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Journal of Happiness Studies

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01.08.2013 | Research Paper

Are Relative-Income Effects Constant Across the Well-Being Distribution?

verfasst von: Santiago Budria

Erschienen in: Journal of Happiness Studies | Ausgabe 4/2013

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Abstract

This paper challenges the common assumption made by economists to date that income comparisons are similarly important in different segments of the subjective well-being (SWB) distribution. The results, based on the 2000–2007 waves of the German SOEP and on a Generalized Ordered Probit for panel data, show that relative income, as measured either by the mean income of the reference group or the individual ordinal ranking within the group, exerts a differential effect across SWB levels. Such divergence is assessed by means of the tradeoff ratio between household income and the relative income variables. The results show that a low rank and falling below the average income in one’s group are significant determinants of low SWB but largely irrelevant when accounting for high SWB. The fact that conditionally unhappy individuals are more sensitive to comparisons, particularly if they are unfavorable, is consistent with earlier laboratory studies in the field of psychology.

Vorheriger Artikel Refinement and Psychometric Properties of the Eight-Item Brazilian Positive and Negative Affective Schedule for Children (PANAS-C8)

Nächster Artikel Subjective Well-Being and Hedonic Editing: How Happy People Maximize Joint Outcomes of Loss and Gain

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The available evidence indicates that: (1) those who are low on self-esteem or depressed are less likely to improve their mood after comparing with worse-off others and respond more negatively after comparing with better-off others (Wheeler and Miyake 1992; Aspinwall and Taylor 1993); (2) neurotic people respond more negatively to social comparisons, regardless of whether they are downwards or upwards (Van der Zee et al. 1998); and (3) comparisons pose a threat to the self when individuals feel they cannot determine their standing on the comparison dimension, or, to put it another way, the relative importance of downwards and upwards comparisons depends on the individual’s locus of control (Wood and VanderZee 1997).

We call attention to the average income level \( \bar{y}_{i} \) included in the regression, which can be regarded as part of the Mundlak term. However, for expositional purposes, we prefer to maintain a separate notation. Given its potential correlation with SWB and income, the proportion of years in employment during the observation period was also included as an additional Mundlak term in the earlier stages of the paper. This variable failed to be statistically significant in most specifications and was therefore dropped.

A principal component analysis with varimax rotation was conducted. Factor analyses clearly replicated the Big Five factors by yielding a correlation matrix with five eigenvalues above unity. The five principal components accounted for 60.7 % of the total variance. The Cronbach’s alphas for the five dimensions were 0.627, 0.660, 0.620, 0.505 and 0.617, respectively. For a given level of internal consistency, fewer items per dimension result in lower alphas (Mueller and Plug 2006). Hence, although these reliability coefficients are toward the lower range of admissible values, they point to a reasonable amount of internal consistency given the low (3) number of items per personality trait.

The BFI items are: I see myself as someone who… (1) worries a lot, (2) gets nervous easily, (3) is relaxed, handles stress well; (4) is communicative, talkative, (5) is outgoing, sociable, (6) is reserved; (7) is original, comes up with new ideas, (8) values artistic experiences, (9) has an active imagination; (10) is sometimes somewhat rude to others, (11) has a forgiving nature, (12) is considerate and kind to others; (13) does a thorough job, (14) does things effectively and efficiently, (15) tends to be lazy. Neuroticism: (1)–(3), Extraversion: (4)–(6), Openness to experience: (7)–(9), Agreeableness: (10)–(12), Conscientiousness: (13)–(15).

These are: (1) My life course depends on me, (2) Influence on social conditions through involvement, (3) Success takes hard work, (4) Doubt my abilities when problems arise; (5) Haven’t achieved what I deserve, (6) What you achieve depends on luck, (7) Others make the crucial decisions in my life; (8) Possibilities are defined by social conditions; (9) Abilities are more important than effort; (10) Little control over my life. Internal LOC measured by (1)–(4). External LOC measured by (5)–(10).

The alpha reliability coefficient for internal LOC was very low (0.218). An alternative would have been to drop specific items in order to raise the reliability ratio or to exclude this dimension from the set of controls. The principal component analysis was chosen to extract the relevant information from all the items simultaneously.

The OP imposes \( \theta_{itj} = \bar{\theta }_{it} \)for all j. In this case, and considering comparison and transitory income for instance, one gets from Eq. (8) that \( {{{\text{MPE}}_{j} (y^{c} )} \mathord{\left/ {\vphantom {{{\text{MPE}}_{j} (y^{c} )} {{\text{MPE}}_{j} (y)}}} \right. \kern-0pt} {{\text{MPE}}_{j} (y)}} = {{a_{c} } \mathord{\left/ {\vphantom {{a_{c} } a}} \right. \kern-0pt} a}_{s} \), for all j = 1,…, J.

The assessment of the statistical significance of the effects reported in the paper might be regarded as conservative, as clustering at the individual level resulted in approximately 30 % lower standard errors.

Alternatively, the MPE on the probability of reporting “at least j” can be obtained by adding the MPEs reported in columns 1 to j.

A 1-unit increase in logarithmic income corresponds to a 2.718 factor increase in absolute income. Smaller changes can be considered noting that a x percent variation in absolute income corresponds to ln(1 + x/100) units of variation in logarithmic income.

AIC = −2*log-likelihood + 2*k, where k = number of parameters. ΔIAC denotes the difference between specifications.

Rank may proxy for nonlinearities in the effect of family income. To rule out this concern, a third-order polynomial of log family income was included in the regression. The computations, which are not reported here, rendered similar estimates for the rank variable.

These patterns are remarkably consistent with Boyce (2010) and, especially, Blázquez and Budría (2011).

There are three types of regional variables in the SOEP. The more detailed classification is given by the German Regional Units (the Raumordnungsregionen), which are below the level of the federal states (16) and above the level of the counties (291). Such a disaggregation is not used in the present analysis.

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Titel: Are Relative-Income Effects Constant Across the Well-Being Distribution?
verfasst von: Santiago Budria
Publikationsdatum: 01.08.2013
Verlag: Springer Netherlands
Erschienen in: Journal of Happiness Studies / Ausgabe 4/2013
Print ISSN: 1389-4978
Elektronische ISSN: 1573-7780
DOI: https://doi.org/10.1007/s10902-012-9384-9

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