INTERNATIONAL JOURNAL OF SOCIOLOGY OF THE FAMILY
VOLUME 37, NUMBER 2, AUTUMN 2011
PARENTAL SOCIOECONOMIC STATUS AND
SIBLING EDUCATIONAL INEQUALITY
IN THE UNITED STATES
GREGORY M. EIRICH
Columbia University
A small but important literature has theorized that parental socioeconomic
status (SES) affects sibling educational inequality. First, this paper confirms
previous suggestive evidence that as parental SES increases in the United States,
sibling educational inequality decreases. A child from high SES parents is more
likely to have a sibling with a similar (relative) schooling level than a low SES
child, who will be further away educationally from his or her sibling. Second,
this paper casts doubt on the literature’s hypothesized causal mechanisms for
this result. Using a simple simulation approach, this paper illustrates that the
strong negative association between parental SES and sibling educational
inequality does not appear to be due to any active parental investment decisions
to steer (or balance) their children’s schooling trajectories, as the literature has
previously hypothesized; instead, it is likely due to structural forces outside of
parental control. This paper suggests some possible structural forces behind the
patterns uncovered.
A small number of scholars have examined if parental socioeconomic
status (SES) affects educational stratification dynamics among multiple
children within the same family (Becker 1981; Behrman et al. 1995; Conley
2004; Gaviria 2002; Dahan & Gaviria 2003). Relying on the parental
investment paradigm (begun by Becker & Tomes 1976; 1986), they
investigate if parents invest in their various children’s educations
differently depending on the family’s total budget. Specifically, they ask:
Because of SES differences between families, do some families produce
children virtually identical on educational attainment (low sibling
educational inequality), while other families produce very educationallydifferent children (high sibling educational inequality)?
Tentative evidence to date suggests that increased parental SES reduces
the amount of educational inequality between siblings in the United States
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(Conley 2004; Gaviria 2002). A child from high SES parents is more
likely to have a sibling with a similar (relative) schooling level than a low
SES child, who is further away educationally from his or her sibling.
While this result contradicts Becker & Tomes’ original predictions,
researchers have explained it, by arguing either: (a) given extensive
resources, high SES parents actively compensate for ability disparities
between their children by over-investing in the less able siblings to raise
them to the level of the more able ones, thereby lowering sibling
inequality; or (b) low SES parents, given a budget constraint, are forced
to actively exacerbate the children’s ability disparities by withholding
investment in the less able siblings in order to provide full educational
funding for the most able ones, thus increasing sibling inequality; or (c)
both processes are at work. These theories have been applied both to the
U. S. and internationally (for examples of the latter, see Dahan & Gaviria
(2003) on Latin America and Chu et al. 2008 on Taiwan).
Using a nationally representative sample of American siblings, I first
aim to confirm that sibling educational inequality is lower among children
of high SES parents. I then use a simple simulation – which involves
comparing the educational inequality between actual siblings to the
inequality between pseudo-siblings with virtually identical SES
backgrounds, but with no actual kinship relation to each other – to test
for the presence of an active parental investment process, which has been
assumed by previous researchers. I then consider a more social-structural
process as an alternative explanation (Raftery & Hout 1993; Hout &
DiPrete 2006). There may be less educational inequality among high SES
siblings simply because there is less educational inequality among all high
SES children in general, while there is greater educational inequality
among low SES children (and by extension, among low SES siblings).
This fact – combined with a “threshold” model (Breen & Goldthorpe
1997), where parents try to prevent children from falling below their
own SES position, yet are not as concerned about how high children rise
up the education ladder – could produce the same results, without having
to posit differential parental investment decisions. The simulation
technique allows us to adjudicate between the typical economic
explanation and this new one.
By testing the parental investment perspective, this paper contributes
to a chorus of sociologists, especially economic sociologists, who argue
economic models often rest on unrealistic notions of hyper-rationality
of agents (for a review, see Rona-Tas & Gabay 2007). Many highlight
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how the overall structure of socioeconomic opportunity seeps into our
most intimate relationships, including among siblings (Conley 2004;
Zelizer 2005). My research indicates that interpreting findings through
the individualistic and homo economicus lens of parental investment models
may be uncalled for, when models linked to the educational opportunity
structure as a whole may produce more credible results.
