Full-time vs. part-time employment: Does it influence frequency of grandparental childcare?
Abstract
The impact of grandparents’ employment on grandparental childcare has been examined repeatedly, but
the findings have so far been inconsistent. We contend that these inconsistencies may have resulted
from variations in model specification and crude measurement of employment status. Furthermore, we
assert that earlier research overlooked gender differences in the ability to combine paid
employment and caregiving as well as variations between maternal and paternal grandparents. We also
question the causal interpretation of earlier findings that were based on cross-sectional data. We
revisit the issue of the impact of the intensity of employment and analyze SHARE data from 19
countries. We find a significant positive association between part-time employment (as compared to
full-time employment) and the frequency of grandparental childcare in a cross-sectional sample, but
only among paternal grandmothers. Capitalizing on the panel component of SHARE, we use a
within-person estimator to show that this association is unlikely to reflect a causal effect of the
intensity of labor market attachment on the frequency of the care of grandchildren, but more
probably results from omitted variable bias. We argue that grandparents most likely to provide
(intensive) childcare are also most likely to adjust their employment in anticipation of
caregiving. The paper documents the usefulness of role strain theory among grandparents and
highlights that part-time jobs may reduce role conflict and may thus make grandparenting a more
easily manageable experience.
Keywords: Grandparents ˖ Childcare ˖ Part-time employment ˖ Intergenerational Solidarity
Word count: 4,993
Introduction: Employment and intergenerational childcare in older age
Intergenerational transfers are gaining importance as a consequence of two population developments.
First, growing longevity results in more years of shared lives between grandparents and
grandchildren (Bengtson 2001). Second, increasingly fragile intragenerational bonds – mirrored, for
instance, in rising divorce rates – emphasize the significance of intergenerational relations, as
people more often turn to other generations within the family for help. Grandparent involvement
with (grand)children is an important form of intergenerational transfer. It facilitates – among
other things – parental participation in the labor force (Gray 2005; Hank and Buber 2009; Lee and
Bauer 2010; Yong 2008) and also seems to increase parents’ odds of having an (additional) offspring
(Aasve et al. 2012).
We argue that the growing policy emphasis on increasing employment among older people (Walker and
Maltby 2012) can bring about – as an unintended consequence – tensions between employment and
grandparental childcare (Geurts et al. 2014; Meyer 2012). Combining caregiving and employment roles
– and thus being in a situation of role conflict (Goode 1960) – may even increase stress levels and
worsen mental health (e.g. Glynn et al. 2009; Opree and Kalmijn 2012; Payne and Doyal 2010). We
suggest, however, that not all forms of employment may have the same impact on childcare provision.
Most importantly, we argue, part-time employment should not represent a significant barrier to
intergenerational caregiving. We examine this claim empirically to see if the continued involvement
of older individuals in economic activity can be compatible with intensive and frequent provision
of grandparental childcare.
Employment and grandparental childcare
There is significant potential for conflict between the roles of paid worker and caregiver, since
both of these roles require a great deal of time and energy (Luo et al. 2012). This article focuses
exclusively on grandparental childcare, although grandparents may also provide other types of both
inter- and intragenerational help. While the role conflict argument is substantively and
theoretically appealing, empirical evidence concerning the tension between grandparental employment
and the provision of grandparental childcare is somewhat ambiguous. For instance, most researchers
report that the net association between employment and grandparental childcare is negative (Aasve
et al. 2012; Hank and Buber 2009; Lee and Bauer 2010; Luo et al. 2012). Silverstein and Marenco
(2001), however, found no significant effect of employment on grandparental childcare.
Earlier research relied on somewhat oversimplified measures of employment status and typically
distinguished only two categories of labor force attachment: for instance, employed/not employed
(Baydar and Brooks-Gunn 1998; Guzman 2004; Igel and Szydlik 2011; Wang and Marcotte 2007) or
working/not working (Aasve et al. 2012; Hank and Buber 2009; Lee and Bauer 2010; Uhlenberg and
Hammill 1998). The most detailed measurement involved three categories: working full-time, working
part-time, and not working (Luo et al. 2012; Silverstein and Marenco 2001). Danielsbacka and
Tanskanen (2012) elaborated the comparisons of employment status in a British sample and found that
grandparents working part-time generally provide care more often than grandparents working
full-time, at a level similar to that of grandparents who do not work at all. We believe that an
even more refined measure would illuminate in more detail how levels of labor market attachment
associate with the provision and intensity of grandparental care. Toward that end, we maintain the
distinction between full-time and part-time employment, since the latter offers both the
possibility of gainful employment and availability for caregiving. Moreover, we propose to
differentiate various categories of non-working individuals. For instance, being out of the labor
force may allow grandparents to provide care with high intensity, while being unemployed does not
necessarily allow grandparents the time and energy to babysit their grandchildren.
