https://orcid.org/0000-0002-0049-7833
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ABSTRACT
The literature on how family status and health in later life relate is extensive. Although research has focused on the health effects of grandparenthood and grandparenting, explorations of whether ageing without children can lead to mental health impairments have achieved mixed results. We bridge empirical traditions to investigate the relationship between family status and mental health in Europe by using data from the Survey of Health, Ageing and Retirement in Europe (SHARE) and sampling roughly 160,000 men and women aged 50–89 from 17 European countries. Mental health is evaluated through depressive symptoms on the EURO-D scale and compared between childless or grandchildless people and grandparents at different ages. To identify the association between (grand)parenthood and mental health status net of confounders, we perform inverse probability-weighted regression adjustment (IPWRA). The rich information SHARE provides facilitates considering the common factors that may influence (grand)parenthood and depressive symptoms. Results show that the three groups hardly differ in mental health: grandchildless men and grandfathers reported fewer depressive symptoms, if any, than childless men aged 70–79. Overall, while mental health does not seem to relate to family status per se, it could be crucial when accounting for the entire individual life course.
Acknowledgement
This paper uses data from SHARE Waves 1, 2, 3, 4, 5, 6, 7, 8 and 9 (DOIs: 10.6103/SHARE.w1.800, 10.6103/SHARE.w2.800, 10.6103/SHARE.w3.800, 10.6103/SHARE.w4.800, 10.6103/SHARE.w5.800, 10.6103/SHARE.w6.800, 10.6103/SHARE.w7.800, 10.6103/SHARE.w8.800, 10.6103/SHARE.w8ca.800, 10.6103/SHARE.w9ca800), see Börsch-Supan et al. (2013) for methodological details.
Declarations
All authors whose names appear on the submission contributed equally to the manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 Our final sample includes N = 159,115 observations (n, individuals = 46,325), and results from the following procedure. Overall, SHARE includes n = 139,620 and N = 383,647. First, we excluded from the analytical sample individuals who answered the SHARELIFE modules in neither wave 3 nor wave 7 (N = 75,586, n = 48,421). Second, we excluded individuals who did not provide information on their childhood situation (N = 67,267, n = 30,396); over half of missing records relate to respondents’ father’s education, which was asked only in waves 5, 6, or 8. We also deleted individuals who were not born in the country of interview (or had missing information on their birthplace, N = 24,265, n = 6,137). In addition, we deleted individuals who did not provide information on Euro-D (N = 41,357). The high number of missing observations result from wave 7, as only respondents surveyed in wave 3 (SHARELIFE Interview) were asked this question in wave 7.
Further, we deleted people who were either younger than 50 or older than 89 (N = 4,537) and a listwise deletion was performed for people lacking information on the remaining covariates (N = 2,153). As the final step, we examined the sample size for each country and retained only those countries where there were at least 100 observations for each family status. Therefore, we excluded Bulgaria, Cyprus, Finland, Hungary, Israel, Latvia, Lithuania, Malta, Portugal, Romania and Slovakia (N = 9,367).
2 Including in the model a variable measuring partnership formation in more detail (e.g. ever had a partner: yes/no) would violate the overlap assumption (see ‘Analytical Strategy’). Childless individuals are by far the least likely to have formed a partnership during their life course. Conditioning on an explicit partnership variable would make the probability of receiving the treatment significantly different for the three groups we formed. For the cohort under study, leaving the parental home often corresponded with starting a family (Angelini & Laferrère, Citation2013), making living status at the age of 20 a reasonable proxy for partnership formation.
3 We implemented the IPWRA approach with the Stata© command teffects ipwra.