any particular vitamin can have negative effects. While previous research has focused on job quality or job quantity separately, the interplay between the two has been relatively unexplored. According to Warr’s vitamin model, varying dosages of job quality can affect an individual’s sleep quality, and the combination of job quality and job quantity may also exert effects. For example, having a high-quality job with a low workload may positively impact sleep quality, while a low-quality job with a high workload may have a negative impact. However, a job with high job quality and a moderate workload may be optimal for good sleep quality.

Despite the importance of this issue, research on the relationship between job quantity and job quality has been limited. Although most studies have focused on underemployment and zero-hours contracts, some research suggests that the correlation between working hours and job quality dimensions is not straightforward or linear, indicating that their interaction can result in diverse outcomes. For example, shorter working hours can reduce work intensity when employees have control over their schedules, while employer-led shorter working hours are often associated with higher work intensity (Piasna, 2018). Given the mixed results of previous studies, the primary aim of this study is to explore how the “dosage” of job quality interacting with job quantity affects sleep quality.

Research gaps and questions

There has been a lack of communication between research on job quantity and job quality, with both areas largely running in separate tracks without engaging in direct dialogue. This disconnect is unexpected given that both areas are concerned with employees’ sleep quality, and the failure to integrate these areas could result in misguided policy recommendations. Therefore, it is crucial to integrate both job quantity and job quality, as well as their combined effects on employees’ sleep quality, into academic studies and policy implications.

The present study aims to fill these research gaps and contribute to the literature in three main ways. Firstly, while prior research has examined the relationship between job characteristics and sleep quality, few studies have simultaneously examined the impact of job quantity and job quality on sleep quality. In other words, limited studies have explored the relative importance of job quantity and job quality when more emphasis is placed on long working hours. Therefore, this study employs a unified analytical framework of job quantity and job quality with exhaustive indicators to investigate their relative importance. This study sheds lights on the multidimensionality and significance of job quality, and investigates how good and bad jobs differ in their impact on sleep quality. 

Secondly, prior research has often measured job quantity and job quality by a limited number of indicators, ignoring their multidimensionality and comprehensiveness. Furthermore, the relative importance and impact of each job characteristic remain questionable. Therefore, this study revisits the influence of job quantity on sleep quality by including objective (working time) and subjective (working time preference match) measurements at the same time, rather than adopting an arbitrary standard of over- and under-employment. Moreover, the present study employs eight indices to measure job quality based on multiple disciplinary literature. Understanding the relative importance of each job dimension could help employees improve their sleep quality more strategically and effectively. 

Thirdly, this study aims to examine the interplay between job quantity and job quality and their impact on sleep. Since job quality is inevitably associated with working time, it is crucial to investigate the joint effects of job quality and job quantity on sleep. The degree of job quality may also determine or moderate the effects of job quantity on sleep. However, previous research has paid little attention to the interaction effects of job quality and job quantity on sleep quality. Understanding the interaction between job quality and job quantity on sleep quality can provide a more accurate assessment of the impact of different job quality indicators, independent of job quantity and other confounding factors. Therefore, this study fills a critical gap in the literature by examining the joint effects of job quality and job quantity on sleep quality and further highlighting the importance of job quality.

Therefore, the aim of this study is to investigate the independent and interactive effects of job quality and job quantity on employees’ sleep quality. The study aims to answer two primary questions:

  1. What is the significance of job quality indicators and job quantity, including work hours and the match between actual and preferred working hours for the sleep quality of employees?
  2. To what extent does the impact of job quality depend on job quantity? Specifically, does the effect of job quality decrease for employees with short, long, or unmatched working hours? 

Methods

Data and sample

The European Working Conditions Survey (EWCS) conducted by Eurofound provides extensive information on work in various countries, sectors, occupations, and age groups. This study employs data from the sixth wave (2015) of EWCS*, which surveyed approximately 44,000 workers across 35 countries. The questionnaire covered various aspects of employment. The survey employed a multi-stage, stratified clustered sampling technique that randomly selected primary sampling units in each country based on the probability proportional to size principle and then sampled households in each primary sampling unit (Eurofound, 2017). Moreover, we apply cross-sectional weights to ensure representativeness in the analyses.

In this study, the sample selection focuses on employees and excludes respondents who are self-employed, as their employment circumstances are primarily determined by themselves. Additionally, employees between the ages of 18 and 65 are included, while those who reported working more than 48 hours per week are excluded, as they tend to work in atypical occupations and their actual working hours are difficult to define. After removing cases with missing key values (list wise deletion), the analytical sample consisted of 11,066 men and 13,416 women, with a missing data percentage of approximately 16%. Further analyses using multiple imputation method to deal with missing data are reported in the sensitivity analyses.

