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Reaching Compromises in Workers’ Life Satisfaction: A Multiobjective Interval Programming Approach


The appraisal of job satisfaction and life satisfaction has been the focus of attention of work-family research. Despite being frequently regarded as a non-work variable, life satisfaction plays an important role in organizational behaviour and human resource management. Previous research has ascertained that workers’ life satisfaction is inherently a multidimensional concept. We extend this line of work by analysing the main factors that might have an influence on the trade-offs among four different aspects of workers’ life satisfaction (satisfaction with education, present job, family life, and social life) in reaching compromises between them. A methodological approach that combines econometric and multiobjective interval programming techniques has been used. This methodological framework allows evaluating the compromises of specific aspects of workers’ personal and working conditions in different scenarios given as intervals. Our findings suggest that female workers must generally spend more time at their jobs than men to reach the highest balanced levels of satisfaction across all aspects under evaluation. Additionally, one child is sufficient to reach the highest levels of life satisfaction (among all factors considered in its assessment) for both men and women. One possible policy implication of these results may be that existing work-family arrangements are not sufficient in the current context of falling birth rates all over Europe.

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  1. Several influential papers, as Stevenson and Wolfers (2013), Aghion et al. (2016) and Fehder et al. (2018), among many others, have used the terms “life satisfaction” and “well-being” interchangeably.

  2. The study of satisfaction using a combination of econometrics and multiobjective programming techniques has only been performed for the Spanish population (Marcenaro-Gutierrez et al. 2010, analysed Spanish workers’ satisfaction; while Marcenaro-Gutierrez et al. 2015, evaluated Spanish teachers’ satisfaction). Besides, a combination of econometrics and multiobjective interval programming techniques has also been used in the assessment of Spanish workers’ satisfaction (Henriques et al. 2018).

  3. The objective measurement of satisfaction is defined as “health and development—behaviour, emotions, attainment, and so on—and, to some extent, the factors that impinge on these, such as housing, parenting, environment, and socioeconomic situation”.

  4. These countries are: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, FYR of Macedonia, Germany, Greece, Hungary, Iceland, Ireland, Italy, Kosovo, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Turkey and United Kingdom.

  5. Although in many countries the retirement age is 65, very few individuals in this sample were 65 years old, so we have considered those who were 64 or younger.

  6. Specifically, “those assisting on family farm or business” (0.6%), “unemployed people” (8.1%), those “unable to work due to long-term illness or disability” (2.1%), “retired” people (29.5%), “full time homemaker/responsible for ordinary shopping and looking after the home” (8.7%), “in education/students” (5.7%) and “other” (representing only 0.9%) were removed from the sample. In addition, we kept individuals who were of working age (18–64 years old; which entailed reducing 0.5% of the sample after removing the previous individuals). The proportion of working population is very similar to the one reported by the Federal Reserve Bank of St. Louis (

  7. The data we use for our empirical approach also includes information on satisfaction with aspects regarding pecuniary issues (current standard of living, accommodation, economic situation of the country, etc.). However, the challenges of dealing with more than three objective functions in multiobjective programming are clearly acknowledged in the scientific literature, namely the computation time for many indicators that can easily become computationally hard and the ratio of non-dominated points, which tends to increase rapidly with the number of objective functions (Emmerich and Deutz 2018). In such cases, highly correlated objective functions should be avoided in order to prevent the reduction of the search space for efficient solutions. Therefore, due to the high correlation of these satisfaction variables with the four aspects used in this study (especially in the case of education and present job satisfactions, with correlations reaching 0.90 in some countries as, e.g., Greece), we have only considered these four satisfaction aspects.

  8. If we had also employed the lower and upper bounds for those not statistically significant variables, this would have implied assuming that, on average, the variable is significantly different from zero, which is not the case.

  9. Michalos (2017) includes an extensive discussion on the link between education and life satisfaction.

  10. This means that whenever any of the categories of a categorical variable (with 3 or more categories) is not significant, one of them is not included in the constraint.

  11. The computations corresponding to these profiles are not provided in order to save space, but are available upon request to the authors.


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This research was partly supported by the Spanish Ministry of Economy and Competitiveness (Project ECO2017-88883-R) and by the Fundação para a Ciência e a Tecnologia (FCT) under Project Grant UID/Multi/00308/2019. This work has been also partly supported by the Andalusian Regional Ministry of Economy, Knowledge, Business and University (PAIDI group SEJ-532 and UMA18-FEDERJA-024 also supported by FEDER funding). Carla Oliveira Henriques also acknowledges the training received from the University of Malaga PhD Programme in Economy and Business [Programa de Doctorado en Economía y Empresa de la Universidad de Malaga].

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Correspondence to C. O. Henriques.

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See Table 9.

Table 9 Decision variables and descriptive statistics.

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Henriques, C.O., Lopez-Agudo, L.A., Marcenaro-Gutierrez, O.D. et al. Reaching Compromises in Workers’ Life Satisfaction: A Multiobjective Interval Programming Approach. J Happiness Stud 22, 207–239 (2021).

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  • Life satisfaction
  • Education
  • Work
  • Family life
  • Social life
  • Multiobjective interval programming
  • Econometric analysis

JEL Classification

  • J28
  • C54
  • C61