A Model of Income Evaluation: Income Comparison on Subjective Reference Income Distribution

People's evaluation of the relative position of their income is not as accurate as the relative income hypothesis assumes. It is observed from empirical survey data that income evaluation is concentrated in the middle. We develop a model that assumes income comparison on a subjective income reference distribution to explain the centralization phenomenon of income evaluation. We conduct theoretical analysis and empirical parameter estimation using Bayesian statistical modeling. The theoretical analysis shows that the centralization of income evaluation distribution occurs when the subjective reference distribution is more dispersed than the objective distribution. Empirical analysis using Japanese data from 2015 shows that the relationship between subjective and objective distributions differed depending on social categories with different social experiences. Women had a more ambiguous distribution than men. Among men, those aged 45--54 had a subjective distribution closest to the objective distribution. Thus, the subjective reference income distributions that potentially define people's evaluation of their income and their differences based on social category were only clarified by constructing the model.

Income Status and Life Satisfaction

The importance of both income rank and relative income, as indicators of status, has long been recognised in the literature on life satisfaction and happiness. Recently, several authors have made explicit comparisons of the relative importance of these two measures of income status, and concluded that rank dominates to the extent that reference income becomes insignificant in regressions including both these explanatory variables, and that even absolute or household income, otherwise always positively related to happiness, may lose statistical significance. Here we test this hypothesis with a large UK panel (British Household Panel Survey and Understanding Society) for 1996–2017, split by age and retirement status, and find, contrary to previous results, that rank, household income and reference income are all usually important explanatory variables, but with significant differences between subgroups. This finding holds when rank is in its often-used relative form, and also with absolute rank.

Buying Happiness in an Unequal World: Rank of Income More Strongly Predicts Well-Being in More Unequal Countries

Does income rank matter more for well-being in more unequal countries? Using more than 160,000 observations from 24 countries worldwide, we replicate previous studies and show that the ranked position of an individual’s income strongly predicts life evaluation and positive daily emotional experiences, whereas absolute and reference income generally have weak or no effects. Furthermore, we find the association between income rank and an individual’s well-being to be significantly larger in countries where income inequality, represented by the share of taxable income held by the top 1% of income earners, is high. These results are robust to using an alternative measure of income inequality and different reference group specifications. Our findings suggest that people in more unequal societies place greater weight on the pursuit of higher income ranks, which may contribute to enduring income inequality in places where greater well-being can be bought from moving up the income ladder.

People do not adapt. New analyses of the dynamic effects of own and reference income on life satisfaction

Do people adapt to changes in income? In contradiction to much of the previous literature, I find no evidence of adaptation to income in GSOEP (1984-2015) and UKHLS (1996-2017) data. Furthermore, I find that people also do not adapt to changes in reference income. Instead, reference income effects may be subject to reinforcement over time. Following the empirical approach of Vendrik (2013), I obtain these findings by estimating life satisfaction equations in which contemporaneous and lagged terms for a respondent’s own household income and their estimated reference income are simultaneously entered. Additionally, I instrument for own income and include lags of a large set of controls. What was found to be adaptation to raw household income in previous studies turns out tohave been driven by reinforcement of an initially small negative effect of household size that grows large over time. Implications of this result for the estimation of equivalence scales with subjective data are discussed.

Assessing the risk profile of dairy farms: application of the Income Stabilisation Tool in Italy

Purpose This paper bridges the gap between theory and practice in the application of the Income Stabilisation Tool (IST). With an application to the dairy sector, the purpose of this paper is to propose methodology for the quantification of reference income when farm structural change occurs and estimate the role of farm attributes on the probability of income loss, offering an ex ante evaluation of farm resilience to risk. Design/methodology/approach Based on a balanced Farm Accountancy Network farm-level panel ranging from 2008 to 2014, three hypotheses of reference income calculation are tested to assess whether farms structural changes over the years significantly affect the level of IST indemnification. The role of farm characteristics on the probability of an income reduction is then evaluated by estimating a multinomial logit model. Findings Results show that farms’ structural changes significantly affect IST indemnities and need to be considered in calculating the reference income. The estimated model suggests that farm characteristics significantly affect the probability of a severe income drop and hence risk resilience. Extensive livestock systems seem to reduce the probability of an income drop, while farms in upland areas managed by young farmers seem to experience increased risk exposure. Originality/value The research provides one of the first attempts to define risk profile of dairy farms by modelling the probability of an income reduction on observable attributes. Indeed, among different sectors, dairy farms emerge as the main candidates for the application of the IST.