Variables

Dependent variable

The current study investigates sleep quality as the dependent variable, which is evaluated by three questions in the survey. Specifically, respondents were asked to report their sleep-related difficulties, including “difficulty falling asleep,” “waking up repeatedly during sleep,” and “waking up with a feeling of exhaustion and fatigue.” The responses were assessed using a 5-point Likert scale, ranging from 1 (daily) to 5 (never). To develop a composite measure of sleep quality, this study employs principal component analysis, which generates a single factor with a Cronbach’s alpha coefficient of 0.80. A higher factor score indicates better sleep quality and fewer sleep-related difficulties. The factor score is standardized and multiplied by 100, resulting in a score range of 0 to 100.

Independent variables

The present study employs two independent dimensions to evaluate job quantity and job quality. To measure job quantity, the study adopts three variables. The first variable measures the actual working hours per week including overtime work and second jobs, and is divided into four categories: “1-16,” “17-34,” “35-40,” and “41-48.” The first two categories represent shorter working hours, the third category represents full-time working hours, and the last category indicates overtime work. The second variable is a work hour match measure constructed based on respondents’ answers regarding whether their actual working hours match their preferred working hours. This measure consists of three categories: “over-employed”, “matched”, and “under-employed”. Over-employed refers to actual working hours being longer than employees’ preferences, matched means that working hours are consistent with their expectations, and under-employed means that working hours are shorter than employees’ expectations. The third variable is created by combining the first two variables, resulting in six subcategories: “full-time over-employed,” “full-time matched,” “full-time under-employed,” “part-time over-employed,” “part-time matched,” and “part-time under-employed.”

To evaluate job quality, eight distinct job quality indices are utilized. This operational framework was originally proposed by Green et al. (2013) and has since been applied in research by other scholars (Felstead et al., 2019). The eight indicators are earnings, skills and discretion, physical environment, social environment, work intensity, prospects, working time quality, and the meaningful work index. The first seven indices are constructed using the EWCS (Eurofound, 2017), and the meaningful work index is developed by Wang et al. (2022). Table A2 in the appendix provides detailed information on the specific components of each index.

Control variables

The present study includes demographic and household characteristics, as well as engagement in non-work activities, as control variables in the analyses. The demographic and household characteristics controlled for in this study include age, age squared, gender, presence of a partner in the household, presence and age of children, ethnicity, and educational attainment. Non-work activities, specifically participation in caring for and educating children, cooking and doing housework, and caring for the elderly/disabled, are also included as control variables. Additionally, participation in social leisure activities and activity-restricting illness are included as control variables. The results of robustness checks indicate that there is no issue of multicollinearity among all independent and control variables (VIF<2). 

Table A3 (in the appendix) presents the descriptive statistics of key variables. The average score for sleep quality is 74.28. More than half of the respondents report having full-time jobs, and over 60% of respondents indicate that their actual work hours and preferences were matched. Regarding the average job quality indicators, the top three dimensions with the highest scores are physical environment (83.71), meaningful work (79.98), and social environment (77.73).

Method

Considering that the data are hierarchically collected and structured and individuals within a country share certain commonalities, the present study uses country-level fixed effects models. Sleep qualityij is the dependent variable measuring the sleep quality of individual i in country j. Job quantityij and Job qualityij are primary predictors measuring the job quantity and job quality of individual i in country j. Covariatesij are control variables, which can vary at both individual and country level. cis the country level error term and µij is the individual level error term. The analyses concentrate on within-country variations and highlight individual characteristics by assuring that Cov (Xij, µij) = 0. 

Sleep qualityij= β1Job quantityij + β2Job qualityij + β3Covariatesij + c+ µij

In practice, the present study first investigates the relationship between job quantity and sleep quality by adding a series of demographic characteristics. Then, the study adds the eight job quality indices to examine the relative significance of job quantity and job quality and further investigate the most powerful job quality indices. In addition, the study examines the interaction terms between job quality and job quantity to explore group variation in sleep quality.

