Modelling the socio-economic determinants of fertility: a mediation analysis using the parametric g-formula

Bijlsma, Maarten J.; Wilson, Ben

doi:10.4054/mpidr-wp-2017-013

Cited by 3 publications

(4 citation statements)

References 67 publications

(100 reference statements)

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“…This is accomplished in the simulation step of the g-formula, including a 500-iteration bootstrap to produce standard errors and confidence intervals ( Efron & Tibshirani, 1994 ). The simulation process follows that of other longitudinal g-formula implementations elsewhere ( Bijlsma et al, 2017 ; Bijlsma & Wilson, 2020 ; Robins, 1986 ). By taking the differences between the intervention scenario and the natural course scenario, we calculate the total effect of our intervention of postponing retirement to age 67 ( Wang & Arah, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

“…The parametric g-formula offers a solution to these methodological issues. The g-formula is an innovative statistical approach that enables analysis of time-varying processes, while allowing for selection, reverse causality, and mediation ( Bijlsma & Wilson, 2020 ; VanderWeele and Tchetgen Tchetgen, 2017 , Vanderweele and Tchetgen Tchetgen, 2017 ; Wang & Arah, 2015 ). As such, the g-formula is a statistically flexible approach that allows us to examine the interdependent influences of life-course processes – such as education, partnership, health, and labor force participation – on later-life cognitive function, irrespective of the functional form that the relationships of mutual influence may take ( De Stavola et al, 2015 ; VanderWeele et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

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Does postponing retirement affect cognitive function? A counterfactual experiment to disentangle life course risk factors

Hale

Bijlsma

Lorenti

2021

SSM - Population Health

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Evidence suggests that contemporaneous labor force participation affects cognitive function; however, it is unclear whether it is employment itself or endogenous factors related to individuals’ likelihood of employment that protects against cognitive decline. We exploit innovations in counterfactual causal inference to disentangle the effect of postponing retirement on later-life cognitive function from the effects of other life-course factors. With the U.S. Health and Retirement Study (1996–2014, n = 20,469), we use the parametric g-formula to estimate the effect of postponing retirement to age 67. We also study whether the benefit of postponing retirement is affected by gender, education, and/or occupation, and whether retirement affects cognitive function through depressive symptoms or comorbidities. We find that postponing retirement is protective against cognitive decline, accounting for other life-course factors (population: 0.34, 95% confidence interval (CI): 0.20,0.47; individual: 0.43, 95% CI: 0.26,0.60). The extent of the protective effect depends on subgroup, with the highest educated experiencing the greatest mitigation of cognitive decline (individual: 50%, 95% CI: 32%,71%). By using innovative models that better reflect the empirical reality of interconnected life-course processes, this work makes progress in understanding how retirement affects cognitive function.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Does postponing retirement affect cognitive function? A counterfactual experiment to disentangle life course risk factors

Hale

Bijlsma

Lorenti

2021

SSM - Population Health

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“…Indeed, many recent papers using mediation and decomposition analyses have found contribution estimates of >100 or <0. 2 , 19 , 22 Contribution estimates of <0 or >1 could also occur due to imprecision in the underlying estimates. For this reason, it is important to present and interpret such estimates with their accompanying standard error.…”

Section: A Counterfactual Approach To Decompositionmentioning

confidence: 99%

Educational note: causal decomposition of population health differences using Monte Carlo integration and the g-formula

Sudharsanan

Bijlsma

2021

International Journal of Epidemiology

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One key objective of the population health sciences is to understand why one social group has different levels of health and well-being compared with another. Whereas several methods have been developed in economics, sociology, demography, and epidemiology to answer these types of questions, a recent method introduced by Jackson and VanderWeele (2018) provided an update to decompositions by anchoring them within causal inference theory. In this paper, we demonstrate how to implement the causal decomposition using Monte Carlo integration and the parametric g-formula. Causal decomposition can help to identify the sources of differences across populations and provide researchers with a way to move beyond estimating inequalities to explaining them and determining what can be done to reduce health disparities. Our implementation approach can easily and flexibly be applied for different types of outcome and explanatory variables without having to derive decomposition equations. We describe the concepts of the approach and the practical steps and considerations needed to implement it. We then walk through a worked example in which we investigate the contribution of smoking to sex differences in mortality in South Korea. For this example, we provide both pseudocode and R code using our package, cfdecomp. Ultimately, we outline how to implement a very general decomposition algorithm that is grounded in counterfactual theory but still easy to apply to a wide range of situations.

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“…Increasing differences in childbearing outcomes may contribute to the reproduction of socio‐economic inequalities over the life course. That is, the association between socio‐economic well‐being (SEWB) and (foregoing) childbearing may be bidirectional (Bijlsma & Wilson, 2020; Plotnick, 2009; Umberson et al, 2010), and its direction might depend on the life course stage. In other words, SEWB may influence childbearing intentions and behaviours earlier in life, and childbearing outcomes may in turn influence SEWB in later life.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Multivariate Hierarchical Growth Curve Model to Examine Cumulative Socio-Economic (Dis)Advantage among Childless Adults and Parents

Verkroost¹

2022

Journal of the Royal Statistical Society Series A: Statistics in Society

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Childlessness and socio-economic well-being interact dynamically throughout the life course, possibly resulting in an accumulation of socio-economic (dis)advantage. Methods commonly used to investigate this hypothesis are unable to simultaneously acknowledge that childlessness entails a heterogeneous and processual 'non-event' which interrelates with multiple life domains. I use Bayesian multivariate hierarchical growth curve modelling to facilitate synchronous incorporation of these substantive complexities. I construct prospective interdependent life course trajectories of socio-economic well-being for eventual parents and permanently childless adults; distinguishing voluntary, involuntary, circumstantial and indecisive childlessness. Using 1970 British Cohort Study data, I find that parents and voluntarily childless adults are more satisfied with life than non-voluntarily childless adults. Voluntarily and circumstantially childless women earn most and mothers least, while fathers outearn childless men. (Dis)advantage in economic and male subjective well-being accumulates throughout the life course. Group differences in partnerships, employ-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Modelling the socio-economic determinants of fertility: a mediation analysis using the parametric g-formula

Abstract: Working papers of the Max Planck Institute for Demographic Research receive only limited review. Views or opinions expressed in working papers are attributable to the authors and do not necessarily reflect those of the Institute.

Cited by 3 publications

References 67 publications

Does postponing retirement affect cognitive function? A counterfactual experiment to disentangle life course risk factors

Does postponing retirement affect cognitive function? A counterfactual experiment to disentangle life course risk factors

Educational note: causal decomposition of population health differences using Monte Carlo integration and the g-formula

A Bayesian Multivariate Hierarchical Growth Curve Model to Examine Cumulative Socio-Economic (Dis)Advantage among Childless Adults and Parents

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