By proposing multiple siblings from the same family as a new unit of
analysis for stratification, this paper also advances our thinking on siblings.
Ever since Blau & Duncan (1967), research indicates that siblings impact
individuals’ educations through their sheer number, decreasing educational
attainment as sibship size grows (Steelman et al. 2002). I test a relational
model of educational attainment instead (like Conley 2004; Heflin &
Pattillo 2006), where families should be evaluated not only regarding
one child’s educational attainment, but regarding the reliability of
producing that level of education in multiple children (Scarr & Grajek
1982). Reliability is crucial since one sibling is not easily replaceable for
another; the correlation between the educational attainments of siblings
is surprisingly low, only 0.5 (Solon et al. 2000).
THEORETICAL FRAMEWORK
Parental SES, Parental Investment Decisions and Sibling Inequality
The small literature on parental investment behaviors and sibling
inequality has produced a number of conflicting theories, all tied to Becker
& Tomes (1976; 1986). They originally predicted that as parental SES
increases, educational inequality among siblings will grow, where “SES”
is narrowly construed as parents’ wealth and income. On the one hand,
high SES parents will invest in more scholarly siblings up to the marginal
rate of return on that educational investment, but will withhold
investment in weaker siblings, due to their predicted lower return on
parental wealth. Parents want their children to have equal total living
standards ultimately, and so high SES parents will compensate less
endowed children with inter vivos transfers and bequests later in life to
bring them in line with more endowed children who achieved their living
standard via advanced schooling. They predict: Siblings from low SES
parents, on the other hand, will have more educational equality because
their parents will recognize the family does not have the wealth holdings
required to compensate the less endowed siblings with financial transfers
and bequests later in life, to bring them in line with the child who received
more initial investments. Low SES parents will make suboptimal but
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equal investments in each child’s human capital, rather than giving more
to some based on their abilities. They predict that as parental SES increases,
educational inequality among siblings will grow. No evidence has been
found for this hypothesis.
Given lack of empirical support, two challenges have been made to
this theory, which either singly or in combination, would predict that as
parental SES increases, educational inequality between siblings decreases.
One theory argues, high SES parents will actively work to minimize the
human capital differences between their children. Behrman et al. (1995)
developed the “compensating hypothesis,” where parents compensate
for ability deficits in lesser endowed siblings, trying to even out human
capital levels, so that eventual earnings levels will be more equal, mitigating
the need for inter vivos transfers to less able children later in life. Parents,
when faced with children heading in opposite educational directions,
will try to raise laggard children up to some family goal by over-supporting
the weaker siblings, even if those investments are suboptimal in Becker’s
sense (Behrman et al. 1995). Conley speculates that “when parents have
lots of ‘class’ resources to go around—time, money, social connections—
children often are more alike since parents don’t have to ‘choose’ between
them and can actively compensate for disparities in skill or pluck. (Think
of the Kennedys or the Bushes.)” (Conley 2004: 6).
The other theory argues that low SES conditions – i.e., having “limited
opportunities and resources” – “may elicit parenting strategies that
accentuate sibling differences by directing family resources to the betterendowed siblings” (Conley & Glauber 2008: pp. TBD). Conley’s (2004)
hypothesis is that since low SES families face budgetary constraints, they
are forced to choose between their desire to efficiently allocate their
resources and their desire to aid all their children in achieving equivalent
education levels; and they will choose to efficiently invest in the better
endowed siblings. Dahan & Gaviria (2003) have also developed this theory
in much the same way, and tested it using data from multiple Latin
American countries, finding some support.
Using the Panel Study of Income Dynamics (PSID) and the Health
and Retirement Survey (HRS), Gaviria (2002) finds that siblings from
families with high wealth have lower educational inequality than siblings
from families with little to no wealth. In Conley’s (2004) interviews, he
finds evidence that low SES parents make investment choices in favor of
one or two siblings at the expense of other less academically-oriented
ones. In quantitative work (also using the PSID), Conley & Glauber
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(2008) find weak – but directionally correct – differences between high
SES and low SES families. The sibling education correlation for children
whose mother had 13 years of education or less (his proxy for low SES)
is lower than for children whose mother had more than 13 years of
education. Lastly, using the National Longitudinal Survey of Youth
(NLSY79), Mazumder (2004) finds that siblings from above-average
income families have slightly higher educational correlations compared
with siblings from below-average income families.