Results

Main findings

Table 1 presents the findings of multivariate country-level fixed effects models examining the associations between job quantity, job quality, and sleep quality. Model 1 indicates that individuals who work long hours have lower sleep quality than those who work full-time, while those who work short hours have similar levels of sleep quality. Furthermore, the findings reveal that employees whose work hour preferences do not match their actual work hours experience poorer sleep quality than those whose preferences and work hours align. Model 2 replicates Model 1 and includes a variable combining work hours and work hour match, indicating that employees who perceive themselves as over-employed or under-employed have consistently lower sleep quality than those who work full-time and perceive their work hours to align with their preferences, regardless of whether they work full-time or part-time. The detailed outcomes of the control variables are available in Table A4 in the appendix.

[Insert Table 1 Here]

Model 3 extends the analysis by incorporating eight job quality indicators as continuous variables, demonstrating that all job quality dimensions are significantly associated with sleep quality. A joint significance test for all job quality indicators suggests that overall job quality is highly associated with employees’ sleep quality (p<0.001), resulting in a substantial increase in R2 from 16% in Model 1 to 25% in Model 3. Therefore, job quality seems to be a crucial factor in determining variations in employees’ sleep quality. Moreover, the adverse effects of long working hours, overemployment, and underemployment are reduced in magnitude and degree of significance after incorporating job quality indicators. This implies that the impacts of long working hours and unmatched work hours on sleep quality may partly be attributed to employees’ poor job quality. Specifically, most job quality indices positively influence sleep quality, except for the skill and discretion index, which may be due to the intense workload and work demands for jobs with professional and strict requirements.  

To compare the relative weights of various job quality indices, a series of Wald tests are conducted. The results show that the work intensity index has the highest coefficient among all job quality indicators, and this effect is statistically significant (p<0.05). The physical environment index (p<0.01) and job meaningfulness index (p<0.05) also have significant effects on sleep quality, ranking second and third in terms of their relative weights. Model 4 replicates the findings of Model 3 using a combined variable of both work hours and work hour match, and the results are generally consistent. The negative effects of unmatched categories are attenuated or disappeared after considering job quality indicators, providing further support for the importance of job quality in explaining employees’ sleep quality.

[Insert Table 2 Here]

Table 2 presents the results of interaction effects between work hour match and sleep quality. The findings indicate that the impact of the eight job quality indices is consistent across different work hour match groups, except for the earnings index and prospects index, which are not significant for over-employed workers. Regarding the interplay between work hour match and job quality, most of the interaction terms are insignificant, suggesting that the impact of job quality on sleep quality is independent of work hour matches or job quantity. Figure 1 utilizes the predicted coefficients to illustrate the interaction effects.

[Insert Figure 1 Here]

Sensitivity analysis

The study conducts several robustness checks to confirm the results. Firstly, multiple imputation is employed to check the results. In the main analyses, cases with missing key values are removed using listwise deletion, which constitute approximately 16% of the sample. Little’s MCAR test indicates that the missing values are not missing completely at random, indicating potential bias. To address this issue, multiple imputations are operated using chain equations to create 20 datasets with imputed missing values. The results are presented in Table A5 in the appendix. The main findings remain consistent after the multiple imputation process, suggesting that the main findings are not greatly influenced by the missing data. Therefore, the results are robust to alternative model specifications.

Secondly, actual work hours are employed for job quantity. In the main analyses, work hour match, as a subjective measurement, is used. To further validate the results, actual working hours of employees are included as an objective measure. The findings present consistent patterns for the interaction between actual work hours and job quality, except for the significance of earnings index for employees with long work hours and the significance of working time quality index for workers with short work hours. These results are reported in Table A6 and Figure A1 in the appendix. Additionally, high prospects scores tend to lower the sleep quality of employees with long work hours compared to those with standard work hours, and the interaction shows a similar pattern on the meaningful index. Taken together, the results are robust to alternative variable specifications.

Thirdly, patterns over gender are checked. Women are assumed to undertake primary family responsibilities and are more likely to experience work-family conflict. Some studies have also found gender differences in job quality (Stier & Yaish, 2014).Therefore, the present study checks the results for males and females respectively. The results reveal nuanced heterogeneity over gender. Almost all job quality indicators (except earnings) are associated with sleep quality for both male and female employees, as reported in Table A7 in the appendix. In addition, career prospects only affect females’ sleep quality, while the working time quality index only affects males. Furthermore, the adverse effects of skills and discretion are stronger on males’ sleep quality. In sum, the results are robust with nuanced gender differences.

