Job Characteristics and Life Satisfaction in the EU: a Domains-of-Life Approach

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Abstract

Working life has come to permeate every domain of life. Characteristics once thought to affect only the job domain have become important determinants of how people assess their daily lives. This article explores the influence of job characteristics on satisfaction with several life domains in 28 EU countries, asking: 1) What is the relationship between job characteristics and satisfaction with work and other domains of life? 2) Is the job domain more important for life satisfaction than other domains of life? Additionally, we apply a domains-of-life perspective to investigate possible differences in these relationships between high- and low-skilled workers, using data on white-collar workers from the third European Quality of Life Survey (3EQLS) and multiple Ordinary Least Squares (OLS) regressions to estimate the models.Work–life balance and perceived job (in)security emerge as important determinants of satisfaction regarding all domains and both types of workers studied. Satisfaction in the work domain ranks fourth in contributing to overall life satisfaction, after the standard of living, family life and social life domains. This relatively low direct contribution to life satisfaction of the work domain is particularly visible among low-skilled workers. We conclude with a discussion of the implications for workers’ wellbeing of the increasing insecurity in the job market and the fact that meaning is often sought through work despite the effects of poor work–life balance on most life-domains.

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Change history

  • 08 September 2020

    The original version of the article unfortunately contained an error in Table 2.

Notes

  1. 1.

    Following Gough and McGregor (2007) we understand “wellbeing” as an umbrella term comprehending both objective and subjective approaches. Concerning subjective wellbeing, we draw on Diener (1984) and Diener et al. (1999) in considering its hedonic and cognitive dimensions, the former linked to positive and negative effects and the latter to life satisfaction.

  2. 2.

    The transformation from mass production to flexible production, characteristic of the post-Ford era, has also altered the political and economic structure of society and its production systems. Thompson (2003) found that flexible production dramatically reduced the demand for unskilled labour, requiring workers with flexible specialization and multi-skilled (social and technical skills). The number of unskilled industrial workers has been falling for nearly thirty years. This decrease is reflected in the transformation of the workforce, with the growth of managerial and professional services, and the increase in white-collar jobs to the detriment of blue-collar jobs: towards to a service economy, with a decline in the mass production and manufacturing sectors. This change also implies global competition, flexible production systems, flatter and more flexible organizational structures, with the emphasis on innovation, diversification and subcontracting (Avis 1996; Brown and Lauder 1992).

  3. 3.

    Legislators, senior officials and managers, professionals and technicians, associate professionals, clerks, service workers, and shop and market sales workers.

  4. 4.

    Eurostat Labour force data: http://ec.europa.eu/eurostat/web/lfs/data/ accessed 06/12/2017.

  5. 5.

    Knowledge workers’ are defined as a new type of white-collar workers who generally possess higher academic degrees, greater skill levels or knowledge, working in the three highest standard occupational classifications (managers, professionals, associate professionals) (Huang 2011).

  6. 6.

    For an exhaustive list of life domains used in other studies see Loewe et al. (2014, pp. 74–75).

  7. 7.

    “Post-Fordism” refers to the dominant system of economic production, consumption, and associated socioeconomic phenomena in most industrialized countries since the late twentieth century. It describes an approach to work organization that relies on flexibility, adaptation and innovation (Heery and Noon 2008).

  8. 8.

    For an exhaustive list of life domains used in other studies see Loewe et al. (2014, pp. 74–75).

  9. 9.

    ISCO detailed classification: http://www.ilo.org/public/english/bureau/stat/isco/index.htm [accessed 29/01/18].

  10. 10.

    Structural equation modelling is an umbrella term that incudes ‘methodologies that seek to represent hypotheses about the means, variances and covariances of observed data in terms of a smaller number of ‘structural’ parameters defined by a hypothesized underlying model’ (Kaplan, 2009, p. 1). In our study, all variables (independent, control and dependent variables) are observed – and not psycho-social constructs or latent variables, which are the type of variables SEM was designed to model (Nachtigall et al., 2003). The literature on subjective wellbeing has examples of studies using SEM (Loewe et al. 2014; Rode and Near 2005) when the dependent variables were not observed and the study of particular paths was not the aim of the research. Two-step models were preferred when all subjective wellbeing variables were observed and the goal was to examine particular relationships – as in the case in this paper, which focuses on the role of work-related variables.

  11. 11.

    “The semi-logarithm specification implies diminishing returns to any domain satisfaction, an increasing marginal rate of substitution between satisfaction in any two domains, and concavity of life satisfaction in domains” (Rojas 2007, p. 11).

  12. 12.

    As we did with the work-life characteristics and satisfaction with domains of life regressions (equations 1), we have estimated the linear regression model in two additional ways in order to assess the robustness of the results. First, by cluster-robust stander errors (CRSE); second, by using a hierarchical linear regression model. The results obtained from CRSE estimation and from using a hierarchical linear model are available upon request.

    All models were also estimated using an ordered probit model to check the robustness of the results. The coefficients and significance of the estimators did not differ notably between specifications. Hence, following Rojas (2007) we decided to maintain the OLS specification.

  13. 13.

    Percentage of temporary employees in 2017, data from Eurostat, Employment and unemployment (LFS) statistics: http://ec.europa.eu/eurostat/web/lfs/data/database [accessed 20/05/2018]

  14. 14.

    See Jeffrey et al. (2014) for additional measures to improve wellbeing at work and overall life satisfaction.

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Acknowledgments

The authors acknowledge the support of a doctoral grant from the Universitat Oberta de Catalunya, UOC. The article has also benefited from funding for the project Responsible Innovation and Happiness: A New Approach to the Effects of ICTs, supported by the Research Council of Norway and conducted by the Centre for Technology, Innovation and Culture, University of Oslo.

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Appendix

Appendix

Table 5 Pearson’s correlations across domains
Table 6 Indicators and domains, Pearson’s correlations
Table 7 Likert-type scales: Life and domains-of-life satisfaction responses
Table 8 Occupation mean comparison: results from T-test across domains

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Viñas-Bardolet, C., Guillen-Royo, M. & Torrent-Sellens, J. Job Characteristics and Life Satisfaction in the EU: a Domains-of-Life Approach. Applied Research Quality Life 15, 1069–1098 (2020). https://doi.org/10.1007/s11482-019-09720-5

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Keywords

  • Domains of life satisfaction
  • Job satisfaction
  • Life satisfaction
  • White-collar workers
  • Working conditions
  • Work–life balance conflict