Population-Level Variability of Happiness Trends in the United States.

In this paper we examine population-level variability of aging and cohort trends in happiness in the United States. Following the technique outlined by Fosse (2019), we focus on estimating diachronic age and cohort trends, which captures the joint age-period and cohort-period effects in a set of time-series, cross-sectional data. The main advantage of this technique is that it is based on estimates that are not subject to the identification problem. To date the great majority of studies on happiness rely on ad hoc, implicit constraints, which can generate highly misleading temporal estimates. We find that diachronic age and cohort trends are large, negative, and statistically significant. We also examine howthese trends vary across a large number of variables, including ethnicity, fertility, region, gender, and social class. We find substantial population-level heterogeneity in both diachronic age and cohort trends, with the majority of interaction effects large and statistically significant. In general, results indicate that diachronic age and cohort trends tend to amplify the beneficial effects of variables show in previous studies to increase happiness. However, notably we find that having children and getting married incurs diminished benefits for the diachronic cohort trend. We conclude with suggestions for further research on temporal trends in happiness. [ABSTRACT FROM AUTHOR]