Age and Life Satisfaction: Getting Control Variables under Control

Bartram, David

doi:10.1177/0038038520926871

Cited by 67 publications

(67 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In terms of other predictors, the current paper uses key variables that have proved to be significant determinants of SWB rather than relying on "usual suspects" (Bartram, 2021). This includes age and age squared, gender, marital status, the presence of child(ren), selfreported health status, main activity (occupations), and income (e.g., Aassve et al, 2012;Akaeda, 2019;Bartram, 2021;D'Ambrosio et al, 2020;Helliwell et al, 2020;Perelli-Harris et al, 2019;Steptoe et al, 2015;van der Meer, 2014). Indeed, Cuñado and de Gracia (2012) detected a substantial association between happiness and these attributes in their empirical analysis using the ESS data.…”

Section: Variablesmentioning

confidence: 99%

Does Education Make People Happy? Spotlighting the Overlooked Societal Condition

Araki¹

2021

J Happiness Stud

View full text Add to dashboard Cite

The association between education and subjective well-being has long been investigated by social scientists. However, prior studies have paid inadequate attention to the influence of societal-level educational expansion and skills diffusion. In this article, multilevel regression analyses, using internationally comparable data for over 48,000 individuals in 24 countries, detect the overall positive linkage between educational attainment and life satisfaction. Nevertheless, this relationship is undermined due to the larger degree of skills diffusion at the societal level, and no longer confirmed once labor market outcomes are accounted for. Meanwhile, the extent of skills diffusion per se is positively and substantially associated with people’s subjective well-being even after adjusting for key individual-level and country-level predictors, whereas other societal conditions including GDP, Gini coefficients, safety, civic engagement, and educational expansion do not indicate significant links with life satisfaction in the current analysis. Given that recent research suggests skills diffusion promotes the formation of meritocratic social systems, one may argue it is the process of fairer rewards allocation underpinned by skills diffusion, rather than the status quo of macroeconomy, economic inequality, social stability, and educational opportunities as such, that matters more to people’s subjective well-being.

show abstract

Section: Variablesmentioning

confidence: 99%

Does Education Make People Happy? Spotlighting the Overlooked Societal Condition

Araki¹

2021

J Happiness Stud

View full text Add to dashboard Cite

show abstract

“…There is plenty of empirical evidence that has been marshaled in favor of this interpretation (most recently, Blanchflower, 2021). At the same time, important critiques have been raised against the U-curve as well, arguing that the empirical evidence is weaker than it appears (Galambos et al, 2020) and that the U-curve appears only after inappropriate adjustments to the data have been made (Bartram, 2021;Glenn, 2009). While the variations in the shape of the relationship between age and happiness across different countries and time periods have been explored (Bittmann, 2021), how the relationship changes for people with different incomes is a question that has not received a lot of scientific attention (Hsieh, 2011).…”

Section: Introductionmentioning

confidence: 99%

The Relationship Between Age and Happiness Varies by Income

Toshkov

2021

J Happiness Stud

View full text Add to dashboard Cite

The link between age and happiness has been the subject of numerous studies. It is still a matter of controversy whether the relationship is U-shaped, with happiness declining after youth before bouncing back in old age, or not. While the effect of age has been examined conditional on income and other socio-demographic variables, so far, the interactions between age and income have remained insufficiently explored. Using data from the European Social Survey, this article shows that the nature of the relationship between age and happiness varies strongly with different levels of relative income. People in the lowest decile of the income distribution experience a ‘hockey stick’: a deep decline in self-reported happiness until around age 50–55 and a small bounce back in old age. The classic U-curve is found mostly in the middle-income ranks. For people at the top of the income distribution, average happiness does not vary much with age. These results demonstrate the important role of income in moderating the relationship between age and happiness.

show abstract

“…What variables should be included as controls? A safe choice is to include age and sex (Bartram 2021). There is no ambiguity about causal direction for those variables: if there are any causal relationships here, they would run from age and sex to unemployment and happiness.…”

Section: Demonstrationmentioning

confidence: 99%

Cross-Sectional Model-Building for Research on Subjective Well-Being: Gaining Clarity on Control Variables

Bartram

2021

Soc Indic Res

Self Cite

View full text Add to dashboard Cite

Happiness/well-being researchers who use quantitative analysis often do not give persuasive reasons why particular variables should be included as controls in their cross-sectional models. One commonly sees notions of a “standard set” of controls, or the “usual suspects”, etc. These notions are not coherent and can lead to results that are significantly biased with respect to a genuine causal relationship.This article presents some core principles for making more effective decisions of that sort. The contribution is to introduce a framework (the “causal revolution”, e.g. Pearl and Mackenzie 2018) unfamiliar to many social scientists (though well established in epidemiology) and to show how it can be put into practice for empirical analysis of causal questions. In simplified form, the core principles are: control for confounding variables, and do not control for intervening variables or colliders. A more comprehensive approach uses directed acyclic graphs (DAGs) to discern models that meet a minimum/efficient criterion for identification of causal effects.The article demonstrates this mode of analysis via a stylized investigation of the effect of unemployment on happiness. Most researchers would include other determinants of happiness as controls for this purpose. One such determinant is income—but income is an intervening variable in the path from unemployment to happiness, and including it leads to substantial bias. Other commonly-used variables are simply unnecessary, e.g. religiosity and sex. From this perspective, identifying the effect of unemployment on happiness requires controlling only for age and education; a small (parsimonious) model is evidently preferable to a more complex one in this instance.

show abstract

Age and Life Satisfaction: Getting Control Variables under Control

Cited by 67 publications

References 32 publications

Does Education Make People Happy? Spotlighting the Overlooked Societal Condition

Does Education Make People Happy? Spotlighting the Overlooked Societal Condition

The Relationship Between Age and Happiness Varies by Income

Cross-Sectional Model-Building for Research on Subjective Well-Being: Gaining Clarity on Control Variables

Contact Info

Product

Resources

About