A brief review of relevance to A: effects of sibling composition and parental disruption
Several studies have shown a negative association between sibship size and children’s
education in Western countries (e.g. Booth and Key 2009; Conley and Glauber 2006; Jæger
2008). The most common explanation is that, with more siblings, there is more competition
for parental attention and resources. However, many of the more recent investigations that
have been based on either better data or more advanced methods to account for unobserved
potential confounders have reported little or no effect (Angrist et al. 2010; Åslund and
Grönqvist 2010; Black et al. 2005; de Haan 2010). Some authors have also considered birth
order, in addition to or instead of the number of siblings, in studies of children’s education
(Åslund and Grönqvist 2010; Black et al. 2005) or intellectual capacity (Kristensen and
Bjerkedal 2007). The motivation for that is not always clear, though. While there are some
biological and social arguments that involve birth order directly, other arguments that have
occasionally been mentioned are really about parents’ age at the time of birth (e.g. older
parents can better afford to educate a child, and young parents have more years to reap the
benefits of the child’s education).
Attempts to consider the siblings’age have been made in few of the studies that deal
with industrialized countries (but more commonly in poor settings, see e.g. Kravdal et al.
2013). For example, Lawson et al. (2013) reported that having relatively old siblings was
more of a disadvantage than having young siblings. A closely related issue is, of course, the
age difference between the siblings. One possible effect is that narrowly spaced children may
stimulate each other. On the other hand, it has also been argued that a short birth interval may
lead to fewer positive interactions between the mother and the older child (Baydar et al.
1997). In a review of possible mechanisms, Steelman et al. (2002) concluded that, despite
some possible benefits for narrowly spaced children, the overall advantage was associated
with wide spacing.
Relationships between sibling composition and health have also been studied, not only
in less developed settings, where there has been much concern about adverse effects of short
birth intervals, but also in rich countries. Some have focused on health indicators at the time
of birth or in very early childhood; others have studied later health outcomes. For example, a
reduced body length at birth and slower subsequent growth has been seen among those with
many siblings, older siblings being at a particular disadvantage (Lawson and Mace 2008).
There are also studies that have shown an adverse effect of short (or long) birth intervals on
birth weights and other birth outcomes (Wendt et al. 2012), and attempts have been made to
identify causally intermediate maternal physiological factors such as nutritional depletion
(Smits et al. 2013). On the other hand, Lundborg et al (2013) showed positive effects of
number of siblings on children’s length in the longer term, which was thought to reflect that
greater exposure to infections transmitted from siblings early in life is beneficial for the
development of the immune system. Also implications for mental health have been studied.
Some authors have found good mental health at age 3-9 among those who were last born in a
large family and poor mental health among those with relatively many younger siblings
(Lawson and Mace 2010), while others have concluded that the middle child has advantages
with respect to mental health (Carballo et al. 2013). Finally, there is some evidence of effects
on physical health. For example, some studies have shown a relationship between birth order
and development of cancer (von Behren et al. 2011; Wanderås et al. 1998), and another
showed a beneficial effect of few siblings on a child’s cancer survival (Syse et al. 2012).
Others have dealt with bowel diseases, diabetes or other diseases (Montgomery 2002; Stene et
al. 2001; Victorino and Gauthier 2009). In addition to the higher infection risk among
children with many older (or younger) siblings and disadvantages related to resource dilution,
it has been suggested that sibsize or birth order effects could be due to differences in the in-
utero hormonal environment. A lower birth weight could be involved in that causal pathway
(Park et al. 2008; Gravset et al. 2007).
In addition to affecting the children’s education and health, the number of siblings
may have consequences for the children’s fertility. For example, Murphy and Knudsen
(2002), Kotte and Ludwig (2011), Kolk (2014), and Murphy (2014) reported positive
relationships. The first-mentioned also showed an effect of birth order. However, the age of
the siblings, which one may expect to be of some importance, has received little attention. For
example, those who have had much younger siblings, and have therefore learned much about
the costs and benefits of parenthood, may have been influenced by that experience.