Variations in grandparental childcare by gender and kinship
Grandmothers generally provide childcare more often than grandfathers. Point estimates of the
frequency of care vary somewhat between studies, but the gender gap is found consistently. For
instance, Guzman (2004) reports that 52 % of U.S. grandmothers and 38 % of U.S. grandfathers
provide some childcare, whereas Hank and Buber (2009), analyzing data from 10 European countries,
report that 58 % and 49 % of grandmothers and grandfathers care for grandchildren, respectively.
Furthermore, grandfathers tend to provide childcare less often without their spouse (Uhlenberg and
Hammill 1998) and are less often engaged in intensive care (Hank and Buber 2009).
The effects of employment may vary significantly between women and men, but the exact nature of the
gender difference is ambivalent. On the one hand, it is argued that women are more capable of
combining paid work with childcare. Craig and Mullan (2011: 835) maintain that mothers are able to
maintain higher levels of “childcare time by cutting back on their own leisure, personal care, and
sleep”, while fathers are less able/willing to manage multiple roles. This gender difference seems
to be deeply rooted in the history of economic structures, gender-specific roles, and attitudes
toward the gendered division of labor, all of which have led contemporary women to combine various
roles, such as employee and caregiver, more often (see e.g. Haller and Hoellinger 1994). Hence,
they are likely to have – by the time they become grandmothers – the skills necessary to perform
and effectively manage their multiple social roles. On the other hand, another stream of literature
suggests that employment may be more strongly related to grandparental childcare among grandmothers
than among grandfathers since grandmothers tend to provide more intensive care and thus there is
higher potential for role conflict (see e.g. Van Bavel and De Winter 2013). Empirical evidence
seems to favor the former argument. While Hank and Buber (2009) showed that employed grandparents
were less likely to provide regular childcare than non-employed grandparents, the prohibitive
effect of employment was stronger among grandfathers than among grandmothers.
The effect of employment status on grandparental childcare may also vary by kinship link, and we
may get an incomplete – or outright biased – picture if these variations are not modeled properly.
Some authors argue that the role conflict may be greater among maternal grandparents, because they
usually provide care more often due to the matrilateral effect (Danielsbacka and Tanskanen 2012).
Because it is more challenging to combine employment with the more intensive childcare provided by
maternal grandparents (typically grandmothers), the effect of employment (and thus the salience of
the role conflict) may be strongest among maternal grandparents and maternal grandmothers in
particular. On the other hand, we may argue that the negative effect of employment on grandparental
childcare would be reduced among maternal grandparents, since strong normative pressures to provide
care for their daughter’s children would make them sacrifice their own leisure and sleep in order
to play the grandparental role according to expectations.
Is the association between intensity of employment and care spurious?
One major limitation of existing research is the cross-sectional nature of the data. As a
consequence, causality between employment and care is difficult to establish. Both grandparental
childcare and employment may be jointly determined by a third variable, such as the level of family
cohesion or normative solidarity (Igel and Szydlik 2011), i.e. a commitment to meet familial
obligations (Bengtson and Roberts 1991). For instance, Van Bavel and De Winter (2013) show that
some women retire in anticipation of becoming grandmothers. Similar considerations shed doubt on
the causal interpretations of findings from cross-sectional data.
This paper investigates the association between the intensity of labor market attachment and the
frequency of grandparental childcare. We argue that part-time employment (in contrast to full-time
employment) is associated with a higher frequency of care. We also suggest that the size of this
association varies by grandparental gender and kinship link, being probably stronger among
grandfathers and among paternal grandparents. Finally, we utilize a within-person estimator applied
to panel data to examine if we can attribute a causal interpretation to the association found in a
cross-sectional sample.
Data, variables, methods, and the modeling strategy
Data
We use data from the Survey of Health, Ageing and Retirement in Europe (SHARE). This is a
cross-nationally coordinated data collection based on repeated interviews with probability samples
of the 50+ population in each participating country. Overall, 19 (mostly European) countries have
participated in at least one wave of SHARE so far. We use the database in two complementary
analyses: one uses cross-sectional data taken from the first data collection in each of the 19
countries and the other relies on the longitudinal dimension of SHARE thus limiting the number of
available countries to 13. A list of countries with selected survey characteristics is presented in
Table 1.
Our analysis focuses on dyads consisting of “family respondents” and all their children (and, by
extension, the children of those children). The “family respondent” is defined as the member of the
household who answered questions about children, grandchildren, and grandparental care. Some of the
variables measured in the survey are characteristics of the respondents (such as sex, age,
education level, employment status, health, marital status, number of children, and number of
grandchildren), other variables refer to the children (their employment and marital status and the
age of the youngest child of child in the set, all of which are reported by the family
respondents), and yet other variables describe the relationships between the respondents and their
children (e.g. geographical distance). The respondents were asked about all their children.