Overall, increased parental SES ought to reduce educational inequality
between siblings. Yet this result could occur either because of active
parental differential investment (as the scholars above argue, but do not
directly demonstrate) or because of an alternate theory (outlined below).
Social Structure, Parental SES and Sibling Inequality
A social-structural explanation for the above results is quite plausible if
two conditions are met: (1) children of high SES parents fall into a tighter
“landing area” of educational destinations, compared to children of low
SES parents (Raftery & Hout 1993; Hout & DiPrete 2006); and (2) sibling
inequality reflects the overall educational distribution of children from
the same SES, such that a narrow educational distribution for high SES
children translates into low educational inequality among high SES
siblings, precisely because they are high SES children – and vice versa for
low SES children. Figure 1 illustrates this idea. In this hypothetical
example, the box-and-whisker plots indicate that low SES children have
a very wide range of ultimate educational attainments, with middle SES
children having a less wide range of such attainments, and high SES
children having a very narrow range of ultimate amounts of schooling;
this is Condition 1. The arrows indicate the possible correspondence
between the macro-level educational inequality found within each SES
class and the micro-level inequality found among a pair of siblings from
that SES class; this is Condition 2. There are two possible reasons why
the micro-level inequality found among a pair of siblings from a given
SES class would mirror the amount of macro-level educational inequality
found within that SES class.
One, a threshold model could produce the observed pattern. Parents
try to prevent children from falling below their own SES position yet
are less concerned about how high children rise up the education
distribution (Breen & Goldthorpe 1997). It is possible that ceiling and
floor effects are stronger for children of high SES parents than for low
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Figure 1: Hypothetical Correspondence Between SES-Specific Overall Educational
Inequality and Inequality Among Siblings of the Same SES Class
Overall (PopulationLevel) Educational
Distribution, by SES
Sibling Educational
Inequality, by SES
High
SES
High
SES
Middle
SES
Middle
SES
Low
SES
Low
SES
SES ones, meaning that high SES children typically fall into a narrower
range of educational attainment levels than children from low SES families
(Hout & DiPrete 2006; Mare & Chen 1986). Low SES parents, since
their expected “floor” is low to begin with, can push their children to
rise over a wider range of educational levels more easily. This explanation
differs from the parental investment model because the education
distribution itself, which is external to parental control, determines the
amount of sibling educational inequality; not individual parental
assessments of, and differential investments in, their portfolio of children,
made in light of the overall family budget. Parents can invest however
much they think is necessary to make sure their children do not fall
behind, but those investments will translate into lower inequality among
high SES siblings, since their likely ultimate educational placements were
less unequal than for low SES children to begin with.
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Higher educational inequality among children from low SES families
may also be driven by a second reason: upward mobility public policies
directed to low SES children. They often provide opportunities for only
a limited number of low SES children to move up, and not all siblings
from the same family can access the limited number opportunities. Conley
& Glauber (2008) note that “society writ large could generate a larger
variance in outcomes for disadvantaged families through a process of
tokenism that generates apparently random influences when viewed at
the family level” (pp. TBD). Some low SES children are tapped for special
free programs (e.g., Prep for Prep, charter schools, after-school programs)
and rise dramatically within the education system as a result, while their
siblings because of bad luck, bad timing or lower initial apparent potential,
did not get access to the same types of programs and attain lower levels
of schooling. Under this scenario, if the social policy consequences from
the low SES educational distribution could be accounted for, the greater
sibling distance amongst low SES children than amongst high SES children
would diminish.
DATA AND METHODS
Sample
I use a nationally-representative sample of siblings from the General Social
Survey’s (1994) module, the Study of American Families (SAF), fielded
by Robert M. Hauser and Robert D. Mare; the response rates are quite
high with the GSS in general, approximately 71 per cent. Beyond the
usual battery of questions asked in the GSS, they asked the respondent
for information on one randomly-chosen sibling (for an introduction to
the SAF, see Hauser & Mare 1994).1 Therefore, my analyses will apply
to two siblings per family. I limit my analysis to families where: both
siblings are full siblings (to ensure they had the same parents) and both
are 25 years of age or older (to ensure they have completed their
educations). I use GSS-provided sample weights in all analyses, making
my results representative of the non-institutionalized, civilian, Englishspeaking adult United States population in 1994.
