Abstract
Previous research on informal spousal caregiving has documented a reduced life satisfaction among caregivers, but it has rarely considered the dynamics over time, the mechanisms that drive these effects, and different types of transitions out of caregiving. This study aims to fill this gap by focusing on spousal caregivers’ life satisfaction before, during, and after episodes of caregiving. We apply a dynamic perspective which enables further understanding of the adaptation of caregivers to the changed situation over time, distinction of different subdimensions of satisfaction, and consideration of the transition out of caregiving that can be driven by death or recovery of the partner. Using rich panel data of the German Socio-Economic Panel Study (GSOEP), we find that transition into caregiving has a lasting negative impact on the caregivers’ life satisfaction. This decline in satisfaction seems to be mainly driven by negative impacts of caregiving on leisure time. For transitioning out of caregiving, the results depend upon the reasons for ending this task. While we find no changes in caregivers’ life or domain satisfaction after recovery of the care recipient, our results show that transitions out of caregiving caused by the death of the dependent partner go along with increases in well-being after a first negative shock.
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Notes
The response rates on the household level relate to the refreshments samples E (1998: 53.6%), F (2000: 50.9%), H (2006: 40.0%), J (2011: 33.0%), and K (2012: 34.7%). The percentage of successful follow-ups range within the years 1999 and 2016 between 95.2 and 99.8% per sample and year. The probability of panel attrition for (spousal) caregivers is not reported by the GSOEP, but non-response rates for some subgroups may give a hint for the attrition among caregivers: the probability of unsuccessful follow-ups is higher for widowed persons and single households (Kroh et al. 2018). Hence, we cannot fully rule out biases due to panel attrition, especially for analyses focusing on transitions out of caregiving due to the death of the partner. However, the reported group differences in nonresponse are not very large making it seem unlikely that panel attrition severely biases results, especially after controlling for time-constant unobserved heterogeneity in fixed effects models.
Using the association between two subjectively assessed variables like life satisfaction and domain satisfaction is not free of problems, because interpersonally varying rating standards might simultaneously affect both subjective indicators. Such an approach might overestimate the association between the two factors. However, investigating the conditional association of caregiving and life satisfaction after controlling for domain satisfaction in fixed effects models (see Sect. 3.2) might help to get rid of such person-specific differences, while models with only one subjective indicator do not suffer from the aforementioned problem.
A more fine-grained operationalization for longer caregiving episodes does not make sense as the average duration of caregiving in our data is about 2 years (for a similar duration of 4 years, see Jacobs et al. 2016).
We assume that caregiving with a data gap of no longer than 1 year and the same caregiver-care recipient-constellation belongs to the same episode. We treat gaps longer than 1 year as repeated events and exclude person-years for the “second” event. Changing care recipients were also treated as repeated events.
However, note that estimates for later years after the transition out of caregiving only map related changes in life satisfaction for individuals which are involved in informal caregiving for such a long time. Hence, these findings cannot be necessarily generalized to persons with short-term informal caregiving.
Nonetheless, we controlled in robustness checks for education, employment status, marital status, household size, presence of children in the household, monthly household income, and self-rated health. As can be seen in Tables 11 and 12 in the Appendix—despite substantial loss of observations due to missing data in these controls—estimates for the effects of transitions into caregiving remained remarkably stable. For the effects of transitions out of caregiving, the main conclusions about adaptation also remain the same, in particular that caregivers quickly recover within 3 to 5 years. However, coefficients for transitions out of caregiving due to death of partner change quite a bit after additional covariate adjustment and the negative effect of death of partner in the year of the transitions becomes insignificant. Note that besides confounding two other things might cause this change in coefficients: First, the number of caregivers drops from 288 transitions out of caregiving by 35% to 187. Model 2 in Table 12 illustrates that a substantial part of this change in point estimates is due to the reduction in the sample. Second, household size and marital status capture part of the effect of the dummy impact function for death of partner. After dropping these two variables, coefficients clearly move in the direction of the original results (see Models 4 and 5 in Table 12).
Furthermore, please note that fixed effects models treat the fixed effects as nuisance parameters. Thus, when calculating the overall R2, the amount of variance explained by the fixed effects is not taken into account. Using Stata’s areg command to determine the total relative amount of explained variance, we find an overall R2 of 0.56 for model 2. This illustrates the fact that fixed effects models control for unobserved heterogeneity even if such time-constant variables are not directly included in the model.
Controlling for potential effects of anticipation in the year before the transition (analyses available upon request) reveals an even stronger association as the increasing dependency of the partner for care and support cast their shadows ahead and decrease overall life satisfaction of the future caregiver by 0.2 scale points in the year before the transition.
However, please note that this might overestimate the actual extent of mediation for two reasons. First, we cannot control for unobserved heterogeneity using fixed effects. Second, the model relies on two subjective satisfaction measures potentially sharing common causes.
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Acknowledgements
The data were provided by the German Institute for Economic Research (DIW Berlin). A detailed description of the GSOEP can be found in Wagner et al. (2007). We thank the editors Milena Nikolova and Stephanie Roussow as well as the reviewers for helpful comments.
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Gerlich, R., Wolbring, T. “In Good Times and in Bad, in Sickness and in Health”: A Longitudinal Analysis on Spousal Caregiving and Life Satisfaction. J Happiness Stud 22, 1481–1516 (2021). https://doi.org/10.1007/s10902-020-00281-1
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DOI: https://doi.org/10.1007/s10902-020-00281-1