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Perceived Capabilities as an Aggregated Indicator for Well-Being

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Abstract

Much of the applied research on quality of life uses information on life satisfaction (or happiness) as an aggregate indicator for well-being. In this paper, we engage in an application of the capabilities approach for the measurement and interpretation of individual well-being. Specifically, we measure self-reported or perceived capabilities by asking the (Flemish) respondents “How do you consider your possibilities/opportunities in life in general?”. We examine whether this kind of information is helpful for the challenge of aggregating underlying dimensions of well-being. We compare with the satisfaction with life approach and differentiate between two kinds of determinants: realisations on various life domains and personality traits. We find that perceived capabilities are more connected to realisations on life domains, while life satisfaction is more related to the personality traits. This result supports the position that information about (perceived) capabilities is a useful additional ingredient for the assessment of general or aggregated well-being.

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Notes

  1. LEVO is the Dutch acronym for “LEvensomstandigheden in Vlaanderen Onderzocht” (research on living circumstances in Flanders). It is a yearly large-scale survey organized in the framework of a research seminar at the University (College) Ghent. The field work is carried out by students of the University (College) Ghent. Organization, supervision, controlling and cleaning is performed by the authors.

  2. In the survey, we describe the two opposites of each personality trait: e.g. for the variable ‘extravert’ the left column of the question mentions ‘introvert/closed’ and the right column is ‘extravert/open’. We use 3/2/1/0/1/2/3 as answering possibilities in the survey, such that we do not give any direction to the respondent about what is considered as good or not. In the discussion in the text, we mention the variables as the descriptions of the right hand side as this gets the highest number in the processing.

  3. A t-tests on the means of full-time workers versus students shows that the difference in satisfaction is not significant (p = 0.768), the difference in capabilities is significant (p = 0.001).

  4. However, t-tests on the means of pensioners versus househusbands/wives shows that the differences are not significant (for satisfaction, p = 0.543; for capabilities, p = 0.146).

  5. A t-tests on the means of pensioners versus students shows that the difference in satisfaction is not significant (p = 0.455), the difference in capabilities is significant (p = 0.000).

  6. We did not fix the number of factors in advance but kept those factors with an eigenvalue greater than one. See Appendix 1 for the rotated component matrix.

  7. Van Praag & Ferrer-i-Carbonell (2010) have a theoretical discussion on the estimation methods in happiness economics, their conclusion: “It follows that we do not have to be very anxious on which particular estimation method is used” (p.15). Also for our results, the conclusions are similar when using an ordered regression model. This can be seen from the comparison of the results for both methods as presented in Appendix 2.

  8. Socio-economic position is correlated with many other variables (such as age for the distinction between students and pensioners, incapable to work is related to health, unemployed to social life and income,…). Socio-economic position is significant in a model only including the different positions but no longer when including the different life domains. As we want to capture the impact of life domains and personality for satisfaction and capabilities, we therefore exclude the socio economic-position from the models presented.

  9. The ‘product measure’ proposed by Pratt (1987, in Thomas et al., 1998) is the product of the correlation and the beta weight. The relative Pratt index is computed by dividing this product by the R2 of the model.

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Authors and Affiliations

  1. Faculty of Economics and Business Administration and Sherppa, Ghent University, Henleykaai 84, 9000, Ghent, Belgium

    Luc Van Ootegem & Elsy Verhofstadt

  2. Research Institute for Work and Society, KU Leuven, Belgium

    Luc Van Ootegem

Authors
  1. Luc Van Ootegem
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  2. Elsy Verhofstadt
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Correspondence to Luc Van Ootegem.

Appendices

Appendix 1

Table 8 Rotated component matrix for the personality factors
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Appendix 2

Table 9 Comparison of results for OLS and ordered probit models
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Van Ootegem, L., Verhofstadt, E. Perceived Capabilities as an Aggregated Indicator for Well-Being. Applied Research Quality Life 10, 615–629 (2015). https://doi.org/10.1007/s11482-014-9343-1

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