I utilize the following variables in my analyses.
1. Dependent Variable. Sibling Educational Inequality. Following
Gaviria (2002), I use a scale-free measure of educational inequality (Allison
1978; Sørensen 2002), the coefficient of variation (CV), which is calculated
as follows: CV = (σi/µi)*100, where σi is the standard deviation of the
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ith family’s educations (i.e., generated from both siblings), divided by µi,
the ith family’s mean educational attainment (i.e., generated from both
siblings). Individual educational attainment is the number of years of
formal schooling reported by the GSS respondents about themselves and
their siblings, top-coded to 20 years. Without direct observation of
parental behaviors toward their children, most researchers rely on total
years of education as “an indirect, but behavioral measure” of parental
investment patterns (Hopcroft 2005: 1116). In fact, Kim (2005) finds that
each $1,000 educational contribution from parents translates into a 0.067
increase in the total number of years of schooling for their children.
Table 1
Summary Statistics (Unweighted), Full Siblings, Aged 25 or Older
Coefficient of Variation, Sibling Educational Attainments
Parental SES
Total Number of Siblings
Age Difference, Pair
Average Age, Pair
Mixed Sex Pair
African-American
Average Educational Attainment, Pair
Obs.
Mean
St. Dev.
837
837
837
837
837
837
837
837
10.59
0.04
3.56
4.98
45.79
0.50
0.10
13.56
12.95
1.36
2.61
3.79
13.78
2.46
2. Independent Variable. Socioeconomic Status (SES). SES refers to the
resources that individuals accumulate and the lifestyles they embody,
given their joint positions in the education, labor, marriage and property
markets (Weber 1978). My proxy for SES is generated from the first
factor score from a principal components analyses of three measures: (1)
the highest total number of years of schooling attained by the respondent’s
parents, whether mother or father, which is generally found to be the
single best predictor of children’s educational attainment (Blake 1989);
(2) the highest prestige ranking score of either parent’s occupation when
the respondent was 16 years of age, which is indicative of “permanent
income” and of the family’s resource-generating capacity (Blau & Duncan
1967; Goldthorpe 1996); and (3) a 5-point Lickert item asking respondents,
compared with American families in general, how large was their family’s
income when they were age 16, ranging from far below average to far
above average. The Eigenvalue on the first principal component is above
1 (1.87) and accounts for most of the total variance (62.32%).
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3. Control Variables. Total Number of Siblings. The total number
of siblings that the respondent and sibling have, including step-siblings,
half-siblings and adopted siblings. Sibling Age Difference. The number
of years of age separating the two siblings in the pair.3 In addition to
the age difference, I include the Sibling Average Age (of the pair) to
account for positive drifts over time in educational attainment or other
cohort effects (Hauser & Sewell 1985). Sibling Sex Mix. I constructed an
indicator variable for whether the pair was mixed-sex or same-sex (where
“same-sex” means either two males or two females). Race. I included an
indicator variable for whether the respondents are African-American,
or not.
Techniques of Analysis
To test my hypotheses, I rely on two techniques.
1. OLS Regression. In order to assess the relationship between parental
SES and sibling educational inequality, I run an OLS regression,
represented as follows:
Yi = α + β1Xi + β2Zi + ε,
where Yi is a measure of sibling educational inequality, Xi is the SES of
the siblings’ parents, and Zi is a vector of other characteristics of the
family or of the sibling pair.
2. Simulation. To adjudicate between parental investment theories
and a social-structural one, I run a simple simulation, which is
implemented as follows. I ranked all families according to parental SES.
I then gave every family a new sibling drawn from the family right above
it.2 In this way, I made pseudo-siblings, with virtually identical SES
backgrounds, but with no real kin relation to each other. I then reran the
above OLS regression equation on this sample of pseudo-siblings, and
compared the resulting estimates on the parental SES coefficient to the
original ones obtained above. If parental investment decisions are really
responsible for the SES gradient on sibling inequality, when I rerun the
regression on pseudo-siblings, the coefficient on parental SES should be
greatly attenuated (close to zero) because I removed the ability for parents
to differentially invest in their children. After all, in the simulated world,
parents cannot assess both children’s abilities and long-term prospects,
since they only know one child, nor can they decide how to invest across
both children, since they only actually invested in one of the two children.