Further, they were asked if they provided care (and if so, how often) for the children of each
individual child (when the child has multiple children – that is, grandchildren of the respondent –
levels of care for each individual child were not differentiated).
The analysis is based on a sub-sample of all “family respondents”. We chose all “family
respondents” aged 50+ who had at least one grandchild under the age of 16 at the time of the
interview; while different papers use a different cutoff age, the current practice is to use age 16
(see Hank and Buber 2009). Childless children of the chosen respondents were excluded from the
sample. After deleting cases with missing responses, we obtained a cross-sectional sample of 16,636
grandparents (level-2 observations in the parlance of multi-level analysis) and 25,903 children
(level-1 observations).
The panel sample consists of 6,910 repeatedly-interviewed grandparents and 10,058 children with a
total of 22,576 repeated measurements of caregiving (and of other variables). The panel sample
consists of all respondents – including refreshers from later waves – that participated in at least
two waves of data collection, i.e. the panel sample may contain respondents that were not part of
the cross-sectional sample. Identical grandparent selection criteria were applied for each wave
(e.g. the same cutoff age was applied to the youngest grandchild in order for the grandparent to be
included in a given wave). Most variables are time-varying in nature – including the provision and
frequency of childcare – and were updated at each interview. These repeated measurements are nested
within children and constitute a third level of clustering present in the data.
Table 2 presents the descriptive statistics of our cross-sectional sample. On average, there are
2.71 children per grandparent; the minimum is (by definition of our sample) 1 and the maximum is 7.
We see that 52 % of respondents in our sample never provide care for grandchildren, with 45 % of
maternal grandmothers, 53 % of maternal grandfathers, 56 % of paternal grandmothers, and 63 % of
paternal grandfathers never providing care. Slightly over 13 % of maternal grandmothers provide
care almost daily, while only 9 % of maternal grandfathers, 8 % of paternal grandmothers, and 6 %
of paternal grandfathers do so (Table 2). Note that the kinship link is derived from the sex of the
child, and thus a respondent (grandparent) that has children of both sexes may appear twice in
Table 2 – once as a paternal grandparent (when reporting on care provided to the children of a
son), and once as a maternal grandparent (when reporting on care provided to the children of a
daughter).
The data structure of the cross-sectional sample suggests that some of the standard assumptions of
regression analysis – such as independent observations – are not upheld here: several children of
the same respondent (grandparent) are unlikely to be independent. Hence, the analysis must take
these interdependencies into account; we do so by the use of hierarchical linear models (HLM) with
two levels. Country may be viewed as another level of clustering present in the data, which would
suggest that a three-level model should be used. However, we decided to incorporate country as a
set of fixed effects (binary indicators) into the cross-sectional analysis, since we had only a
relatively small number of countries and the assumption of the random selection of countries was
not supported by the study design. We have checked for consistency of results across individual
countries. The fixed-effect model described below also controls for country characteristics.
We use random-intercept ordinal logistic regression models to analyze the cross-sectional sample;
the effect of employment status is shown for the overall sample as well as for four sub-samples
defined by the sex and kinship link of the grandparent, thus differentiating maternal and paternal
grandmothers and grandfathers. We also use a within-person estimator (fixed-effect model) to
account for the potential bias stemming from unobserved grandparent-level covariates. The
fixed-effect model is estimated on the panel data.
Dependent variable
The intensity of grandparental childcare – our dependent variable – was measured using two
questions: “From which of your children [is/are] [the grandchild/the grandchildren] you have looked
after?” and – if respondents indicated that they had provided care – “On average, how often did you
look after the child(ren) of [{child name}] in the last twelve months? Was it... 1. Almost daily,
2. Almost every week, 3. Almost every month, 4. Less often”. This latter question was asked only of
respondents who had looked after their grandchild(ren) without the presence of the parents during
the last twelve months. By combining responses to both questions, we obtained an ordinal variable
with five response options (almost daily/almost every week/almost every month/less often/never; the
order of the response categories was reversed before analysis).
Main explanatory variable
Our key explanatory variable – labor-market involvement – consisted of four categories instead of
the most common two (working vs. not working). We divide the category “not working” into two parts:
“out of the labor force” and “unemployed”, because these two groups face different barriers to
caregiving. In particular, unemployed individuals are likely to be preoccupied with their job
search, and hence to be much less likely to be available for caregiving. We also divide “working”
grandparents into two categories: “working full-time” and “working part-time”. The distinction
between “full-time” and “part-time” is based on the reported number of hours worked per week: any
respondent working less than 35 hours per week is classified as working part-time (this is the most
common definition across countries and industries; see Information Sheet No. WT-4, 2004).
Table 2 shows that most of the SHARE respondents in our cross-sectional sample, a total of 74 %,
were out of the labor force (the percentage varies between 71 and 78 % depending on the
grandparent’s gender and kinship link). Around 4 % were unemployed at the time of the interview,
16 % were employed full-time, and 6 % worked as part-time employees.
