Abstract
Whether perceived importance can be used as a weighting factor for the aggregation of domain satisfaction is an important issue in the literature on subjective well-being (SWB) measures with multidimensional domains. This paper extends the weighting approach proposed by Benjamin et al. (Am Econ Rev 104:2698–2735, 2014) and uses it to weight domain satisfaction. The weights estimated by this approach can be interpreted as marginal utility, and the weighted average of the domain satisfaction of each individual can be interpreted as the individual’s utility. Using a data set collected from a 2015 survey that we conducted with residents of all Japanese prefectures, we show that this weighted average improves the goodness of fit for overall SWB measures compared with the unweighted averages of domain satisfaction. This finding supports incorporating perceived importance measures into SWB measures. Moreover, we show that, although there are differences in perceived importance among some domains that are less relevant to specific sub-populations, such as work for people above 65 years or relations with family for those who live alone, the relative importance of most domains is similar across the different categories of gender, household income, age, and household composition. Allowing for heterogeneity in the importance weights across these demographic groups provides only negligible improvements to the validity of the domain importance. The approach suggested in this paper can be considered a resident-oriented approach in the sense that the weights are derived from the perceived importance of residents themselves and are thus expected to contribute to resident-oriented policymaking.
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Notes
Although the DEA and MCDM approaches use only domain satisfaction scores, we categorize them as part of domain importance because the estimated weights derived from this approach can be interpreted as implicitly assumed domain importance weights.
For a detailed description of the data, see Okada et al. (2019).
While the better life index considers 11 domains, the number of domains in our paper is 18. This is because compared with the OECD better life index, we add (1) relationships with local people, relationships with friends and acquaintances, and relationships with family instead of community, (2) politics and participation in policy making instead of civic engagement, (3) leisure time, eating habits, and religion instead of life satisfaction, (4) status and honor, and (5) assets, status and honor. However, as Cummins (1996) suggests, there are at least 173 different domain names that have been used in the literature, and the possible number of domains is even larger. Thus, the domains we cover are unlikely to be comprehensive, which is true of all other previous studies. This problem is discussed in the discussion and conclusion section as a limitation of the study. Another potential problem is the overlap among domains; the inclusion of overlapping domains in the composite well-being index leads to double-counting (Benjamin et al., 2014). This potential problem is also discussed in the discussion and conclusion section.
Another potential problem is that “income” and “work” are irrelevant to full-time students, and their answers for these domain satisfactions might lack credibility. This point is also discussed in the discussion and conclusion section.
If the number of respondents is n, the total sample size is 171n. Since number of respondents of our survey is 246,642, the total sample size is 42,175,782 (= 171*246,642).
Another possible way to construct the dependent variable \({y}_{i}\) is to take the differences of the level of importance ratings. This approach has the advantage that it utilizes the information on the level of importance ratings, which is ignored in the estimation model represented by Eqs. (5)–(8). However, in return for this advantage, it requires that the evaluation of the level differences be precisely measured with a 9-point scale (from -4 to + 4), which would be a too strict requirement considering that most previous studies that have evaluated such level differences have adopted at most a five-point scale (e.g. Benjamin et al., 2014).
Among the demographics that Benjamin et al (2014) choose, we do not take social ideology (liberal, moderate, or conservative) into consideration because these classifications are not necessarily distinctive in Japan. Moreover, we do not consider religiosity (attendance or nonattendance at religious services at least monthly) because regular participations in religious services is not common in Japan. Instead, we add (d) household composition, which is often considered to affect people’s well-being (e.g. Frey, 2008).
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Funding
This work was financially supported by the Environment Research and Technology Development Fund (1-2001) of the Environmental Restoration and Conservation Agency of Japan and JSPS KAKENHI Grant Numbers JP19K15919 and 20H00648.
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Kitsuki, A., Managi, S. Importance Weighting in Subjective Well-Being Measures: Using Marginal Utilities as Weights for Domain Satisfaction. J Happiness Stud 24, 1101–1120 (2023). https://doi.org/10.1007/s10902-023-00636-4
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DOI: https://doi.org/10.1007/s10902-023-00636-4