Fourthly, this study explores the variation across seven regions in Europe. Although the seven regions share many similarities in culture, there are still variations in the labor market and social environment. Table A8 in the appendix shows that the effects of job quantity and job quality are consistent across different regions, with no discernible impact. Job quality has a universal positive impact on sleep quality across different regions on physical environment, social environment, work intensity, working time quality, and meaningfulness. Some subtle differences are noted. Earnings is not significant in Nordic, South, East North, and East South, skill and discretion is not significant in Nordic and East Central, and prospects is not significant in North Western, South, and East North. Thus, the results are robust with nuanced heterogeneity across different regions of Europe.

Discussion And Conclusions

This study aims to contribute to ongoing discussions surrounding job quality and shorter working hours by examining the relative importance of job quality and job quantity on employees’ sleep quality and the extent to which the impact of job quality is dependent on individual work hours and work hour match.

The first key finding of the study is that job quality has a greater influence on employees’ sleep quality than job quantity. Although previous studies have examined the effects of job quality on sleep quality, few have employed multidimensional job quality indices. The study fills the gap by using the unified framework of EWCS and reveals that all job quality dimensions are significantly associated with sleep quality. In addition, the effects of job quantity on sleep quality can be partly explained by poor job quality. These findings are consistent with a previous study that examined the relationship between job quality and mental health using the same data (Wang et al., 2022). This study has significant contributions to the sociology of work literature by emphasizing the significance of job quality to employees' sleep quality. Furthermore, the study emphasizes the need for policy-makers to prioritize job quality in shorter workweek policies to achieve the benefits of working less for individuals, families, and communities.

Second, the study found that the importance of each job quality dimension was not even, with physical environment, job meaningfulness, and work intensity being the three strongest predictors. Moreover, while most favorable job characteristics (e.g., good pay, good physical/social environment, decent career prospects, low work intensity, high working time quality, and meaningful work) are associated with better sleep quality, high skill and discretion is correlated with poor sleep quality. The intense demands of high-requirement  work may counteract the benefits of professional training or work, resulting in deteriorated sleep quality. This study provides important theoretical and policy implications for discussions about the relationship between job quality, and employees’ sleep quality. The findings highlight the importance of improving job quality indicators such as promoting the physical environment, doing meaningful and useful work, and lowering work intensity. 

Third, this study indicates that job quality affects sleep quality regardless of work hours by examining the interaction between job quality and job quantity. The impacts of job quantity are demonstrated by both subjective and objective dimensions, overcoming the disadvantage of adopting an arbitrary standard of work hours. In other words, the effects of job quality are consistent across different job quantity categories. The findings provide essential policy implications to emphasize and improve job quality when shorter working hours tend to spread across Europe. To improve employees’ sleep quality and well-being, policymakers should pay more attention to job quality instead of solely focusing on declining work hours. 

However, the study has limitations as a cross-sectional survey and does not allow for the exploration of long-term and dynamic effects of job quality on sleep quality, although it is already the only available data covering all dimensions of job quality and job quantity as far as we know. Future research could investigate the causal effects of job quantity and job quality on sleep quality using appropriate longitudinal data. Additionally, policy-makers may need to consider creating more high-quality, short-hours jobs, while considering employees’ work hour preferences, as an effective approach to prevent unemployment and safeguard the sleep quality and mental health of the population.
 

Declarations

Acknowledgements

Not applicable.

Authors’ contributions

Y.G. designed the research framework, analyzed and interpreted the data, and wrote the manuscript. S.W. contributed to the design of the research framework, analysis and interpretation of data, critical review of the content, and has participated in the approval of the final version of the manuscript. All authors reviewed the manuscript. The author(s) read and approved the final manuscript.

Funding

The authors declare that the research group is not currently funded.

Data Availability

Data are available on the Ministry of Health website, and can be accessed through the link below: https://www.eurofound.europa.eu/surveys/about-eurofound-surveys/data-availability#datasets

Declarations

Ethical approval and Consent

This research does not contain experiments with human participants or animals.

Consent for publication

Not applicable.

Competing Interest

The authors declare they had no financial or non-financial competing interests during this study.