Control variables
We utilize the following continuous control variables in our models: age and age squared (of the
grandparent), number of children, and number of grandchildren (also measured at the level of
grandparents). All continuous variables were centered on their means to render the intercepts more
readily interpretable. We also control for the following categorical variables: grandparent’s
marital status (married, divorced, widowed, never married; we also worked with an alternative
measure of union status that distinguished cohabitors to test the sensitivity of the results, but
the substantive findings were unaffected and are not reported here), grandparent’s subjective
health status (very good, good, bad), and grandparent’s education measured on the ISCED scale
differentiating the three substantively most meaningful categories (categories 0-1, 2-4, and 5-6 on
the original scale; for a detailed justification of this particular categorization of education see
the International Standard Classification of Occupations 2012). Geographical distance between the
grandparent’s residence and child’s residence (in the same house or household, up to 5 km, between
5 and 100 km, more than 100 km) is specified for each child (level-1 observation). We also tracked
level-1 characteristics: the marital status of the child (married, never married, divorced/widowed;
these last two categories were merged because there were too few widowed children), labor force
participation of the child (full-time job, part-time job, unemployed; the measurement of a child’s
employment status in the survey was not as detailed as for grandparents), and, finally, the age of
the youngest grandchild in the set (0-3, 4-8, 9-15; the age of the youngest child is typically
categorized in the literature in order to capture its potentially non-linear effect; see e.g.
Silverstein and Marenco 2001; Hank and Buber 2009; Igel and Szydlik 2011).
Results
Modeling the frequency of grandparental childcare in a cross-sectional sample
Model 1 in Table 3 shows that part-time employment is associated with a higher frequency of
grandparental childcare than full-time employment, net of the other variables in the model. The
ordered log-odds of being in a higher category of care frequency increase by 0.247 when working
part-time rather than full-time (p=0.002). Thus, part-time labor force participation seems to open
up the opportunity for grandparents to combine economic roles with grandparental childcare. Indeed,
there seems to be little difference in the frequency of grandparental childcare between part-time
employed and out-of-the-labor-force grandparents. Consistent with our expectations, unemployed
grandparents provide grandparental childcare almost as frequently as full-time employed
grandparents. Apparently, unemployment is as demanding as full-time employment and does not allow
grandparents to spend more time with their grandchildren.
Other effects in Model 1 (see Table 3) are not surprising and reflect what is known from the
literature. We see that, everything else being equal, the frequency of care seems to increase with
the age of the grandparent, but this effect is non-linear and the trend is reversed at higher ages.
The frequency of caregiving, quite understandably, decreases with the increasing age of
grandchildren. Grandmothers provide care more frequently than grandfathers. The frequency of care
seems to increase with the grandparent’s educational level. Both the number of children and the
number of grandchildren seem to reduce the frequency of care for the children of each individual
child. Married grandparents provide care more frequently than divorced, widowed, or never-married
grandparents. When the child is female, grandparents provide care more frequently than when the
child is male.
We also estimate a separate model for each of the four sub-populations defined by grandparent’s
gender and kinship link to the grandchild (differentiating maternal and paternal grandparents). We
present these models in Table 3 (Models 2-5). Correlates of the intensity of grandparental
childcare clearly differ across these four groups. First, we see that part-time employment (in
comparison to full-time employment) is rather strongly associated with a higher frequency of
grandparental childcare among paternal grandmothers, but not among maternal grandmothers, the two
respective coefficients being 0.395 and 0.046 (see Table 3; the respective p-values are 0.006 and
0.714). Among both paternal and maternal grandfathers, on the other hand, part-time employment
seems to increase the frequency of care, but neither of these two coefficients is statistically
significantly different from 0 at the 0.1 level (the p-values being 0.177 and 0.144, among paternal
and maternal grandfathers, respectively). Other effects in Table 3 are fairly consistent across
subpopulations with the exception of the effect of marital status of the grandparent and of the
child. Marital status seems to correlate with grandparental childcare much more strongly among
grandfathers than among grandmothers. The marital status of the child also shows some association
with grandparental childcare: maternal grandparents of divorced, widowed, or never-married children
consistently report a higher frequency of care than the maternal grandparents of married children
(the effect of a child’s marital status may reflect various levels of the institutionalization of
marriage and other partnership situations, see e.g. Cherlin 1978; Nock 1995).
Fixed-effect model
We capitalize on the panel component of SHARE to address the possibility of a spurious association
between employment and care. Repeated interviews with the same respondents enable the use of
within-subject estimator techniques, such as fixed-effect regressions. These methods control for
the additive effects of all measured and unmeasured characteristics of the subjects from whom the
repeated measurements were taken (see e.g. Allison 1999: 188). Since normative intergenerational
solidarity is likely to be highly stable in grandparents, these statistical techniques control for
its effect on grandparental childcare provision, even though no direct measure of grandparental
commitment is present in the data set. Fixed-effect methods have several drawbacks, such as reduced
sample size and lower efficiency of estimates (Allison 1999), which prevent them from becoming
default techniques for analyzing panel data. Hence, we offer them as an important complement to the
analyses presented above to check if the associations observed in the cross-sectional sample
support a causal interpretation.
