3.3.1 Data and Sample
I used the DEAS, a nationwide survey of the German population age 40 and older. The survey includes various baseline samples (1996, 2002, 2008, and 2014) and several follow-ups (Klaus et al., 2017). For this study, I applied a) the baseline 1996 sample and its 2002 follow-up, b) the baseline 2002 sample and its 2008 follow-up, and c) the baseline 2008 sample and its 2011 and 2014 follow-ups. In the latter subsample, follow-ups were conducted every three years instead of every six years. Therefore, I took advantage of both follow-ups. The main survey was conducted through personal interviews. Participants were then asked to respond to an additional questionnaire that collected sensitive information. Life satisfaction was measured in this drop- off questionnaire.
The pooled baseline sample contained 14,127 individuals. In the first step, I excluded participants for four reasons: 1) no drop-off questionnaire (14.2%), 2) no follow-up interview available (58.3%), 3) item nonresponse (14.4%), and 4) no full information in baseline survey (0.04%). After exclusion, the sample contained 4.133 individuals with 9,611 person-years of observation. Descriptive sample characteristics are presented in Table 1. Women comprise 49.0 % of the sample, and the mean age is 61.6 (SD=11.3). In the second step, I generated two samples suited for the corresponding analysis. For Analytical Sample 1, I excluded those observations that indicated an exit from grandchild care and volunteering. Thus, a genuine within-person effect can be identified that only takes into account the initiation of these activities. This procedure further reduced the sample by 14.9%. In Analytical Sample 2, I excluded those observations that indicated an exit from grandparental childcare and informal
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care. In this case, the sample was further reduced by 16.4%. The sample selection process is described and visualized in Figure S1 in the Online Appendix.
3.3.2 Measures
Life satisfaction is measured using five items from the satisfaction with life scale developed by Diener (2000). The Items are: In most ways my life is close to my ideal; The conditions of my life are excellent; I am satisfied with my life; So far, I have gotten things I want in life; If I could live my life over, I would change almost nothing.” The variable is calculated as the mean value of the scale if participants responded to at least to three out of five items. The DEAS applies a 5-point scale with the following response categories: “strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree.” Higher variable values indicate higher life satisfaction levels.
The DEAS measures grandchild care with a single item: “Do you look after or supervise other people’s children privately, e.g., your grandchildren or the children of siblings, neighbors, friends, or acquaintances?” Multiple answers are possible. The main explanatory variable— grandchild care—appears as 1 if participants identify themselves as grandchild caregivers. It appears as 0 if participants provide no care at all or if they provide care exclusively for nongrandchildren. The models included a second variable, childcare (nongrandchildren), to control for participants who provide care for nongrandchildren. In this case, the variable appears as 1. It appears as 0 if participants provide no care at all or if they provide care exclusively for grandchildren.
Formal volunteering is measured as a dummy variable and appears as 1 if observations volunteer in groups or organizations (0=no volunteering). Informal care is also measured as a dummy variable and appears as 1 if participants provide care for people with needs (0=applies no informal care).
All models control for a set of relevant time-varying covariates. They include dummies for the number of children including the categories no children (reference category), 1-2 children, and 3+ children, and the number of grandchildren including the categories no grandchildren, 1-2 grandchildren, and 3+ grandchildren. Partnership status is captured with a dummy variable (0=no relationship; 1=in relationship). Self-rated health is measured as a single item coded from 1= very bad to 5= very good. Employment status is captured with 3 dummy variables including employed (reference category), not employed, and retired. Furthermore, the models include
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dummies for age categories and dummies capturing the interview year to adjust for period effects.
3.3.3 Analytical Strategy
As discussed above, some theoretical and empirical research acknowledges that privileged populations are more likely to provide grandchild care and engage in productive activities in later life. To approach a causal effect of grandchild care, I adjusted for such a selection bias in the regression analysis. The fixed-effects panel approach is an acknowledged standard for controlling for unobservable time-invariant individual factors (e.g., class, ethnicity, and education) that might cause a selection effect (Brüderl & Ludwig, 2015). Therefore, the fixed- effects model extracts unit-specific means and applies only with within-person variation. This allowed me to make statements about within-person changes throughout the observed period. Within-person changes in activity arrangements are presented in Table 2. All analyses are stratified by gender.
The models include interaction effects to estimate the effects of activity combinations. For each combination of grandchild care and another activity, I run separate models. In a recent working paper (Giesselmann & Schmidt-Catran, 2018), the authors point out that standard interaction terms in the fixed-effects approach do not identify a genuine within-person effect. For the purposes of this analysis, I address the problem in a simple way because each model deals with two categorical variables. Instead of multiplying the two variables with each other, it is possible to include indicators for each combination of the two variables (Giesselmann & Schmidt- Catran, 2018). The interpretation of the coefficients is explained in the results section.
I addressed selectivity due to panel and drop-off attrition and nonresponse with inverse probability weighting (Vandecasteele & Debels, 2006). I used logistic regression models to estimate the probability that a person participated in the baseline survey and survived the selection process to generate the analytical samples (see Figure A1 in the Online Appendix). I generate the weights as the inverse of the predicted probabilities and standardized to the mean value 1. Thus, the number of observations in the weighted analysis equals the number of observations in the unweighted analysis. The logistic regression models are presented in the Online Appendix (Table A1). I estimate separate models for men and women, including all control variables applied in the actual analysis. In addition, I take educational status into account.
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