References

  1. Angrave, D., & Charlwood, A. (2015). What is the relationship between long working hours, over-employment, under-employment and the subjective well-being of workers? Longitudinal evidence from the UK. Human Relations, 68(9), 1491–1515. https://doi.org/10.1177/0018726714559752
  2. Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. I. (2014). Burnout and Work Engagement: The JD–R Approach. Annual Review of Organizational Psychology and Organizational Behavior, 1(1), 389–411. https://doi.org/10.1146/annurev-orgpsych-031413-091235
  3. Bannai, A., & Tamakoshi, A. (2014). The association between long working hours and health: A systematic review of epidemiological evidence. Scandinavian Journal of Work, Environment & Health, 40(1), 5–18. https://doi.org/10.5271/sjweh.3388
  4. Barnes, C. M., & Drake, C. L. (2015). Prioritizing Sleep Health: Public Health Policy Recommendations. Perspectives on Psychological Science, 10(6), 733–737. https://doi.org/10.1177/1745691615598509
  5. Basner, M., Babisch, W., Davis, A., Brink, M., Clark, C., Janssen, S., & Stansfeld, S. (2014). Auditory and non-auditory effects of noise on health. The Lancet, 383(9925), 1325–1332. https://doi.org/10.1016/S0140-6736(13)61613-X
  6. Berset, M., Elfering, A., Lüthy, S., Lüthi, S., & Semmer, N. K. (2011). Work stressors and impaired sleep: Rumination as a mediator. Stress and Health, 27(2), e71–e82. https://doi.org/10.1002/smi.1337
  7. Briner, R. B. (2000). Relationships between work environments, psychological environments and psychological well-being. Occupational Medicine, 50(5), 299–303. https://doi.org/10.1093/occmed/50.5.299
  8. Burgard, S. A., & Ailshire, J. A. (2009). Putting Work to Bed: Stressful Experiences on the Job and Sleep Quality. Journal of Health and Social Behavior, 50(4), 476–492. https://doi.org/10.1177/002214650905000407
  9. Cacioppo, J. T., Hawkley, L. C., Berntson, G. G., Ernst, J. M., Gibbs, A. C., Stickgold, R., & Hobson, J. A. (2002). Do Lonely Days Invade the Nights? Potential Social Modulation of Sleep Efficiency. Psychological Science, 13(4), 384–387.
  10. Chatzitheochari, S., & Arber, S. (2009). Lack of sleep, work and the long hours culture: Evidence from the UK Time Use Survey. Work, Employment and Society, 23(1), 30–48. https://doi.org/10.1177/0950017008099776
  11. Cropley, M., Dijk, D.-J., & Stanley, N. (2006). Job strain, work rumination, and sleep in school teachers. European Journal of Work and Organizational Psychology, 15(2), 181–196. https://doi.org/10.1080/13594320500513913
  12. Drake, C. L., Roehrs, T., Richardson, G., Walsh, J. K., & Roth, T. (2004). Shift Work Sleep Disorder: Prevalence and Consequences Beyond that of Symptomatic Day Workers. 27(8).
  13. Driver, H. S., & Taylor, S. R. (2000). Exercise and sleep. Sleep Medicine Reviews, 4(4), 387–402. https://doi.org/10.1053/smrv.2000.0110
  14. Eurofound. 2017. Sixth European Working Conditions Survey – Overview report (2017               update), Luxembourg, Publications Office of the European Union.
  15. Fagnani, J., & Letablier, M.-T. (2004). Work and Family Life Balance: The Impact of the 35-Hour laws in France. Work, Employment and Society, 18(3), 551–572. https://doi.org/10.1177/0950017004045550
  16. Fellinger, J., Holzinger, D., & Pollard, R. (2012). Mental health of deaf people. The Lancet, 379(9820), 1037–1044. https://doi.org/10.1016/S0140-6736(11)61143-4
  17. Felstead, A., Gallie, D., Green, F., & Henseke, G. (2019). Conceiving, designing and trailing a short-form measure of job quality: A proof-of-concept study: A short form measure of job quality. Industrial Relations Journal, 50(1), 2–19. https://doi.org/10.1111/irj.12241
  18. Fernández-Macías, E. (2012). Job Polarization in Europe? Changes in the Employment Structure and Job Quality, 1995-2007. Work and Occupations, 39(2), 157–182. https://doi.org/10.1177/0730888411427078
  19. Fryer, D. (1986). Employment deprivation and personal agency during unemployment: A             critical discussion of Jahoda's explanation of the psychological effects of    unemployment. Social Behaviour, 1(1), 3–23.