The estimated parameters of the fixed-effect regression of the frequency of care are presented in
Table 4 and we clearly see that these estimates differ from those based on the random-intercept
models. Most importantly, the fixed-effect specification suggests that part-time employment does
not increase the frequency of grandparental childcare (the respective coefficient is 0.004; see
Table 4). The difference between full-time employment and part-time employment is not significant
in any of four sub-samples defined by the sex and kinship link of the grandparent and the results
are substantially the same, hence we do not present results for these sub-samples. Once all
grandparental characteristics are accounted for, the intensity of labor force attachment no longer
affects the frequency of grandparental childcare. Hence, it appears that the choice between
full-time and part-time work is influenced by unmeasured characteristics of the family (such as
family cohesion and normative solidarity) that also impact the frequency of grandparental
childcare. Thus, the association observed in the cross-sectional sample was not confirmed.
Whereas being out of the labor force seems to increase the frequency of grandparental childcare
both in the random-intercept and the fixed-effect model, unemployment effects differ across these
two model specifications. The random-intercept model indicated no effect of unemployment, but the
fixed-effect model suggests that unemployment increases the frequency of care (see Table 4):
grandparents seem to spend more time taking care for their grandchildren after they become
unemployed and this change may be causally attributed to job loss.
Some other parameters of the fixed-effect regression model differ from those of the
random-intercept models presented in Table 3. For instance, the fixed-effect model suggests a
positive effect of the number of grandchildren on frequency of care, thus indicating that when a
new grandchild is born (to any child), grandparents tend to increase the frequency of their
childcare of any of their grandchild set. The fixed-effect model fails to identify any effect of
grandparent’s health and marital status on caregiving.
Conclusion and discussion
This study investigated the effect of grandparent’s employment on the provision of grandparental
childcare and on its dynamics over time using data from the Survey of Health, Ageing and Retirement
in Europe. While this topic is quite frequently researched, previous analyses have led to
inconsistent results. We argue that these inconsistencies may have resulted from model
misspecification (e.g. interactions with gender and kinship links were omitted from previous
models) and from inadequate measurement of grandparental employment status. Furthermore, while many
authors tended to interpret associations observed in cross-sectional data as causal, we asserted
that these associations may be spurious. Thus, we also employed with-person estimators applied to
panel data to see if the observed employment status effects may be interpreted causally.
First, we find that the strength of labor market attachment relates to the intensity of
grandparental childcare strongly and consistently with the theory of role conflict (Goode 1960), at
least in the cross-sectional sample. Part-time employed grandparents report significantly higher
frequencies of childcare than grandparents with full-time jobs. Indeed their reported levels of
grandparental childcare are almost identical to those reported by out-of-the-labor force
grandparents. It appears that part-time jobs open up the possibility for grandparents to provide
intensive childcare without entirely leaving the labor market and thus they may represent an answer
to some of the dilemmas that active aging policies imply. Interestingly, unemployed grandparents
report the same levels of childcare as full-time employed grandparents, which is also consistent
with the role conflict theory as job searching may be very time consuming and may thus prevent
grandparents from more frequent interaction with grandchildren. Overall, as a theoretical
contribution, this paper indicates that the role conflict (or role strain, see Goode 1960)
perspective may be utilized to describe the situation of working grandparents.
Second, we showed that the role conflict (between employment and grandparenthood) varies by
grandparent’s gender and kinship link. Grandparental childcare provided by grandfathers is only
weakly (and insignificantly) limited by employment status, be it full-time or part-time. Among
paternal grandmothers, full-time employment represents a significant barrier to frequent
caregiving, but part-time employment raises the frequency of caregiving by a large margin to a
level comparable with that of non-employed grandmothers. Grandparental childcare among maternal
grandmothers, on the other hand, is limited to the same degree by both part-time and full-time
employment. The gendered nature of the employment effects has two implications:
1. If a labor-market policy is established to promote part-time employment in order to reduce the
tension between work and grandparental caregiving, it should focus on occupations and industries
with a higher share of female workers and/or with a high potential to employ women part-time. Only
then would it reduce the work-care conflicts (and related reduced well-being, see e.g. Glynn et al.
2009; Payne and Doyal 2010; Opree and Kalmijn 2012) resulting from prolonged careers, at least for
some grandmothers.
2. Maternal grandmothers seem to provide care regardless of the intensity of their labor market
attachment. This is likely to indicate that they are under the strongest normative pressure to play
the role of the (maternal) grandmother and to limit their own leisure in order to comply with the
requirements of their role. Indeed, maternal grandmothers provide childcare most frequently.
