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Social Indicators Research

Erschienen in:

12.07.2015

Effects of Objective and Subjective Income Comparisons on Subjective Wellbeing

verfasst von: Byela Tibesigwa, Martine Visser, Brennan Hodkinson

Erschienen in: Social Indicators Research | Ausgabe 1/2016

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Abstract

In this article we present results from the Cape Area Panel Study investigating how income comparisons affect the subjective well-being of young adults and parents. The article builds on previous research in this area by studying whether an individual’s subjective well-being depends on the comparisons they make, not only to those around them, i.e., external comparisons, but to themselves across different time periods, i.e., internal comparisons. In the comparisons, we use both objective and subjective measures of relative income. Overall, we find strong evidence to suggest that income comparisons, both relative to neighbours and relative to oneself, affect subjective well-being. Another interesting observation is that, depending on the comparator, young adults and parents differ in their perceptions of their own well-being. Furthermore, and as expected, we found that objective, as well as subjective, measures of well-being influence well-being, but that this is more prevalent in external than internal comparisons.

Vorheriger Artikel The National Work–Life Balance Index©: The European Case

Nächster Artikel Can Corruption Erode Confidence in Political Institutions Among European Countries? Comparing the Effects of Different Measures of Perceived Corruption

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The study of happiness originated from psychology (Frey and Stutzer 2002; Frijters et al. 2004). Easterlin’s (1974) seminar paper, which introduced the Esterlin paradox, sparked an interest in happiness in economics literature (Frey and Stutzer 2002). It was only in the 1990s, however, that interest in the study of happiness was rekindled in the economics field (Frey and Stutzer 2002; Frijters et al. 2004).

Owing to the fact that neighbourhoods in South Africa are homogenous in terms of housing size and housing type. The neighbourhood in this case is equivalent to the enumerated area.

See other related theories: adaptation-level theory (see Parduci 1995; Frederick and Loewenstein 1999); aspiration theory (see Michalos 1991; Inglehart and Klingemann 2000); personality theory (see Costa and McCrae 1980); dynamic equilibrium theory (see Headey and Wearing 1992) and hedonic treadmill theory (see Carver and Scheier 1990; Frederick and Loewenstein 1999; Lyubomirsky 2001; Diener et al. 2006).

Some have argued that, while personality does feature as an important determinant, other factors such as circumstantial and social demographic factors also play a role, albeit a much smaller one. For example, Argyle (1987) concludes that 15 % of the explained variance of happiness is due to these external factors, based on a general survey of the psychological literature on the subject. Supporting this, Veenhoven (1994) finds that happiness is influenced by changing circumstances and adversity, making the study of happiness a worthwhile endeavor. All of this is probably best summarized in Diener et al.’s (1999) thirty year review of subjective wellbeing, when they conclude that “subjective wellbeing has both trait-like and state-like properties” (Diener et al. 1999, p. 280).

There is some compelling evidence to support set point theory and it has been studied widely (Magnus and Diener 1991; Headey and Wearing 1989) and has shown that a large part of the variance of subjective well-being is explained by unique individual genetic factors. For example, in a study of twins growing up in separate homes, it was found that there was little difference between their subjective well-being, leading to the conclusion that from 40 to 55 % of the variance in happiness can be put down to genetic factors (Tellegen et al. 1988). In another study by Lykken and Tellegen (1996), looking at long term subjective wellbeing, it was concluded that as much as 80 % of the long-term variance is due to inherent individual characteristics.

This was outlined in a famous study by Brickman et al. (1978) showing that lottery winners over time were not significantly happier than the control group and victims of spinal injuries were happier than they were expected to be given their disability.

See also Huppert and Wittington (2005) and Diener et al. (2006) for a critique of the set point theory and other related theories.

For more details, see Davis (2012) who provides a formulation of the model, while Senik (2004); Senik (2008); Kingdon and Knight (2007) and Davis and Wu (2014) offer empirical evidence.

The Cape Area Panel Study Waves 1-2-3 were collected between 2002 and 2005 by the University of Cape Town and the University of Michigan, with funding provided by the US National Institute for Child Health and Human Development and the Andrew W. Mellon Foundation. Wave 4 was collected in 2006 by the University of Cape Town, University of Michigan and Princeton University. Major funding for Wave 4 was provided by the National Institute on Aging through a grant to Princeton University, in addition to funding provided by NICHD through the University of Michigan.

Unfortunately this question did not appear in the other waves of CAPS, which meant that we could not fully exploit the panel nature of the dataset for the purposes of this analysis.