  20. Garbarino, S., Guglielmi, O., Puntoni, M., Bragazzi, N., & Magnavita, N. (2019). Sleep Quality among Police Officers: Implications and Insights from a Systematic Review and Meta-Analysis of the Literature. International Journal of Environmental Research and Public Health, 16(5), 885. https://doi.org/10.3390/ijerph16050885
  21. Green, F., Mostafa, T., Parent-Thirion, A., Vermeylen, G., van Houten, G., Biletta, I., & Lyly-Yrjanainen, M. (2013). Is Job Quality Becoming More Unequal? ILR Review, 66(4), 753–784. https://doi.org/10.1177/001979391306600402
  22. Hale, L., & Do, D. P. (2007). Racial Differences in Self-Reports of Sleep Duration in a Population-Based Study. Sleep, 30(9), 1096–1103. https://doi.org/10.1093/sleep/30.9.1096
  23. Hawkley, L. C., Preacher, K. J., & Cacioppo, J. T. (2010). Loneliness impairs daytime functioning but not sleep duration. Health Psychology, 29(2), 124–129. https://doi.org/10.1037/a0018646
  24. Henry, D., Knutson, K. L., & Orzech, K. M. (2013). Sleep, culture and health: Reflections on the other third of life. Social Science & Medicine, 79, 1–6. https://doi.org/10.1016/j.socscimed.2012.11.023
  25. Heyes, J., Tomlinson, M., & Whitworth, A. (2017). Underemployment and well-being in the UK before and after the Great Recession. Work, Employment and Society, 31(1), 71–89. https://doi.org/10.1177/0950017016666199
  26. Jahoda, M. 1982. Employment and Unemployment: A Social-Psychological Analysis,      Cambridge, Cambridge University Press.
  27. Kamerāde, D., & Richardson, H. (2018). Gender segregation, underemployment and subjective well-being in the UK labour market. Human Relations, 71(2), 285–309. https://doi.org/10.1177/0018726717713829
  28. Kim, J., Henly, J. R., Golden, L. M., & Lambert, S. J. (2020). Workplace Flexibility and Worker Well‐Being by Gender. Journal of Marriage and Family, 82(3), 892–910. https://doi.org/10.1111/jomf.12633
  29. Kim, T. J., & von dem Knesebeck, O. (2016). Perceived job insecurity, unemployment and depressive symptoms: A systematic review and meta-analysis of prospective observational studies. International Archives of Occupational and Environmental Health, 89(4), 561–573. https://doi.org/10.1007/s00420-015-1107-1
  30. Knudsen, H. K., Ducharme, L. J., & Roman, P. M. (2007). Job stress and poor sleep quality: Data from an American sample of full-time workers. Social Science & Medicine, 64(10), 1997–2007. https://doi.org/10.1016/j.socscimed.2007.02.020
  31. LaMontagne, A. D., Milner, A., Krnjacki, L., Schlichthorst, M., Kavanagh, A., Page, K., & Pirkis, J. (2016). Psychosocial job quality, mental health, and subjective wellbeing: A cross-sectional analysis of the baseline wave of the Australian Longitudinal Study on Male Health. BMC Public Health, 16(S3), 1049. https://doi.org/10.1186/s12889-016-3701-x
  32. Leufstadius, C., Eklund, M., & Erlandsson, L.-K. (2009). Meaningfulness in work – Experiences among employed individuals with persistent mental illness. Work, 34(1), 21–32. https://doi.org/10.3233/WOR-2009-0899
  33. Linton, S. J., Kecklund, G., Franklin, K. A., Leissner, L. C., Sivertsen, B., Lindberg, E., Svensson, A. C., Hansson, S. O., Sundin, Ö., Hetta, J., Björkelund, C., & Hall, C. (2015). The effect of the work environment on future sleep disturbances: A systematic review. Sleep Medicine Reviews, 23, 10–19. https://doi.org/10.1016/j.smrv.2014.10.010
  34. Magnavita, N., & Garbarino, S. (2017). Sleep, Health and Wellness at Work: A Scoping Review. International Journal of Environmental Research and Public Health, 14(11), 1347. https://doi.org/10.3390/ijerph14111347
  35. Mai, Q. D., Jacobs, A. W., & Schieman, S. (2019). Precarious sleep? Nonstandard work, gender, and sleep disturbance in 31 European countries. Social Science & Medicine, 237, 112424. https://doi.org/10.1016/j.socscimed.2019.112424
  36. Marucci-Wellman, H. R., Lombardi, D. A., & Willetts, J. L. (2016). Working multiple jobs over a day or a week: Short-term effects on sleep duration. Chronobiology International, 33(6), 630–649. https://doi.org/10.3109/07420528.2016.1167717
  37. Miller, A. D., & Rottinghaus, P. J. (2014). Career Indecision, Meaning in Life, and Anxiety: An Existential Framework. Journal of Career Assessment, 22(2), 233–247. https://doi.org/10.1177/1069072713493763