Cross-sectional data do not provide conclusive evidence that labor-market involvement has a causal
effect on intergenerational caregiving. It is quite plausible that some grandparents adjust their
labor market status in anticipation of caregiving. For instance, a family-oriented senior may
reduce her working hours or even retire to be a more flexible caregiver (Van Bavel and De Winter
2013). In this case, her greater involvement in the care of her grandchildren would not be the
result of her more flexible work schedule, but rather of her family orientation. We used the panel
component of SHARE (in selected countries) and a within-person fixed-effect model to reduce the
risk of model misspecification due to unmeasured variables such as family cohesion and/or the
commitment to family values and norms of intensive intergenerational bonds.
A comparison of the fixed-effect and random-effect models sheds doubt on the causal interpretation
of most (but not all) of the employment status effects. Most importantly, part-time employment did
not result in more intensive childcare in comparison to full-time employment. The fixed-effect
model suggests that important control variables were omitted from the random-effect models, thus
highlighting the importance of value profiles and family cohesion, which may jointly influence both
employment and caregiving patterns among grandparents.
We nevertheless maintain that active aging policies should be concerned with part-time employment
opportunities. Clearly, the availability of more part-time job opportunities in the economy and
smoother transitions from full-time to part-time jobs would not make grandparents care for their
grandchildren more often. These structural conditions may, however, make grandparental childcare –
if grandparents decide to provide it to their grandchildren – a less stressful and more easily
manageable experience (see e.g. Morrow-Howell et al. 2005; but see also Hansen and Slagsvold 2014).
This may apply most strongly in societies in which intense grandparenting is expected as a result
of strong familialistic norms and yet in which only limited numbers of part-time jobs exist.
We want highlight two possible limitations of our investigation. First, data collection of the
cross-sectional sample spans over 7 years in individual countries. It is not easy to refute – both
theoretically and empirically – possible impact of period effects on our findings. Most
importantly, some data were collected before the onset of the Great recession, while other
countries joined SHARE during the crisis. It is conceivable that the macro-economic downturn might
have changed people’s responses to caregiving needs in the family. For instance, part-time jobs may
be less easily available upon employer’s request and thus changing employment intensity in
anticipation of caregiving may not be feasible. Moreover, grandmothers may be less willing to
reduce their working hours since their wage may be perceived as a more valuable contribution to the
household budget vis-à-vis increased employment insecurity. Furthermore, the impact of the Great
recession may vary across countries depending, for instance, on unemployment levels, GDP growth,
and welfare system. Therefore, an empirical assessment of these potential period effects with SHARE
data would face various problems related to the number of degrees of freedom available for the
analysis. In fact, it might require an altogether different research design or a much larger sample
of countries. These difficulties notwithstanding, we included a dummy for Great recession into our
models reported in Table 3 as a basic check for period effects and found that the estimated
parameters of the intensity of employment change very little.
Second, cross-sectional analysis uses a different (and also larger) sample of countries and
individuals. Different samples do not, however, explain the difference between the
random-coefficient and fixed-effect models. We conducted several sensitivity analyses, which
confirmed that the difference between the cross-sectional and panel analyses is maintained even if
we estimate all random-intercept models on the sample of the 13 countries, for which also the panel
data are available. Thus, we are confident that our results indeed reflect behavioral patterns
rather than differences in sample definitions.
Acknowledgment
This paper uses data from SHARE wave 4 release 1.1.1, as of March 28th 2013 and SHARE wave 1 and 2
release 2.5.0, as of May 11th 2011. The SHARE data collection has been primarily funded by the
European Commission through the 5th Framework Programme (project QLK6-CT-2001-00360 in the thematic
programme Quality of Life), through the 6th Framework Programme (projects SHARE-I3,
RII-CT-2006-062193, COMPARE, CIT5-CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812) and through
the 7th Framework Programme (SHARE-PREP, N° 211909, SHARE-LEAP, N° 227822 and SHARE M4, N° 261982).
Additional funding from the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01
AG08291, P30 AG12815, R21 AG025169, Y1-AG-4553-01, IAG BSR06-11 and OGHA 04-064) and the German
Ministry of Education and Research as well as from various national sources is gratefully
acknowledged (see www.share-project.org for a full list of funding institutions).
This research received financial support from the Czech Science Foundation (grant num. 13-34958S).
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Table 1: Sizes of the cross-sectional and panel samples by country. Respondents aged 50+ with at
least one grandchild under age 16.