If parents could do nothing to affect the inequality of “fake” siblings, but
the fact that the coefficient on parental SES is the same as in the original
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OLS model, this means that something other than parental actions must
be driving the relationship. To date I am not aware of other researchers
who have used such an approach.
RESULTS AND DISCUSSION
As Table 1 indicates, the mean sibling coefficient of variation on education
in the population is 10.59. The average sibling pair’s standard deviation
in education is about 10.6 per cent the size of their mean educational
attainment level. That is a meaningful amount to differ on; it translates
into almost two years difference in years of schooling for the average
sibling pair.
Our first question is: Does the coefficient of variation vary by the
SES of the family? The results from the multivariate OLS models directly
answer this question and they are conclusive. Table 2 shows that as
parental SES increases, the amount of sibling educational inequality
decreases noticeably (b=–1.50, p<.01). The magnitude of the SES factor
is quite large, where the Beta (standardized) estimate is -0.16; for every
one standard deviation increase in parental SES, sibling inequality is
reduced by about a sixth of a standard deviation. This result is consistent
with either the “low SES reinforcement of differences” hypothesis or the
“high SES compensating” hypothesis. This result is, however, also
consistent with a social-structural perspective, where there are societywide processes that spread low SES siblings further apart from each other
than high SES siblings.
Also of note in the model, mixed-sex siblings are further apart
educationally (b=1.85, p<.05). This is consistent with historical evidence
that boys were given greater educational resources than girls until recently,
even within the same families (Buchmann & DiPrete 2006). The average
age of the pair is also positively associated (b=0.06, p<.10) with
educational inequality, which is consistent with the life-course
standardization model of historical change, where siblings are closer
educationally in recent decades compared with earlier in the century
(Shanahan 2000).
In supplementary analyses, I find that most of the educational
inequality among low SES siblings is focused on differences below high
school graduation levels, and is not found in one sibling “crossing over”
to get at least some college education (results available upon request).
By running a simple simulation, it is possible to rule out some
proposed mechanisms that cannot be behind the strong negative
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Table 2
OLS Regression for Inequality in Educational Attainment among
Full Siblings, Aged 25 and Older
Parental SES
Total Number of Siblings
Age Difference, Pair
Average Age, Pair
Same Sex Pair (reference)
Mixed Sex Pair
All Other Races/Ethnicities (reference)
African-American
Constant
Observations (Pairs)
Adjusted R2
Coeff.
Beta
-1.50**
(0.44)
0.31
(0.21)
0.02
(0.12)
0.06+
(0.03)
1.85*
(0.89)
-1.74
(2.02)
5.94**
(2.04)
837
0.042
-0.16
0.06
0.01
0.06
0.07
-0.04
•
Robust standard errors in parentheses.
Data weighted by sample weights provided by GSS.
**p<.01 * p<.05 + p<.10 (two-tailed)
association between parental SES and sibling educational inequality. Recall
that I ranked all families by SES, and then gave every family a new sibling
drawn from the family right above it. I made pseudo-siblings, with
virtually identical SES backgrounds, but with no actual kinship relation
to each other. I then reran the original OLS regression and compared the
new parental SES coefficient to the original one. This process of shifting
siblings down one family does introduce tremendous randomization;
indeed, the “fake” siblings only have an educational correlation of 0.23
vs. the real siblings of 0.48.
Does it make a difference in terms of the relationship between parental
SES and sibling inequality? As Table 3 shows, it does not make a difference.
The coefficients on parent SES in the real sibling model and the pseudosibling model are statistically indistinguishable: b=-1.61 for the real
siblings, and b=–2.30 for the pseudo-siblings. The t-test for a difference
between these two coefficients yields a t-statistic of -0.75, p=0.45 (twotailed).4 Their Betas are also very similar: 0.17 for the real siblings and
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Table 3
OLS Regression for Inequality in Educational Attainment among Real (Full)
Siblings vs. Pseudo-Siblings (Simulation), Aged 25 and Older
Real Siblings
Pseudo-Siblings
(Simulation)
Coeff.