  38. Muñoz de Bustillo, R., Fernandez-Macias, E., Anton, J.-I. and Esteve, F. 2011. Measuring            More Than Money: The Social Economics of Job Quality, Cheltenham, Edward           Elgar.
  39. Olsen, K. M., & Kalleberg, A. L. (2004). Non-Standard Work in Two Different Employment Regimes: Norway and the United States. Work, Employment and Society, 18(2), 321–348. https://doi.org/10.1177/09500172004042772
  40. Ong, A. D., Bergeman, C. S., Bisconti, T. L., & Wallace, K. A. (2006). Psychological resilience, positive emotions, and successful adaptation to stress in later life. Journal of Personality and Social Psychology, 91(4), 730–749. https://doi.org/10.1037/0022-3514.91.4.730
  41. Piasna, A. (2018). Scheduled to work hard: The relationship between non-standard working hours and work intensity among European workers (2005-2015). Human Resource Management Journal, 28(1), 167–181. https://doi.org/10.1111/1748-8583.12171
  42. Reading, R., & Reynolds, S. (2001). Debt, social disadvantage and maternal depression. Social Science & Medicine, 53(4), 441–453. https://doi.org/10.1016/S0277-9536(00)00347-6
  43. Reid, K. J., Martinovich, Z., Finkel, S., Statsinger, J., Golden, R., Harter, K., & Zee, P. C. (2006). Sleep: A Marker of Physical and Mental Health in the Elderly. The American Journal of Geriatric Psychiatry, 14(10), 860–866. https://doi.org/10.1097/01.JGP.0000206164.56404.ba
  44. Schieman, S., & Young, M. (2010). The demands of creative work: Implications for stress in the work–family interface. Social Science Research, 39(2), 246–259. https://doi.org/10.1016/j.ssresearch.2009.05.008
  45. Smith, A. (1991). A review of the non-auditory effects of noise on health. Work & Stress, 5(1), 49–62. https://doi.org/10.1080/02678379108257002
  46. Stier, H., & Yaish, M. (2014). Occupational segregation and gender inequality in job quality: A multi-level approach. Work, Employment and Society, 28(2), 225–246. https://doi.org/10.1177/0950017013510758
  47. Swanson, L. M., Arnedt, J. T., Rosekind, M. R., Belenky, G., Balkin, T. J., & Drake, C. (2011). Sleep disorders and work performance: Findings from the 2008 National Sleep Foundation Sleep in America poll: Sleep disorders and work performance. Journal of Sleep Research, 20(3), 487–494. https://doi.org/10.1111/j.1365-2869.2010.00890.x
  48. Troxel, W. M., Buysse, D. J., Hall, M., Kamarck, T. W., Strollo, P. J., Owens, J. F., Reis, S. E., & Matthews, K. A. (2010). Social integration, social contacts, and blood pressure dipping in African–Americans and whites. Journal of Hypertension, 28(2), 265–271. https://doi.org/10.1097/HJH.0b013e328333ab01
  49. Van Laethem, M., Beckers, D. G., Kompier, M. A., Dijksterhuis, A., & Geurts, S. A. (2013). Psychosocial work characteristics and sleep quality: A systematic review of longitudinal and intervention research. Scandinavian Journal of Work, Environment & Health, 39(6), 535–549. https://doi.org/10.5271/sjweh.3376
  50. Wang, S., Kamerāde, D., Burchell, B., Coutts, A., & Balderson, S. U. (2022). What matters more for employees’ mental health: Job quality or job quantity? Cambridge Journal of Economics, 46(2), 251–274. https://doi.org/10.1093/cje/beab054
  51. Warr, P. 1987. Work, Unemployment, and Mental Health, New York, Oxford University              Press.
  52. Watson, N. F., Badr, M. S., Belenky, G., Bliwise, D. L., Buxton, O. M., Buysse, D., Dinges, D. F., Gangwisch, J., Grandner, M. A., Kushida, C., Malhotra, R. K., Martin, J. L., Patel, S. R., Quan, S., & Tasali, E. (2015). Recommended Amount of Sleep for a Healthy Adult: A Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society. SLEEP. https://doi.org/10.5665/sleep.4716
  53. Weich, S., & Lewis, G. (1998). Poverty, unemployment, and common mental disorders: Population based cohort study. BMJ, 317(7151), 115–119. https://doi.org/10.1136/bmj.317.7151.115
  54. Williams, A., Franche, R.-L., Ibrahim, S., Mustard, C. A., & Layton, F. R. (2006). Examining the relationship between work-family spillover and sleep quality. Journal of Occupational Health Psychology, 11(1), 27–37. https://doi.org/10.1037/1076-8998.11.1.27