Cross-sectional sample
Panel sample
Sample size
% response rate
Year of the data collection
Sample size
Number of waves
Last data collection
Austria
620
58.1
2004
350
3
2011
Germany
823
63.4
2004
441
3
2011/12
Sweden
1,152
50.2
2004
828
3
2011/12
Netherlands
937
61.3
2004
665
3
2011
Spain
788
53.3
2004
415
3
2011
Italy
771
55.1
2004
614
3
2011
France
956
73.6
2004/05
743
3
2011
Denmark
579
63.2
2004
620
3
2011
Greece
649
61.4
2004/05
410
2
2006/07
Switzerland
250
37.6
2004
278
3
2011
Belgium
1,224
39.2
2004/05
859
3
2011
Israel
945
60.1
2005/06
1
Czech Republic
869
2006/07
304
2
2010/11
Poland
971
2006/07
383
2
2011/12
Ireland
359
2007
1
Hungary
914
2011
1
Portugal
658
2011
1
Slovenia
1,025
2011
1
Estonia
2,146
2010/11
1
Total
16,636
61.8
6,910
Source: SHARE, wave 1, 2 and 4 (the first data collection in each country), own calculations.
Sample sizes refer to the number of grandparents (level-2 observations in the cross-sectional
sample, or level-3 observations in the panel sample). Refreshing samples were used for the panel
analysis as long as new respondents had been interviewed repeatedly; because of the refreshers, the
panel sample may be larger than the cross-sectional sample in some countries.
Note: the data documentation gives only response rates for wave 1.
Table 2: Descriptive statistics of the cross-sectional sample used in analysis.
Maternal grandmother
Maternal grandfather
Paternal grandmother
Paternal grandfather
All grandparents
Grandparent looks after child of child (column percentages)
Almost daily
13.1
9.4
7.9
5.6
9.7
Almost every week
17.5
14.3
14.0
11.2
15.2
Almost every month
11.4
9.9
9.5
8.8
10.2
Less often
13.2
13.5
12.8
11.9
12.8
Never
44.8
52.9
55.8
62.5
52.1
Grandparent’s labor force participation (column percentages)
Full-time employee
14.2
20.7
11.1
17.5
16.0
Part-time employee
8.1
4.6
7.2
4.2
6.4
Unemployed
3.8
4.2
3.3
3.4
3.8
Out of the labor force
73.9
70.5
78.4
74.9
73.8
Grandparent’s age (mean)
63.7
65.2
65.1
66.6
65.0
Age of the youngest grandchild (column percentages)
0-3
33.1
37.1
36.2
39.2
33.9
4-8
32.0
33.2
30.8
33.2
32.4
9-15
34.9
29.7
33.0
27.6
33.7
Education (ISCED) of grandparent (column percentages)
ISCED 0,1
31.3
25.9
32.7
28.3
29.1
ISCED 2-4
54.7
54.4
53.9
52.7
54.8
ISCED 5,6
14.0
19.7
13.4
19.0
16.1
Number of children (mean)
2.80
2.85
2.81
2.88
2.71
Number of grandchildren (mean)
4.39
4.19
4.62
4.40
4.05
Marital status of grandparent (column percentages)
Married
61.2
85.2
58.5
84.9
70.0
Never married
1.8
1.0
1.5
1.0
1.5
Divorced
10.8
6.3
9.5
5.9
8.8
Widowed
26.2
7.5
30.5
8.2
19.7
Grandparent’s health (column percentages)
Very good health
21.9
24.4
20.7
23.6
22.5
Good health
35.3
36.4
34.2
35.3
35.2
Poor health
42.8
39.2
45.1
41.1
42.3
Geographical distance of child (column percentages)
in the same house or household
9.7
8.2
10.5
8.4
9.3
up to 5 km
35.5
34.6
34.9
35.1
35.0
between 5 and 100 km
38.7
40.0
37.4
39.6
38.9
more than 100 km
16.1
17.2
17.2
16.9
16.8
Table 2 continued
Maternal grandmother
Maternal grandfather
Paternal grandmother
Paternal grandfather
All grandparents
Marital status of child (column percentages)
Married
82.0
82.5
84.3
86.0
83.6
Never married
10.0
10.2
8.6
8.0
9.2
Divorced or widowed
8.0
7.3
7.1
6.0
7.2
Labor force participation of child (column percentages)
Full-time job
55.7
56.6
91.9
93.0
73.6
Part-time job
14.4
14.1
1.7
1.6
8.2
Not working
29.9
29.3
6.4
5.4
18.2
# of cases – first level (children)
7,606
5,838
7,121
5,338
25,903
# of cases – second level (grandparents)
6,045
4,598
5,760
4,237
16,636
Source: SHARE, wave 1, 2 and 4 (the first data collection in each country), own calculations.
Geographical distance of child, marital status of child, labor force participation of child and age
of the youngest grandchild are level-1 variables, all other are level-2 variables.
Table 3: Estimated parameters of random-effects ordered logistic models predicting the frequency of
care for grandchildren. Respondents with at least one grandchild under 16 in selected countries,
2004-2011.