Beta
Coeff.
Beta
-1.61**
(0.46)
-0.17
-2.30
(0.38)
-0.20
Age Difference, Pair
0.05
(0.12)
0.01
0.03
(0.05)
0.02
Average Age, Pair
0.07*
(0.03)
0.07
0.13*
(0.05)
0.09
Parental SES
Same Sex Pair (reference)
Mixed Sex Pair
Constant
-
-
-
-
1.78*
(0.88)
0.07
0.43
(1.01)
0.01
6.43**
(1.80)
•
8.20**
(2.30)
•
Observations (Pairs)
Adjusted R2
837
0.045
906
0.059
**p<.01 * p<.05 + p<.10 (two-tailed)
Robust standard errors in parentheses.
0.20 for the “fake” ones. In short, there was no reduction in the SES
coefficient in the pseudo-sibling analysis; if anything, it looks like the
pseudo-sibling simulation has made the SES effect more pronounced. As
parental SES increases for the pseudo-siblings, their “sibling inequality”
decreases; just as it did for the real siblings.
What are the implications of this result? Clearly, in the simulation,
parents could not have been doing any of the things proposed by the
conventional wisdom because I switched out one sibling for a “fake” one
from a different family, who only shares the same family SES level. Parents
could not have assessed both children’s abilities and long-term prospects,
since they only knew one child, nor could they decide how to invest
across both children so as to alter the initial sibling differences, either
reinforcing (if low SES) or minimizing (if high SES) differences, since
they only actually invested in one of the two children. The parents could
do nothing to affect the inequality of “fake” siblings, but the fact that the
overall SES pattern is essentially the same illustrates that siblings from
high SES parents must be more educationally equal based on socialstructural factors, beyond parental control.
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Table 4
Coefficients of Variation of Educational Attainments for SES-Specific
Populations and Among Siblings, by SES
Low SES Families
High SES Families
Low SES CV ≠ High SES CV
Overall Population
Inequality
Sibling
Inequality
17.0
(n=925a)
21.8
(n=1046 a)
**
9.1
(n=386b)
11.8
(n=452 b)
**
**p<.01 * p<.05 + p<.10 (two-tailed)
a. Ns are for individuals. b. Ns are for sibling pairs.
A more social-structural explanation for the relationship between
parental SES and sibling inequality might be found if two conditions are
met: (1) children of high SES parents fall into a tighter distribution of
educational destinations, compared to children of low SES parents; and
(2) sibling inequality mirrors the overall educational distribution of
children from the same SES, such that a narrow educational distribution
for high SES children translates into low educational inequality among
high SES siblings, and vice versa for low SES children. Both of these
conditions appear to be met. Condition 1 is met: as Table 4 indicates,
educational inequality among all high SES children is much lower
(CV=17.0) than among all low SES children (CV=21.8).5 Condition 2 is
also met: as Table 4 shows, among high SES siblings, their sibling
inequality is CV=9.1 vs. among low SES siblings, where their sibling
inequality is CV=11.8. This data provides further support for a socialstructural interpretation of the relationship between parental SES and
sibling inequality.
CONCLUSION
This paper advocated a stratification approach with a more relational
perspective. I shifted from an absolute framework to a relative one, where
educational inequality is modeled not between families, but within them.
By shifting focus, this paper has documented a subtle mechanism by
which SES operates to make children of high SES families in the United
States more equal in their educational levels. While sibling educational
inequality seemed like it might result from a random mixture of family
interactions, it is actually affected by the overall socioeconomic system.
As parental SES increased, the amount of sibling educational inequality
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decreased noticeably as well. Even if many siblings do not experience
this consciously as inequality, this difference in educations between
siblings still matters because sociologists have also noted possible negative
consequences of high sibling inequality, including the exacerbation of
lifestyle differences, the limitation of information exchange and strained
social relationships between siblings (Heflin & Pattillo 2006; Van Gaalen
et al. 2008).