There is also a large literature on how children who have experienced the dissolution
of their parents’ relationship fare compared to other children. Most studies have considered
outcomes at a relatively young age, but some have also reported findings that indicate adverse
effects in early adulthood (Cherlin et al. 1998; Musick and Meier 2010; Strohschein 2005;
Sigle-Rushton et al. 2005) or even later (Clark et al. 2010; Amato and Cheadle 2005). Many
types of outcomes have been considered, for example educational outcomes (Sigle-Rushton et
al. 2005; Amato and Cheadle 2005; Steele et al. 2009; Kim 2011; Musick and Meyer 2010),
mental and physical health outcomes, sexual behaviour, and children’s own family formation
and dissolution (Cherlin et al. 1995; Upchurch et al. 2001; Amato and Cheadle 2005; Musick
and Meyer 2010; Reneflot 2011).
A number of underlying mechanisms have been suggested. For example, the parental
conflict that typically accompanies the disruption may affect the child’s wellbeing. A stressful
home environment may also have implications for the parents’ wellbeing, with further effects
on the child. Additionally, the disruption itself may lead to a lower household income, the
custodial parent having less time and energy to support and supervise the child, or a change of
residence (see reviews in e.g. Bozstek and Beck 2010).
The consequences of disruption are likely to be conditioned by the age of the child
when it occurs (Strohschein 2005). The duration between the disruption and the measurement
of the outcome may have some relevance as well: disruption may produce some sort of crisis
reaction that gradually tapers off. Most studies suggest that children who experience a divorce
at a young age fare worst, but there are also studies showing that this only is the case if factors
linked to age, such as birth order and even year of birth, are not adequately controlled for
(Sigle-Rushton et al. 2014).
A brief review of relevance to B: effects of partnership and reproduction on health and
mortality
Many authors have studied the relationship between an adult’s marital status at a certain age
and his or her health or mortality at that time or later (Roelfs et al. 2011; Shor et al. 2012).
Typically, these studies show a number of advantages for the married, while there is more
uncertainty about the differences between groups of unmarried. Recently, a few studies have
also dealt with the marital status history, such as whether the currently married have
experienced divorce and remarried, and time since divorce, with mixed findings (Berntsen
and Kravdal 2012; Dupre et al. 2009). It is not entirely clear whether marital status is a more
important determinant of health and mortality for men than for women; this may well depend
on age (Shor et al. 2012) and vary across the health outcomes considered (Amato et al 2010).
Associations may also have changed over time. Indeed, a strongly increasing mortality
disadvantage for the unmarried has been observed, though with no good explanation so far
identified (Berntsen 2011).
Only a few authors have considered the difference between married people and
cohabitants – both because the latter group until quite recently has been very small among the
higher age groups and because of lack of data. The evidence so far suggests that the married
have the lowest mortality (Liu and Reczek 2012; Drefahl 2012). Hardly anything is known
about whether a disruption of a consensual union has another health effect than a disruption of
a marriage.
The relationship between reproductive behaviour and health/mortality has received
much less attention than that between marital/partnership status and health/mortality.
Generally, mortality has been found to decrease with increasing number of children for both
sexes, at least up to certain level, after which there may be an opposite association according
to some, but not all studies (e.g. Doblhammer 2000; Grundy and Kravdal 2010). The latter
variation has been thought to partly reflect the importance of contextual factors that mitigate
the burdens of childbearing. The similarity across sexes suggests that the relationship between
the number of children and mortality largely is produced by lifestyle factors associated with
childbearing (causality going both ways), while physiological effects of pregnancies that, of
course, are restricted to women play a modest role.
A relatively high mortality has usually been seen among those who have a low age at
first birth (taking this into account typically strengthens the beneficial effect of high parity),
and the very few studies that have taken birth spacing into considerations have concluded that
women and men with short birth intervals tend to have higher mortality than those with more
average intervals (Grundy and Kravdal 2014).
Few attempts have been made to understand the relationships between reproduction
and health/mortality properly in light of the partnership situation and history. Many have
controlled for current marital status (which may actually to some extent be a result of the
reproductive behaviour rather than a determinant), but few have taken the full marital or
partnership history into account. Also, there has been modest interest in whether parenthood
has less protective effects for some groups than others (e.g. divorced men, who may have had
quite little contact with their children), or – to give this a slightly different twist – whether the
effects of changes in marital status depend on the number of children born or in the old/new
household. (The possible importance of such interactions has been pointed out by Amato
(2010); see Kravdal et al. (2012) and Lusyne and Page (2008) for examples of studies that
have addressed them.)
Methodological challenges in this research area
There are potentially large selection problems in this research area. Consider, for example, the
association between the experience of disruption or the number of siblings on the one hand
and the educational level achieved by the child at age 30 on the other. Such an association
reflects causal effects of these two demographic factors as well as numerous factors that
influence both the parents’ fertility or disruptions risk and the child’s education. The mother’s
education is one such factor, which is often available in survey/register data and possible to
control for. One may also be able to control for child characteristics at an early age, for
example as measured in psychological tests, that can be signals of parental characteristics
with a bearing both on disruption risks and later child outcomes, or that themselves could
exert such influences. However, there will always be additional unmeasured characteristics of
the family members and the environment that may differ between those with few and those
with many siblings, or between those in intact and those in non-intact families.
Various strategies have been used to reduce the problems related to unmeasured
heterogeneity. Some authors have estimated (within-)siblings (fixed effects) models, which
means that constant characteristics of the mother (or father) that affect all their children are
held constant. This method is, of course, relevant only when assessing effects of factors that
vary between siblings, such as age when the parents’ marriage is dissolved, birth order, or
number of siblings within a given age group at certain important points in time of a child’s
life (Dammert 2010). Sibling models may be used also when estimating effects of an adult’s
family- or reproductive behavior on his or her later health (references in Sbarra et al. 2011). A
special case of this type of sibling analysis is to consider twins, the monozygotic being
particularly valuable because of their genetic similarity. Twins do not differ with respect to
the demographic factors considered in part Ia, but can be a valuable supplement in Ib.
An alternative is to do a within-individual analysis. When analysing effects of a
child’s or an adult’s disruption experience, one may be interested in outcomes that can be
measured both at some initial stage and after a disruption may have occurred (Brockman
2013; Cherlin et al. 1998). The idea is then to see whether those who have experienced the
disruption also tend to have experienced particularly large or small changes in the outcome
variable. Even the sibling composition, depending on how it is defined, may vary over a
child’s life (Schmeer 2009), and one may compare this with the change in the outcome
variable of interest.
An alternative to the within-siblings approach when studying child outcomes is to
control for constant unobserved mother-level (or father-level) characteristics by estimating a
multilevel-multiprocess model with equations both for a woman’s fertility or divorce and the
outcomes under study. Each equation must then include a mother-level random tern, and these
must be allowed to be correlated with each other. One advantage of this approach is that also
women with one child, or (relevant with dichotomous outcomes) with all children
experiencing the same outcome, contribute in the estimation. Furthermore, variables that are
constant over the children can be included. There are only a few examples of such studies in
this research area, including our own (Steele et al. 2009).
In some other studies of effects of sibling composition or divorce, instrumentalvariable techniques have been used. Twin births (Black et al. 2005; de Haan 2010), the sex
composition of older siblings (Angrist et al. 2010; Conley and Glauber 2006 ), miscarriages
(Maralani 2008), parents’ sibship size (Jaeger 2008), or changes in divorce laws (see review
by Amato 2010) have been used as instruments. Similarly, in studies of reproductive
behaviour on adults’ later health or mortality, twin births, sex of earlier children, spontaneous
abortions, or successful in vitro fertilizations have been used as instruments (e.g. Kruk and
Reinhold 2014). With respect to twins, which perhaps is the most common instrument, it is
important that a substantial proportion of those who have apparently had an “extra” child
because of a twin birth might have had another child anyway (i.e. one estimates only a “local
average treatment effect”, and even for a rather small group). Besides, many of the
mentioned instruments may have direct effects on the outcome, contrary to the basic
assumption. (See critique in e.g Rosenzweig and Zhang 2009 and Åslund and Grönqvist
2010).
A special challenge that arises when analysing effects of disruption is that a disruption
typically is caused by the low quality of the parents’ relationship, which itself may be
detrimental to the child’s wellbeing – and even the key reason for poorer outcomes among
children in non-intact families. In fact, there may well be positive effects of dissolving a poor
marriage (Strohschein 2005), and some studies have shown that children living with highconflict married parents fare worse than those who have experienced disruption (Musick and
Meyer 2010). Effects of low quality and disruption obviously cannot be separated with
register data. All one can do is to at least control for factors measured a quite long time back,
to be sure they are causally prior to the rather lengthy process that leads to a disruption.
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