Tables

Table 1. Country-level fixed effects models predicting effects of job quantity and job quality on sleep quality.


Model1

Model2

Model3

Model4

Job quantity

 

 

 

 

Work hours per week (Ref. = 35–40)

 

 

 

 

       1~34

-0.57

(0.59)


-0.50 (0.65)


       41~48

-2.24** (0.71)


-0.40

(0.75)


Work hour match (Ref. = Matched)

 

 

 

 

       Over-employed

-4.48***

(0.65)


-1.83**

(0.61)


       Under-employed

-3.21***

(0.84)


-1.63*

(0.81)


Work hours and work hour match (Ref. = Full-time matched)

 

 

 

 

       Part-time matched


0.69

(0.81)


0.41

(0.83)

       Full-time over-employed


-4.61***

(0.66)


-1.49*

(0.62)

       Part-time over-employed 


-6.04**

(1.85)


-3.54*

(1.66)

       Full-time under-employed


-2.56*

(1.17)


-0.52

(1.12)

       Part-time under-employed


-3.16**

(1.01)


-2.39*

(1.05)

Job quality 

 

 

 

 

       Earnings 

 

 

0.12*

(0.05)

0.11*

(0.05)

       Skills and discretion 

 

 

-0.07***

(0.01)

-0.07***

(0.01)

       Physical environment 

 

 

0.16***

(0.02)

0.16***

(0.02)

       Social environment 

 

 

0.09***

(0.01)

0.09***

(0.01)

       Work intensity 

 

 

0.24***

(0.02)

0.24***

(0.02)

       Prospects index 

 

 

0.06***

(0.02)

0.06***

(0.02)

       Working time quality 

 

 

0.10***

(0.02)

0.11***

(0.02)

       Meaningful work 

 

 

0.11***

(0.01)

0.11***

(0.01)

 

 

Constant

86.19***

(1.67)

85.69***

(1.67)

24.84***

(4.30)

24.81***

(4.28)

Observations

24594

24594

24594

24,594

Countries

34

34

34

34

R-squared

0.16

0.16

0.25

0.25

Note. All models control for age, gender, partnership, presence of children, education levels, presence of longstanding illness, ethnicity, household work and caring responsibilities. Robust standard errors in parentheses, ***p < 0.001, **p < 0.01, *p < 0.05.

 

Table 2. Country-level fixed effects models examining the interactions between work hour match and job quality.

 

Main effects 

Differences (interaction effects) 

(Ref= Matched)

 

Over-employed

Matched

Under-employed

Over-employed × Job quality

Under-employed × Job quality

EI 

0.13

0.12*

0.10

0.02

-0.01

 

(0.07)

(0.06)

(0.08)

(0.07)

(0.09)

SDI

-0.06**

-0.07***

-0.07*

0.00

-0.00

 

(0.02)

(0.02)

(0.03)

(0.03)

(0.03)

PEI

0.10**

0.18***

0.20***

-0.08

0.03

 

(0.04)

(0.03)

(0.04)

(0.04)

(0.05)

SEI

0.07***

0.09***

0.13***

-0.02

0.04

 

(0.02)

(0.02)

(0.03)

(0.03)

(0.03)

WII

0.29***

0.21***

0.25***

0.07*

0.03

 

(0.03)

(0.02)

(0.04)

(0.03)

(0.04)

PI

0.03

0.06**

0.08**

-0.03

0.02

 

(0.03)

(0.02)

(0.03)

(0.03)

(0.03)

WTQI

0.12**

0.10**

0.09

0.03

-0.01

 

(0.04)

(0.03)

(0.06)

(0.05)

(0.06)

MWI

0.14***

0.09***

0.10**

0.05

0.01

 

(0.03)

(0.02)

(0.04)

(0.03)

(0.04)

Note. EI = Earnings index, SDI = Skills and discretion index, PEI = Physical environment index, SEI = Social environment index, WII = Work intensity index, PI = Prospects index, WTQI = Working time quality index, MWI = Meaningful work index. All models control for age, gender, partnership, presence of children, education levels, presence of longstanding illness, ethnicity, household work and caring responsibilities. Robust standard errors in parentheses, ***p < 0.001, **p < 0.01, *p < 0.05.