A Note on Empirical Studies of Life-Satisfaction: Unhappy with Semiparametrics?

Ranjbar, Setareh; Sperlich, Stefan

doi:10.1007/s10902-019-00165-z

Cited by 11 publications

(13 citation statements)

References 36 publications

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“…Wunder et al (2013) and Ranjbar and Sperlich (2019) both use semi-parametric methods on German SOEP panel data to examine the relation between age and well-being. They both get the same results.…”

Section: Longitudinal Data and Differential Response By Agementioning

confidence: 99%

Is happiness U-shaped everywhere? Age and subjective well-being in 145 countries

2020

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A large empirical literature has debated the existence of a U-shaped happiness-age curve. This paper reexamines the relationship between various measures of well-being and age in 145 countries, including 109 developing countries, controlling for education and marital and labor force status, among others, on samples of individuals under the age of 70. The U-shape of the curve is forcefully confirmed, with an age minimum, or nadir, in midlife around age 50 in separate analyses for developing and advanced countries as well as for the continent of Africa. The happiness curve seems to be everywhere. While panel data are largely unavailable for this issue, and the findings using such data largely confirm the cross-section results, the paper discusses insights on why cohort effects do not drive the findings. I find the age of the minima has risen over time in Europe and the USA.

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Section: Longitudinal Data and Differential Response By Agementioning

confidence: 99%

Is happiness U-shaped everywhere? Age and subjective well-being in 145 countries

2020

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“…Another important issue is the correct specifi cation of the functional form of the outcome trajectory. Misspecifi cation of the age effects would imply misrepresented potential outcome trajectories, which in turn biases our estimates of the treatment effect (the so-called "bias transfer", see Ranjbar and Sperlich (2020) for formal proofs). Therefore, we should always take care regarding the underlying functional form assumptions of age effects.…”

Section: Specifi Cation Of the Outcome Trajectorymentioning

confidence: 99%

What You Need to Know When Estimating Impact Functions with Panel Data for Demographic Research

Ludwig

Brüderl²

2021

CPoS

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The estimation of impact functions – that is the time-varying causal effect of a dichotomous treatment (e.g., marriage, divorce, parenthood) on outcomes (e.g., earnings, well-being, health) – has become a standard procedure in demographic applications. The basic methodology of estimating impact functions with panel data and fixed-effects regressions is now widely known. However, many researchers may not be fully aware of the methodological subtleties of the approach, which may lead to biased estimates of the impact function. In this paper, we highlight potential pitfalls and provide guidance on how to avoid these in practice. We demonstrate these issues with exemplary analyses, using data from the German Family Panel (pairfam) study and estimating the effect of motherhood on life satisfaction. * This article belongs to a special issue on “Identification of causal mechanisms in demographic research: The contribution of panel data”.

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“…This finding reflects the differential impact of key and major life events that happened at different stages of life (marriage, giving birth, employment, etc.). Midlife crisis, centering on major life disruptions seen as typical to this life stage, such as job loss, divorce, or the death of parents, has been found to be significantly correlated with a lower level of life satisfaction, whereas people become more satisfied after they retired 51 , 52 .…”

Section: Discussionmentioning

confidence: 99%

Trends and patterns of life satisfaction and its relationship with social support in Canada, 2009 to 2018

D’Arcy

et al. 2022

Sci Rep

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The present study aims to explore the trends and patterns of life satisfaction in Canada from 2009 to 2018 and to examine changes in the associations between social support and life satisfaction over time. Data were from ten annual Canadian Community Health Surveys (CCHS). Each survey represents 97% of the Canadian population. Point estimates and 95% confidence intervals (CIs) of life satisfaction were calculated at the population level. Generalized linear regression was used to explore the relationship between life satisfaction and social support both nationally and in different population subgroups. The annual life satisfaction score gradually increased both at national and provincial levels from 2009 to 2018. Individuals who were women, aged between 12 and 19 years, living in rural areas, were most satisfied with their lives. There was a positive correlation between social support and life satisfaction for the provinces and the study years for which information on social support was available. Our findings suggest strengthening social support could be a public health target for promoting greater life satisfaction. Timely availability and analysis of life satisfaction and social support data could better inform policy and promote wellbeing at a population level.

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A Note on Empirical Studies of Life-Satisfaction: Unhappy with Semiparametrics?

Cited by 11 publications

References 36 publications

Is happiness U-shaped everywhere? Age and subjective well-being in 145 countries

Is happiness U-shaped everywhere? Age and subjective well-being in 145 countries

What You Need to Know When Estimating Impact Functions with Panel Data for Demographic Research

Trends and patterns of life satisfaction and its relationship with social support in Canada, 2009 to 2018

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