People do not adapt to income changes: A re-evaluation of the dynamic effects of (reference) income on life satisfaction with GSOEP and UKHLS data

Do people adapt to changes in income? This paper shows that there is no evidence of adaptation to income in GSOEP (1984-2015) and UKHLS (1996-2015) data. Following the empirical approach of Vendrik (2013), I arrive at this surprising answer by estimating (dynamic) life satisfaction equations, in which I simultaneously enter contemporaneous and lagged terms for a respondent’s own household income and their estimated reference income. Additionally, I instrument for own income and include lags of a large set of controls. 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. To explain my findings, a comprehensive account of the puzzling and often divergent results of Ferrer-i-Carbonell and Van Praag (2008), Binder and Coad (2010), Di Tella et al. (2010), and Pfaff (2013) is given. What was found to be adaptation to raw household income in these studies turns out to have 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

People do not adapt to income changes: A re-evaluation of the dynamic effects of (reference) income on life satisfaction with GSOEP and UKHLS data

Do people adapt to changes in income? This paper shows that there is no evidence of adaptation to income in GSOEP (1984-2015) and UKHLS (1996-2015) data. Following the empirical approach of Vendrik (2013), I arrive at this surprising answer by estimating (dynamic) life satisfaction equations, in which I simultaneously enter contemporaneous and lagged terms for a respondent’s own household income and their estimated reference income. Additionally, I instrument for own income and include lags of a large set of controls. 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. To explain my findings, a comprehensive account of the puzzling and often divergent results of Ferrer-i-Carbonell and Van Praag (2008), Binder and Coad (2010), Di Tella et al. (2010), and Pfaff (2013) is given. What was found to be adaptation to raw household income in these studies turns out to have 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

Using memories to assess the intrapersonal comparability of wellbeing reports

Research on subjective wellbeing typically assumes that responses to survey questions are comparable across respondents and across time. Unfortunately, if this assumption is violated, standard methods in empirical research may mislead. I address this concern with three contributions. First, I give a theoretical analysis of the extent and direction of bias that results from violations of this assumption. Second, I propose to use respondents’ memories of past life satisfaction to estimate and thereby to correct for differentials in scale use. Third, using the proposed approach, I test whether wellbeing reports are intrapersonally comparable across time. Using British panel data, I find that the direction in which explanatory variables affect latent satisfaction is typically the same as the direction in which scale use is affected. Unemployment and widowhood have particularly strong effects on scale use. Nevertheless, scale shifts are generally not large enough to affect the sign or statistical significance of estimates compared to models that do not account for scale shifts. Finally, although discussed in the context of life satisfaction scales, the proposed approach is applicable to a wide range of other subjectively reported constructs

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 to have 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

The scientific value of numerical measures of human feelings

Human feelings measured in integers (my happiness is an 8 out of 10, my pain 2 out of 6) have no objective scientific basis. They are “made-up” numbers on a scale that does not exist. Yet such data are extensively collected—despite criticism from, especially, economists—by governments and international organizations. We examine this paradox. We draw upon longitudinal information on the feelings and decisions of tens of thousands of randomly sampled citizens followed through time over four decades in three countries (n = 700,000 approximately). First, we show that a single feelings integer has greater predictive power than does a combined set of economic and social variables. Second, there is a clear inverse relationship between feelings integers and subsequent get-me-out-of-here actions (in the domain of neighborhoods, partners, jobs, and hospital visits). Third, this feelings-to-actions relationship takes a generic form, is consistently replicable, and is fairly close to linear in structure. Therefore, it seems that human beings can successfully operationalize an integer scale for feelings even though there is no true scale. How individuals are able to achieve this is not currently known. The implied scientific puzzle—an inherently cross-disciplinary one—demands attention

Positional, mobility, and reference effects : how does social class affect life satisfaction in Europe?

In this study, we analyse the effects of social class on life satisfaction. We develop a theoretical framework that shows how social class affects life satisfaction through five different pathways. Informed by this framework, we estimate the direct effects of class destination and class origin, the effect of own intergenerational class mobility, as well as the effects of others’ class position and mobility. To do so, we utilize European Social Survey waves 1 to 5 (2002–2010) and obtain information on life satisfaction as well as destination and origin class for about 80,000 respondents in 32 European countries. We find (i) class destination consistently and strongly structures life satisfaction across Europe, (ii) own class mobility has a significant impact on life satisfaction in Eastern Europe, as does (iii) the class mobility of others. The last finding points to the hitherto neglected importance of reference effects when considering the impact of social class on life satisfaction

Equality of opportunity is linked to lower mortality in Europe

Background: This study investigates if intergenerational equality of opportunity is linked to mortality in 30 European countries. Equality of opportunity may lead to greater returns on health investments and, consequently, improved health outcomes. In turn, a perceived lack of fairness in the distribution of life chances and limited possibilities for upward intergenerational mobility can cause anxiety among individuals and gradually compromise their health. Methods: We used information on 163 467 individuals' and their parents' Socio-Economic Index of Occupational Status from a large survey data set-the European Social Survey-to generate three complementary measures of equality of opportunity. We then linked these to administrative data on total, gender-specific and cause-specific mortality rates assembled by Eurostat from the national statistical offices. Results: We found that lower equality of opportunity, measured by the attainment of individuals from the lowest and highest quartiles of socioeconomic status and by the overall intergenerational correlation in socioeconomic status, was related to higher mortality rates, particularly in relation to diseases of the nervous system and the sense organs, diseases of the respiratory system and external causes of mortality. Our measures of equality of opportunity were more consistently linked with mortality of men than women. Conclusion: Equality of opportunity may be an important explanation of mortality that warrants further research. Measures that aim at facilitating intergenerational social mobility can be justified not only via normative considerations of equality of opportunity but also in terms of individuals' chances to enjoy healthy lives

Human wellbeing and machine learning

There is a vast literature on the determinants of subjective wellbeing. International organisations and statistical offices are now collecting such survey data at scale. However, standard regression models explain surprisingly little of the variation in wellbeing, limiting our ability to predict it. In response, we here assess the potential of Machine Learning (ML) to help us better understand wellbeing. We analyse wellbeing data on over a million respondents from Germany, the UK, and the United States. In terms of predictive power, our ML approaches perform better than traditional models. Although the size of the improvement is small in absolute terms, it is substantial when compared to that of key variables like health. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms - i.e. material conditions, health, and meaningful social relations - are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches