Sibling Configuration Predicts Individual and Descendant Socioeconomic Success in a Modern Post-Industrial Society
Goodman A (2013) Sibling Configuration Predicts Individual and Descendant Socioeconomic Success in a Modern Post-Industrial
Society. PLoS ONE 8(9): e73698. doi:10.1371/journal.pone.0073698
Sibling Configuration Predicts Individual and Descendant Socioeconomic Success in a Modern Post- Industrial Society
David W. Lawson 0
Arijeta Makoli 0
Anna Goodman 0
Alex Mesoudi, Durham University, United Kingdom
0 1 Department of Anthropology, University College London , London , United Kingdom , 2 Centre for Health Equity Studies (CHESS), Stockholm University/Karolinska Institute, Stockholm, Sweden, 3 Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine , London , United Kingdom
Growing up with many siblings, at least in the context of modern post-industrial low fertility, low mortality societies, is predictive of relatively poor performance on school tests in childhood, lower levels of educational attainment, and lower income throughout adulthood. Recent studies further indicate these relationships hold across generations, so that the descendants of those who grow up with many siblings are also at an apparent socioeconomic disadvantage. In this paper we add to this literature by considering whether such relationships interact with the sex and relative age of siblings. To do this we utilise a unique Swedish multigenerational birth cohort study that provides sibling configuration data on over 10,000 individuals born in 1915-1929, plus all their direct genetic descendants to the present day. Adjusting for parental and birth characteristics, we find that the 'socioeconomic cost' of growing up in a large family is independent of both the sex of siblings and the sex of the individual. However, growing up with several older as opposed to several younger siblings is predictive of relatively poor performance on school tests and a lower likelihood of progression to tertiary education. This later-born disadvantage also holds across generations, with the children of those with many older siblings achieving lower levels of educational attainment. Despite these differences, we find that while individual and descendant income is negatively related to the number of siblings, it is not influenced by the relative age of siblings. Thus, our findings imply that the educational disadvantage of later-born children, demonstrated here and in numerous other studies, does not necessarily translate into reduced earnings in adulthood. We discuss potential explanations for this pattern of results, and consider some important directions for future research into sibling configuration and wellbeing in modern societies.
Funding: UBCoS is funded by the Swedish Research Council (Grant No 2006-7498) and Swedish Council for Working Life and Social Research (Grant No
20071010). D.W Lawson is funded by a Leverhulme Trust Early Career Fellowship. The funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Children in relatively large families, at least in the context of
modern post-industrial low fertility, low mortality populations,
are now well established to be at an increased risk of poor
outcomes across multiple dimensions of wellbeing. For example,
children in larger families are known to perform relatively poorly
on IQ tests and formal educational assessments [1,2,3,4,5] and
also show signs of poorer physical health [6,7,8,9]. There is also
good reason to believe that these associations are causal, as in most
studies estimated relationships have proven robust to statistical
adjustment for parental characteristics (but see [10,11]). Several
studies also confirm that children in large families are
disadvantaged in terms of both maternal and paternal time-allocation
[1,12,13,14,15]. Furthermore, reflecting the need to feed, clothe,
and house more children, parents in large family households also
report increased levels of economic hardship, even after
adjustment for ethnicity, socio-economic position and other factors
[16,17]. These findings are consistent with simple theoretical
models of household resource dilution, which posit that, all else
being equal, individuals raised in larger families are disadvantaged
because the presence of siblings dictates a division of finite parental
resources . These findings are also supportive of parallel
theoretical frameworks in both economics and evolutionary
biology that posit a parental investment trade-off between
offspring quantity and quality [18,19].
In a recent study , we further demonstrated that negative
relationships between family size and child outcomes extend into
adulthood and across generations, meaning that variation in
fertility may have long reaching consequences for social and health
inequalities. Utilizing a unique multigenerational Swedish cohort,
we demonstrated that children, grandchildren and even
greatgrandchildren of high fertility individuals appear to suffer negative
socioeconomic consequences in terms of schoolmarks, educational
attainment and adult income . These intergenerational effects
remained significant after the adjustment for the family sizes of
intervening generations. This suggests that the socioeconomic costs
of large family size are caused by the dilution of inherited wealth
(see also [21,22]) and other forms of parental investment, rather
than covariation in the fertility of parents and children.
A less certain issue is the extent to which the socioeconomic
effects of large family size may be influenced by the characteristics
of siblings themselves, including their relative age and sibling sex.
In traditional agrarian pre-demographic transition populations
(i.e. high fertility, high mortality), a common finding is that
number of brothers, but not sisters, has strong negative effects on
adult socioeconomic outcomes among males (e.g. [23,24,25]). This
reflects the fact that sons are often in direct resource competition
for parental resource transfers required for marriage and the
establishment of new households. Following systems of
primogeniture, inheritance practices also frequently, but not always (e.g.
), favour children born earlier in the birth order .
Furthermore, there is evidence from anthropological and
demographic studies that sons and earlier-born children are often
treated preferentially by parents during childhood (e.g. [28,29]),
although such biases are not cross-culturally universal in
traditional societies (e.g. ). The ultimate origins of differential
treatment of children by sex and birth order remain the subject of
debate by anthropologists (see [23,27,31]).
Do parents in modern post-industrial low fertility, low
mortality societies also show patterns of biased investment? With
regard to sex-biased parental investment, a number of studies have
suggested that several modern European and North American
populations are characterized by some degree of son preference
. For example, studies of the United States population have
shown that parents with more sons than daughters are more likely
to marry and less likely to divorce [33,34] and that, on average,
fathers earnings increase to a larger extent in the years following
the birth of a son relative to the birth of a daughter . In a
longitudinal study spanning the first 10 years of life in British
children, Lawson and Mace  found mothers spent slightly
more time on average engaging in active childcare activities with
daughters. Fathers, however, showed stronger signs of bias in the
opposite direction, spending more time with sons, particularly after
infancy. On the other hand, Sweden and several other
Scandinavian countries show indications of a slight daughter preference,
as indirectly evidenced by higher rates of having a third child in
two-son families than in two-daughter families . To the extent
that parents do invest preferentially in children of a particular sex,
one might expect siblings of that sex to confer larger detrimental
effects. To date the evidence that brothers vs. sisters have different
effects on child educational outcomes is very mixed [5,37]. For
example, studies of the United States population have concluded
that brothers rather than sisters are associated with lower
educational attainment (e.g. ), while others studies suggest
the opposite pattern (e.g. ) and others still conclude there is no
effect of sibling sex (e.g. ). We are not aware of studies that
have examined sibling sex effects on alternative indicators of adult
socioeconomic position and on the socioeconomic outcomes of
With regard to birth order, a number of time-allocation studies
have suggested that later-born children receive less parental
investment in modern populations [12,41]. An important
conclusion from this literature is that such a later-born deficit may not be
the product of conscious strategic effort of parents. Rather it may
occur as the by-product of equalised treatment of children at
different ages within the family and the fact that older siblings
monopolize parental attention before the birth of later-born
children [4,41,42]. Furthermore, sibling relationships have also
been suggested to be more beneficial to early-born children. This
may be because the act of teaching younger siblings promotes
cognitive development (e.g. ). Proponents of confluence
theory also argue that relative sibling age influences cognitive
development, not because it reduces shares of finite parent
investment, but rather because it alters the intellectual climate of
the family unit assumed to be a function of the average age of all
household members. Early born children are therefore seen to be
at an overall advantage because they experience more time in
households with less children and thus a relatively sophisticated
intellectual family environment .
In line with this literature, many studies have reported that
later-born children perform significantly worse on educational and
IQ tests [2,3,5,45,46]. However, fewer studies directly compare
the consequences of having younger vs. older siblings, and
relatively little is known about whether such effects also influence
adult socioeconomic position and the transmission of
socioeconomic resources across generations. This is important because
although there are strong effects of birth order on educational
attainment, inheritance sums may be bequeathed equally to all
children in modern populations, potentially offsetting the
disadvantage of late birth order. Furthermore, there are indications that
later-born children may be advantaged in other meaningful ways
that could offset their early disadvantage in childhood education.
For example, Lawson and Mace  found that British children
with many older vs. younger siblings scored significantly better on
measures of child mental health, and may therefore be less likely to
suffer a range of adverse outcomes as adults .
In this paper, we build on the analyses in Goodman et al. 
to contribute new data on the effects of sibling relative age and
sibling sex upon own and descendant socioeconomic outcomes.
Utilizing a unique multigenerational Swedish cohort of 14,000
children and their biological descendants, and adjusting for a
number of important covariates, we 1) compare the relationship of
number of brothers versus number of sisters to childhood and
adult measures of socioeconomic position; 2) compare the
relationship of number of older siblings versus number of younger
siblings to these same outcomes; and 3) examine how far any of
these relationships are also observed with respect to socioeconomic
outcomes in children and grandchildren of the cohort member.
All data were derived from the Uppsala Multigenerational Birth
Cohort Study (UBCoS), a unique Swedish dataset that tracks over
14 000 individuals born in the early 1900s and all their
descendants to the present day. UBCoS was approved by the
Regional Ethics committee in Stockholm (dnr 03117, dnr 04
944T and dnr 2009/111532).
Our sample comprises all live births at the Uppsala University
Hospital between 1915 and 1929. This hospital delivered an
estimated 75% of births in Uppsala city and 50% of births in
surrounding rural parishes. This birth cohort is nationally
representative of Sweden in terms of infant mortality and fertility
, albeit with a somewhat higher proportion of infants from
urban areas (46% vs. 31% nationally ).
For our analyses of sibling sex, we used the full birth cohort of
14,192 infants as our starting point, and excluded those who
systematically lacked data on child and/or adult socioeconomic
outcomes. This included cohort members who were never traced
(N = 167), who died (N = 2047) or permanently emigrated before
1970 (N = 110). We also excluded those who were missing reliable
data on number of brothers or sisters (N = 929). This remaining
study population of 10,939 represents 85% of those who survived
to age 10, i.e. who survived long enough to receive any outcome
measure of interest to this study. For our analyses of sibling age, we
further restricted our analyses to the 7091 study members born
19151924 (91% of those who survived to age 10). We did this in
order in increase our ability to measure accurately the childs
number of younger siblings, as sibling data were last collected in
We supplemented this data by linking cohort members to all
biological descendants born up to 31st December 2009, using the
Swedish Multigenerational Register (estimated completeness
97.7% for paternity, 99.6% for maternity ). Our analyses
focus on outcomes in the children and grandchildren of cohort
members; as judged by the distribution of birth years, these
generations were essentially complete by 2009 .
Data on the number of brothers and sisters, and older and
younger siblings was available from multiple sources. First,
obstetric records were available for all cohort members, and
provided two sources of data: (a) the mothers previous number of
live births was recorded in the obstetric record of each cohort
member, irrespective of whether those previous births were at
Uppsala Hospital or not. From 1924 this was done separately for
previous male and previous female births; and (b) we could identify
older and younger siblings born to the same woman at Uppsala
Hospital across the period of data collection (19151929). Second,
census data were available for the 68% of cohort members
successfully traced to the 1930 Swedish census. This recorded
information on the sex and birth year of all household members,
including text descriptions that allowed us to identify their
relationship to the index child (see File S1).
We triangulated data from across these data sources to identify
the number of older and younger siblings for each cohort member,
and their number of brothers and sisters. This involved comparing
the number of siblings of each type reported in each data source,
and using the highest number (see File S1 for more detail). Pearson
correlations between the number of siblings identified in the
different data sources ranged from 0.70 (for no. brothers) to 0.86
(for no. older siblings). Note that we did not count twins or triplets
as either older or younger siblings, but used obstetric information
to create a separate variable to capture twin/triplet status.
In estimating a childs number of siblings, we included not only
full siblings (95% of all siblings in the 1930 census) but also foster/
adoptive siblings (1% in the 1930 census) and step- or half-siblings
(4% in the 1930 census). We did this to capture the sibship
experienced while growing up and also because we were unable to
distinguish between full siblings and maternal half siblings in the
obstetric data. Among those with census data, our findings were
unchanged in sensitivity analyses which only counted full siblings
and/or which were restricted to the 93% of cohort members with
no adoptive, step- or half-siblings. Our findings were also
unchanged in sensitivity analyses restricted to cohort members
born 19151919 who, judging by the age distribution of their
siblings, had by 1930 acquired the majority of their younger
siblings that would ever be born (see File S1). The gender
distribution of older siblings suggested that we had captured older
brothers and sisters equally (see File S1).
Indicators of Socioeconomic Success
We used archive and register data to assign three indicators of
Schoolmarks: standardised average marks across all
compulsory subjects in elementary school (collected age 10 in the
cohort members, age 16 in their grandchildren: not available
for the intervening generation; see also ).
Entering university: ever entering university or equivalent, if
aged 21 or over. Education data was available in 1960, 1970,
and then yearly from 19852008.
Family income: disposable family income, standardised each
calendar year by age and sex and then averaged across all
available calendar years in which the descendant was aged
2165. Income data was available in 1970, and then yearly
Each indicator of socioeconomic success was used as an
individual-level outcome for the cohort members themselves. For
the children and grandchildren of the cohort members, we
generated averages across all available descendants e.g. proportion
of children entering university, average income among
grandchildren. Goodman et al.  and the supplementary information
therein provide full details on how each of these measures were
Parental Socioeconomic Position and Other Early-life
We measured parental socioeconomic position and other
earlylife characteristics using the archived obstetric records. These
records provided data on cohort members birthweight and
gestational age, on the mothers age and marital status, and on
the occupational social class of the head of the household (see
Table 1). Social class was coded using the Swedish socio-economic
classification scheme  and was taken from the obstetric records
of the cohort member if available (92%), or from the records of
their siblings, from the school archive or from the 1930 census if
missing (taking the record closest in time). We included these as
covariates as we have previously shown that all independently
predict the educational outcomes of the children and/or
grandchildren of the cohort members . Our results were
unchanged in sensitivity analyses, which excluded birthweight as a
covariate, as this typically increases with mothers parity and so
could conceivably mediate rather than confound associations with
number of older versus younger siblings.
We used multivariable regression to investigate how the
numbers of different types of sibling were associated with the
socioeconomic success of cohort members and with the average
success of their descendants. To facilitate comparisons of effect
sizes across generations and across outcomes, we standardised all
outcomes for each generation and used these in linear regression
analysis. The only exception was for one of our binary outcomes
(cohort member entering university), for which we used logistic
regression and converted the log-odds to effect sizes .
We compared the effects of number of brothers versus sisters by
entering both simultaneously as linear terms in a multivariable
model, and then calculating the significance of the difference
between the two coefficients. We did the same when comparing
number of older versus younger siblings, except when the outcome
was schoolmarks. In this last analysis we instead made the
comparison between the coefficients using a piecewise approach,
as there was evidence that the association between number of
older siblings and schoolmarks was non-linear (p,0.001, all other
p.0.05 for linearity). We adjusted all these analyses for the cohort
members birthweight, gestational age, twin/triplet status,
mothers age, mothers marital status, parental socio-economic position
and birth year (categorised as in Table 1, all correlation
coefficients #0.43 between early-life characteristics). We
calculated confidence intervals using robust standard errors clustered by
Table 1. Characteristics of Uppsala Multigenerational Birth Cohort Members.
Sibling sex (N = 10,939)
Sibling age (N = 7109)
Numbers add to less than the total sample size for some characteristics because of missing data.
The frequency of missing data ranged from 03.4% for early life
characteristics and from 2.711.8% for outcome characteristics
(NB this excludes impossible outcomes, e.g. average descendant
characteristics among a childless cohort member). All analyses
handled missing data under an assumption of missing at random,
using multiple imputation by chained equations  in Stata (5
imputations, including in our imputation model all variables and
structure included in substantive models).
As shown in Table 1, the 10,939 cohort members used in our
analysis of sibling sex had roughly equal numbers of brothers and
sisters (mean 1.2 vs. 1.1). By contrast, the 7109 cohort members
used in our analysis of sibling age had a larger number of older
siblings than younger siblings (mean 1.7 vs. 0.9). This difference is
largely due to the fact that sibling information was recorded for the
last time in 1930 (when the original cohort members were aged 5
15 years old) and any younger siblings born after 1930 are
therefore missing from our analysis. As for our three
socioeconomic outcomes, correlations between these were always
positive but were not large (0.08#r#0.29 for all outcomes across
all three generations, except for r = 0.56 between schoolmarks and
tertiary education in the grandchild generation).
Overall Estimated Effects of Family Size on Education and
As previously stated elsewhere , total family size showed a
strong association with both education and income in our cohort
members (Figure 1). These effects were seen across the whole
range of increasing family size, with a particularly strong
association with the probability of entering university. For
example, under 5% of those with five or more siblings entered
university as opposed to 16% of those with no siblings,
corresponding to an adjusted effect size of 21.36 standard
deviations (95% CI 21.65, 1.06: see Figure 1).
Sibling Sex, Education and Income
As presented in Figure 2, there was never evidence that the
detrimental effects of having more siblings differed according to
whether those siblings were brothers or sisters. Instead more
siblings of either sex predicted progressively poorer
socioeconomic outcomes among cohort members, with particularly
large effects upon the two educational outcomes. There was
likewise no evidence that the sex of the siblings was important
when we compared the effects of older bothers versus older sisters,
and of younger brothers versus younger sisters, among the 6180
cohort members who had valid information of both sibling age
and sex (all p.0.05 for difference). Finally, there was never
evidence either for this or for subsequent analyses that the effects
presented differed between males and female cohort members (all
p.0.08 for interaction with sex of the cohort member, most
p.0.2). For this reason Figure 2 and subsequent figures combine
male and female cohort members.
Sibling Relative Age, Education and Income
As presented in Figure 3, there was little or no evidence that the
relative age of siblings affected our outcomes in unadjusted
analyses. In adjusted analyses, by contrast, strong evidence
emerged that the negative impact of older siblings was greater
than that of younger siblings for the two educational outcomes.
Adjusting one by one for the characteristics in Table 1 indicated
that the difference between the unadjusted and the adjusted effects
was largely driven by adjusting for whether the mother had ever
been married. The negative confounding of this variable reflected
the fact that early-born children were more likely than later-born
children to be born to never-married mothers (e.g. 36% of first
born children versus 16% of second-born and ,5% thereafter).
Being born to a never-married mother in turn predicted
substantially poorer educational outcomes (e.g. 5% of the children
of never-married mothers entered university, as opposed to 12%
for ever-married mothers; see also ). By contrast, no effects of
sibling age were observed upon income in adulthood after
adjustment for other early-life characteristics.
These substantive findings were all almost identical in analyses
restricted to those born 19151919, for whom most younger
siblings that would ever be born had been born by 1930. Indeed
for schoolmarks there was if anything an even greater difference
between older and younger siblings effects after restricting to those
born 19151919 (see File S1). As such, it did not seem that the
observed differential effect of sibling age could be explained by
greater measurement error with respect to younger siblings.
Differential Effects of Sibling Configuration Across
No intergenerational effects were seen for those variables which
showed no effect on the cohort members themselves. Specifically,
there was never evidence of a differential effect of number of
brothers versus sisters on outcomes in children and grandchildren
(all p.0.14 for difference), an unsurprising result given also no
evidence of a difference among the cohort members themselves.
Similarly, there was no evidence that number of older versus
younger siblings was associated with descendants income (both
p.0.55 for difference).
By contrast, a greater detrimental effect of older versus younger
siblings was observed with respect to tertiary education in the child
generation (p,0.001, see Figure 4). By the grandchild generation
this difference had become non-significant, although a trend
remained in the same direction (see Figure 4). Schoolmark data
was not available for the child generation, and by the grandchild
generation there was no evidence of a greater detrimental effect of
older versus younger siblings (p = 0.76 for difference).
This study demonstrates that sibling configuration, rather than
simply sibling number, predicts individual and descendant
socioeconomic outcomes in a modern population born in early
twentieth century Sweden. Specifically, we find that individuals
growing up with several older siblings achieve lower schoolmarks
Figure 2. Association of the cohort members number of brothers versus sisters with the cohort members schoolmarks,
educational attainment and adult income. p-values presented are for the difference in the effect of number of brothers versus sisters. In the
lefthand column these p-values come from models only adjusting for these two sibling variables. In the right-hand column the p-values come from
multivariable models additionally adjusting for the early-life characteristics shown in Table 1.
and are less likely to progress to tertiary education, when
compared to those that grow up with an equal number of younger
siblings. Such educational effects are also carried forward to future
generations, with a higher number of older vs. younger siblings
also predicting lower educational attainment in the cohort
members children. We found no evidence, however, that adult
income is influenced by the relative age of siblings, and the sex of
siblings was also not associated with any indicator of
Our findings with respect to sibling age and educational
outcomes are consistent with previous studies demonstrating that
later-born children in modern post-industrial low fertility societies
are disadvantaged in terms of IQ, educational achievement and
educational attainment [2,3,5,45,50]. They also extend our
previous research into the long-term consequences of family size
on descendant socioeconomic success , by identifying that the
multigenerational effects of high fertility upon education outcomes
are most strongly driven by the disadvantages of being a later-born
child. These within-family inequalities in child and descendant
outcomes receive comparatively little attention from academics
and policy-makers, but may be comparable in magnitude to the
Figure 3. Association of the cohort members number of older versus younger siblings with the cohort members schoolmarks,
educational attainment and adult income. p-values presented are for the difference in the effect of number of older versus younger siblings. In
the left-hand column these p-values come from models only adjusting for these two sibling variables. In the right-hand column the p-values come
from multivariable models additionally adjusting for the early-life characteristics shown in Table 1.
between-family inequalities driven by factors such as parental
social class .
Interestingly, however, the relative age of siblings did not
influence adult income in either the cohort member or subsequent
generations. This result is surprising since schoolmarks and
educational attainment were at least somewhat predictive of adult
income (observed Pearson correlations 0.080.29). Thus our
findings caution that disadvantages in early life cognitive
development and education associated with family structure
cannot necessarily be extrapolated to later adult socioeconomic
outcomes. A number of factors may explain why those with older
vs. younger siblings achieve similar levels of adult income despite
their demonstrated disadvantage in terms of education. Income is
less strongly associated with total sibling number than the two
educational outcomes (Figure 1; see also ), plausibly because
the dilution of parental resources is a weaker determinant of
offspring outcomes during later adult life than during childhood
and the transition to adulthood. As such, one possibility is simply
that this study lacked power to detect relatively small differential
effects of sibling age on income.
Another possible explanation is that having older siblings may
confer alternative advantages which are not measured by this
study. For example, as noted in the introduction, British children
with several older as opposed to several younger siblings have been
found to score significantly better on validated, parent-rated
measures of child mental health . This finding requires wider
replication, but poor child mental health is associated with a range
of adverse outcomes in later life , and thus could have
knockon negative influences on adult earning potential. It is unclear why
those with older siblings in particular should experience a lower
incidence of child mental health problems, but several studies have
suggested that sibling relationships can be an important source of
social and emotional support. For example Grass, Jenkins & Dunn
 found that self-reported affectionate relationships between
siblings had a protective effect on adjustment to stressful life
events. Downey & Condron  also report that children with
siblings score higher on measures of interpersonal skills, suggesting
that growing up in a multiple child family may enhance future
abilities to navigate social relationships (but see ). More
research is required on the impact of sibling configuration on such
alternative measures of child development and adult outcomes.
We find that the sex of siblings exerted no influence on the
socioeconomic success of cohort members or their descendants.
This finding is consistent with previous reports that parental
investment in modern populations like contemporary Sweden
shows at most only a slight sex preference , and so growing up
with relatively more brothers vs. sisters has no discernable impact
on indicators of socioeconomic outcomes (see also ). We also
found no evidence that the number of brothers vs. sisters
interacted with the sex of the child, contradicting the expectation
of some scholars that sibling competition will be most pronounced
when siblings are of the same sex (e.g. ).
Our study has a number of notable methodological advantages.
Firstly, we were able to consider simultaneously three important
socioeconomic indicators (school achievement, educational
attainment and income) measured at different points of the life course.
Second, we considered these measures both for the cohort
member and for their children and grandchildren. Finally, we
were able to adjust our analyses for birth characteristics and the
socioeconomic position of parents, which may otherwise confound
the effects of resource dilution. As we demonstrate, the differential
consequences of growing up with several older vs. younger siblings
only became apparent after making statistical adjustments for
these factors in particular for the fact that the low birth order
children are most likely to face the disadvantages of being born to
younger and unmarried mothers. Future studies should be aware
of these potentially confounding relationships, particularly in the
context of modern populations where childbirth is relatively more
common outside of marriage.
One important limitation of our study is that information on
half siblings was only collected at 1930 and after this period we
only have information on full siblings. These data restrictions
mean that we were unable to fully characterise each cohort
members full sibling experience in early life, and this is likely to
have introduced some measurement error. Another limitation is
that our measure of adult family income only captures wealth
creation, and excludes wealth ownership, which may be more
strongly influenced by patterns of inheritance . However, we
anticipate that inheritance sums are usually bequeathed equally to
children in modern post-industrial populations and so we do not
suspect this pathway will influence relationships between birth
order and wealth ownership. It is also important to note that
although the results presented here are consistent with popular
hypothesised mechanisms of family resource dilution , they do
not demonstrate causality. Several studies have now shown that
children growing up with many siblings, and with older siblings in
particular, spend less time with their parents engaged in care
activities [12,13,41]. Few such studies, however, have also directly
tested whether such associations mediate the lower levels of
educational achievement and attainment (see  for a notable
exception). Understanding these mechanisms more fully may
ultimately inform the design of interventions that mitigate not only
between-family inequalities generated by family size but also the
within-family inequalities generated through sibling configuration.
File S1 This file provides further information on (a) how we
derived sibling configuration data for each cohort member from
multiple sources and (b) an assessment of the potential for
measurement error by sibling sex and age.
data collection and management. We also thank I. Koupil, H. Colleran
and two anonymous reviewers for useful comments.
Conceived and designed the experiments: DWL AG. Performed the
experiments: DWL AG. Analyzed the data: AG. Contributed reagents/
materials/analysis tools: AM. Wrote the paper: DWL AM AG.
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