All grandparents
(Model 1)
Maternal grandmothers
(Model 2)
Maternal grandfathers
(Model 3)
Paternal grandmothers
(Model 4)
Paternal grandfathers
(Model 5)
Grandparent’s labor force participation
Full-time (reference category)
Part-time
0.247**
0.046
0.279
0.395**
0.312
Unemployed
0.090
0.189
0.122
-0.170
0.177
Out of the labor force
0.278***
0.333**
0.187
0.253*
0.209
Grandparent’s age
0.385***
0.414***
0.418***
0.430***
0.424***
Grandparent’s age squared
-0.003***
-0.004***
-0.003***
-0.004***
-0.003***
Age of the youngest grandchild
0-3 (reference category)
4-8
-0.087*
-0.164*
-0.053
-0.208**
0.134
9-15
-1.137***
-1.318***
-1.126***
-1.180***
-0.855***
Sex of grandparent
Male (reference category)
Female
0.567***
omitted
omitted
omitted
omitted
Grandparent’s education
ISCED 0,1 (reference category)
ISCED 2-4
0.236***
0.056
0.314**
0.341***
0.176
ISCED 5,6
0.435***
0.267*
0.482***
0.406**
0.496***
Number of children
-0.242***
-0.195***
-0.287***
-0.245***
-0.283***
Number of grandchildren
-0.059***
-0.077***
-0.046*
-0.057***
-0.042*
Marital status of grandparent
Married (reference category)
Never married
-0.670***
-0.481*
-1.630***
-0.354
-0.792
Divorced
-0.652***
-0.195*
-1.498***
-0.426***
-1.598***
Widowed
-0.420***
-0.078
-0.880***
-0.212**
-1.356***
Grandparent’s health
Very good health (reference category)
Good health
-0.056
0.047
-0.014
-0.130
-0.056
Poor health
-0.356***
-0.358***
-0.322**
-0.416***
-0.400**
Sex of child
Male (reference category)
Female
0.717***
omitted
omitted
omitted
omitted
Geographical distance of child
In the same house or household (reference
category)
Up to 5 km
-1.224***
-1.403***
-1.131***
-1.098***
-1.222***
Between 5 and 100 km
-2.129***
-2.341***
-2.062***
-2.013***
-2.043***
More than 100 km
-3.282***
-3.481***
-3.180***
-3.077***
-3.416***
Table 3 continued
All grandparents
(Model 1)
Maternal grandmothers
(Model 2)
Maternal grandfathers
(Model 3)
Paternal grandmothers
(Model 4)
Paternal grandfathers
(Model 5)
Marital status of child
Married (reference category)
Never married
0.055
0.247*
0.302*
-0.133
-0.412*
Divorced or widowed
0.055
0.311**
0.289*
-0.418**
-0.082
Labor force participation of child
Full-time job (reference category)
Part-time job
0.056
0.094
0.138
-0.448+
-0.169
Not working
-0.365***
-0.506***
-0.295**
-0.440**
0.091
Country dummies (a total of 18 contrasts) not shown, results available upon request
Cut point 1
-1.099***
-2.620***
-1.815***
-1.530***
-0.717*
Cut point 2
-0.211
-1.722***
-0.883**
-0.658**
0.219
Cut point 3
0.578***
-0.909***
-0.067
0.094
1.077**
Cut point 4
2.188***
0.698**
1.559***
1.741***
2.803***
Source: SHARE, wave 1, 2 and 4 (the first data collection in each country), own calculations.
Number of level-1 observations (children) = 25,903, number of level-2 observations (grandparents)
=16,636.
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 4: Estimated parameters of a fixed-effect regression of the frequency of grandparental care.
Fixed-effect regression
(Model 6)
Intercept
2.520***
Wave number
First wave (reference category)
Second wave
-0.140
Third wave
-0.411
Grandparent’s labor force participation
Full-time (reference category)
Part-time
0.004
Unemployed
0.236*
Out of the labor force
0.211**
Grandparent’s age
0.140*
Grandparent’s age^2
-0.001***
Age of the youngest grandchild
0-3 (reference category)
4-8
0.153***
9-15
-0.058
Number of children
-0.015
Number of grandchildren
0.064***
Marital status of grandparent
Married (reference category)
Never married
1.287
Divorced
-0.198
Widowed
-0.179
Grandparent’s health
Very good health (reference category)
Good health
-0.047
Poor health
-0.044
Geographical distance of child
In the same house or household (reference
category)
Up to 5 km
-0.119
Between 5 and 100 km
-0.387*
More than 100 km
-0.565**
Marital status of child
Married (reference category)
Never married
-0.087
Divorced or widowed
0.016
Labor force participation of child
Full-time job (reference category)
Part-time job
0.051
Not working
-0.032
Number of observations (measures in time)
22,576
Number of observations (children)
10,058
Number of observations (grandparents)
6,910
Source: SHARE, wave 1, 2 and 4, own calculations.
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.