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Counting Happiness from the Individual Level to the Group Level

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Abstract

A procedure for identifying the happy individuals in society is indispensable to efforts to align public policy with the needs of the citizenry. Individual self-reports on happiness, however, must first meet the dual requirements of cardinality and (at least) relative interpersonal comparability before they can be used in conjunction with a counting procedure to give a meaningful estimate of group level happiness. The paper demonstrates a count-based measure of group happiness that exhibits these desired qualities using data from the Philippines.

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Notes

  1. Here, “happiness” and “subjective well-being” (SWB) are considered synonyms. Positive and negative feelings and life satisfaction comprise SWB. Kahneman and Deaton (2010) propose the notion of “emotional well-being” (i.e., emotional happiness) to cover both types of feelings. Note that SWB is a broader concept than happiness in everyday language.

  2. The extant literature takes the numerical quotations of SWB as fulfilling the cardinality requirement, albeit cardinality and numerical quotations are two different concepts.

    Frey et al. (2010) argue the cardinality is not necessary for some of the applications of SWB—that is, the ordinality of SWB is good enough in order to perform analysis that is consistent with utility theory.

  3. An alternative to the lineup scale format is the ladder scale format (Cantril 1965). From Stevens (1946), it is known that a constant increment between two consecutive measures is the minimum requirement for a cardinal measure. The comparability of self-reports is the minimum requirement to make the aggregation of self-reports sensible. Such condition requires the absence of squishing or stretching of values on the scale (Gilbert 2006; see also Kahneman and Miller 1986; Frederick and Loewenstein 1999). The representation of a lineup or ladder scale with constant increment is taken in the social sciences to suggest that self-reports are comparable. Diagram 1 thus seeks to fulfill the requirements in economics for measurement.

  4. Suppose the person declares a 5 or 50 %. Such self-report is deemed similar to an evaluation of a glass that is 50 % full (or 50 % empty)—meaning to say, a 50 % full glass is seen as 50 % full regardless of its size or location, the time of day when it was evaluated, or the demographic and socio-economic profile of the person making the appraisal. In short, half-full glass assessments are interpersonally comparable. Self-reports of 50 % in one instance or location, etc., are by extension comparable to self-reports of 50 % in another instance or location, etc. The same applies for other valuations. Note that absolute interpersonal comparability is not required—it is also impossible to achieve.

    The glass analogy may be problematic for a 100 % full glass if the glass is not calibrated. An experiment using college sophomores (N = 357; male = 183) finds that 5 % of the students drew a “100 % full glass” as a glass that is filled below the brim when they are not given an instruction or pointers to calibrate the glass accordingly. Still, there is no correlation between figuring out the 10 % increments on the scale and drawing a “100 % full glass” as a glass filled below the brim [F(1, 355) = 1.888, p = 0.171)]. Another interpretation of the drawings looks at the maximizer and satisficer behavior (Beja 2012).

  5. The mean valuation of a life domain or another aggregate measure often serves as proxy for the reference group effect (c.f., Clark and Oswald 1996; Luttmer 2006; Clark and Senik 2010). Notice, though, that such information is an external metric—it is not therefore consistent with a personal assessment on one’s status relative to the status of the reference group for a relevant life domain. The introduction of the mean rating of status, domain, etc. might conflate the social reference and social context effects (Grice 1989).

  6. Indicators of socio-economic status are generally weakly related with subjective well-being. Diener et al. (1999) and Lyubomirsky et al. (2005a, b) find that individual circumstances can account for about 10 % of the variance in subjective well-being. Studies of Anderson et al. (2012), Keltner et al. (2003), and Anderson et al. (2001), among others, find that indicators for sociometric status are better than individual circumstances because they are defined locally (i.e., there is proximity) and, of course, defined by the person.

  7. The dataset used in this study includes a standard single-item query on SWB.

  8. The Alkire-Foster procedure has been used to count the number poor children in Bangladesh (Alkire and Roche 2011), identify the potential recipients of conditional cash transfers in Latin America (Azevedo and Robles 2010), and count the happy people in Bhutan (Ura et al. 2012).

  9. An expanded version of the procedure can include both subjective and objective indicators. Section 3 presents the calculations using subjective indicators only. For the expanded procedure, let y = [y ij,subj |y ik,obj ] as an augmented matrix for person i = 1…n (row), subjective life domain j = 1…m (column), and objective life domain k = 1…m (column). The number of columns for the subjective and objective life domains may depend on the coverage of the study. In this case, Y > y ik  > 0, y ik is either an integer (years of schooling) or a continuous number (life expectancy at birth, income, etc.) with Y as the maximum value for the specific objective life domain. The thresholds for y ij,subj and y ik,obj are separately defined: 10 > y * j  > 0 and Y > y * k  > 0. As before, g ij  = 1 iff y ij  > y * j and g ij  = 0 otherwise and g ik  = 1 iff y ik  > y * k and g ik  = 0 otherwise and obtain g = [g ij |g ik ] as an augmented matrix representing the instances that exceed the threshold values. And so, \( \sum\nolimits_{j = 1}^{m} {g_{ij} } \) and \( \sum\nolimits_{k = 1}^{m} {g_{ik} } \) obtain s = [s i |z i ] as an augmented vector where m ≥ s i  > 0 and m ≥ z i  > 0. Lastly, define h = [h i ] as the censored augmented vector s with h i  = 1 iff both s i  ≥ d subj and z i  ≥ d obj and h j  = 0 otherwise. From h, the proportion of happy people is \( \sum {h/n} \).

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Correspondence to Edsel L. Beja Jr..

Appendix: Survey questions grouped by life domains

Appendix: Survey questions grouped by life domains

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Beja, E.L., Yap, D.B. Counting Happiness from the Individual Level to the Group Level. Soc Indic Res 114, 621–637 (2013). https://doi.org/10.1007/s11205-012-0165-y

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