Successful and highly consistent applications have been widespread, studying the effects of noise pollution (Van Praag and Ferrer-I-Carbonell 2004), flooding (Luechinger and Raschky 2009), inflation and employment (Di Tella et al. 2001), terrorism (Frey 2008), air quality (Levinson 2009 and Luechinger 2009), economic performance (Oswald 1997), neighborhood satisfaction (Fried 1984), crime (Powdthavee 2005) and inequality (Alesina et al. 2004) while other studies investigate the structure of happiness functions (Hinks and Gruen 2007; Graham 2005) and how they differ across cultures and countries (Hayo and Seifert 2003; Powdthavee 2007).

Subjective measures are self-reported by the respondent, whereas objective measures are based on actual variables.

For example, while self-reported health has traditionally been an important determinant of subjective well-being, objectively defined health measures such as trips to doctors or independent assessments of respondents’ health have been shown to have a smaller impact on well-being, if any at all. This is especially true for long term and chronic illnesses, were some adaptation has occurred. Interestingly, this is not the case with relative income. Other studies have shown that both objective and subjective measures (Luttmer 2005; Dolan et al. 2008; Bookwalter and Dalenberg 2010) have similar impacts on happiness—even after long periods of time. This suggests that relative income is fundamental to an individual’s happiness and is not susceptible or sensitive to personal or external evaluations of where an individual lies on the income spectrum.

‘How would you compare this household’s current financial situation to the situation of other households in this same neighbourhood? Is the current situation much better, slightly better, about the same, slightly worse, or much worse than the situation of others in this area?’.

The specific questions are as follows: How would you compare the household’s financial situation this year to the situation 1 year ago—2004? Is the situation this year much better, slightly better, about the same, slightly worse, or much worse than it was last year? How would you compare the household’s financial situation this year to the situation 3 years ago—2002? Is the situation this year much better, slightly better, about the same, slightly worse, or much worse than in 2002?

Please note that we are unable to test the objective measure using a 1-year internal comparison because of data limitation. That is, wave 2 (2004) does not contain information on income. Hence in the current analysis we use CAPS wave 3 (2005) and wave 1 (2002) which give us a 3-year comparison.

For example, marital status and number of children (Ferrer-i-Carbonell and Frijters 2004) have been found to be an important covariate, but since we are comparing the results between young adults and their parents, we omit this covariate. Including these in our parent model does not qualitatively change our results.

A more detailed breakdown of education was not possible as almost all respondents had completed primary school, owing to the fact that the area covered was urban. Further, given the young adult and parent split of the dataset, there were a small number of respondents who had completed tertiary study, with a much higher number currently enrolled.

Poor health and average health were merged into one owing to the small number of respondent stating they had poor health; again, this is possibly due to the high proportion of young adults in the sample.

The same data was used by and Kingdon and Knight (2006) and Powdthavee (2007).

According to Manski (1993) the other terminologies used to denote endogenous social effects include social norms, peer influence, neighborhood effects, conformity, imitation, contagion, epidemics, bandwagons, herd behavior, social interactions or interdepend preferences (Manski 1993, p. 531).

The reflection problem occurs because neighbourhood behaviour and characteristics are collinear, as a result of this linear dependence the equation is unidentifiable, as one is not able to determine whether it is behaviour or characteristics that drives the outcome (Manski’s 1993; Haurin et al. 2003). See Manski’s (1993) for a detailed discussion.

Selection arises when individuals are (un)intentionally sorted into the neighborhoods. This non-random assignment of individuals into neighborhoods is likely to result in clusters of individuals with similar socioeconomic characteristics. Such clustering is likely to be tainted by selection bias. See Haurin et al. (2003) and Angrist and Pischke (2008) for an overview of selection bias and (non)randomisation.

People often conform to the attitudinal and behavioural norms of their reference group or social network-such norms constitute a potent source of social influence. That group norms affect individuals’ behavioural choices regarding prevention has been documented. One reason why people adhere to group norms and espouse group values is because they fear sanctions for nonconformity (Fisher 1988, p. 914).

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Titel: Effects of Objective and Subjective Income Comparisons on Subjective Wellbeing
verfasst von: Byela Tibesigwa
Martine Visser
Brennan Hodkinson
Publikationsdatum: 12.07.2015
Verlag: Springer Netherlands
Erschienen in: Social Indicators Research / Ausgabe 1/2016
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-015-1035-1

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