This paper shows it is likely that certain very popular hypothesized
mechanisms are not operative. The proposed mechanisms linked to
parental preferences, investment strategies, and the like are found to
not likely matter. These hypotheses are also tied to the narrow
economics focus on “investment” and “class,” both of which are
exclusively defined in terms of money. Actual mechanisms must be
more structural and indirect in nature. Parental actions do not appear
to be behind the SES patterns we observe. Parents clearly play a large
role in their children’s educational similarity, but it is not the parents
as agents of a given SES class, or responding to a given SES situation,
that treat their children differently, but simply belonging to that SES
class at all.
Previous researchers rightly saw that high SES siblings have less
educational inequality, but they attributed it to the wrong source. They
looked for a micro-explanation involving parental investment behaviors,
when the result appears to be really driven by the fact that high and low
SES children – in general – have different educational destination
distributions. Siblings within their own families merely reflect this general
pattern in society at large.
By finding that social-structural reasons are more consistent with the
lower sibling educational inequality found among higher SES families, I
highlight new possible mechanisms. On the one hand, this result could
be caused by the sibling-blindness of many upward mobility public policies
directed to low SES children. Upward mobility might emerge because
policies only provide opportunity for a limited number of low SES
children to move up, and not all the siblings from the same family have
access to this limited number of slots or opportunities. On the other
hand, a threshold model (where families invest to prevent children from
falling below their own SES position) would produce the very pattern
seen – small distance for high SES children, and larger distance for lower
SES children; as long as the floor and ceiling range is narrower at the top
of the SES distribution than at the bottom of it, which it is. Researchers
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197
need to examine sibling inequalities in more detail and in a new light to
confirm which forces are at work.
From a stratification perspective, because inequality processes in
general favor the powerful, the fact that high SES parents minimize sibling
inequality suggests that sibling educational closeness is a real good. Sibling
equality is a less obvious privilege, maximized by some classes more than
others, and generally not noticed by anyone; this is reminiscent of how
Schwartz (1974) showed that time (in very simple, everyday ways) can
be monopolized by high SES individuals, since low SES people are asked
to wait, while high SES people get to shortcut queues.
Stratification researchers have looked to sibling correlations for an
overall measure of the family of origin’s role in class reproduction. The
sibling correlation of 0.5 has been interpreted to mean either: (A)
“individual” factors like talent or luck still greatly impact educational
success, since half of all academic variance is still within the same families
(Conley 2004; Jencks 1979; Solon et al. 2000); or alternatively (B) familybased reproduction is still quite persistent, since siblings reach much more
similar educational levels than their shared demographic (dis)advantages
would predict (Bourdieu & Passeron 1990).
While both of these interpretations are valid as far as they go, I have
advanced this debate by providing additional evidence of cleavages in
society that impact the family. Since greater uniformity is found among
high SES families, this is another piece of evidence in favor of (B) that
family-based reproduction still matters, and it is obscured when looking
at a population-level parameter like the sibling correlation. Based on these
analyses, the proverbial glass appears to be half-empty, not half-full, for
low SES families because high SES families still manage to equalize more
of their children’s educational futures. In this way, I extend the literature
on family background and educational mobility (Hauser & Wong 1989;
Hauser & Sewell 1989; Hauser & Mossel 1985; Solon et al. 2000). Trying
to find a global measure of family background’s role on attainment must
be tempered, therefore, by an awareness that there is often meaningful
subgroup heterogeneity in how family background influences sibling
variances in education.
This paper’s main limitation is that it does not directly test the
proposed mechanisms behind sibling inequality. Future research would
benefit from looking at parental investment decisions directly, as well as
parents’ prospective assessments and expectations of their children’s
ultimate schooling levels.6 Also, researchers should look to how public
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INTERNATIONAL JOURNAL OF SOCIOLOGY OF THE FAMILY
policies may exacerbate sibling inequality by not having an explicit
preference for siblings of current participants in programs.
A key extension of this paper would look for whether similar
dynamics hold in other countries. We have some evidence that different
countries have different sibling correlations on education and earnings
(Sieben et al. 2001; Björklund et al. 2002; Sieben & De Graaf 2004). We
could imagine high SES families have less ability to equalize their sibling
educational attainments in countries where the educational system is
tracked earlier or more entrance-exam-based; or where private family
financing for higher education is less common than in America; or where
family-life transitions are more equally distributed among classes. This
would be fruitful to explore.
Acknowledgments
I would like to thank Tom DiPrete and Peter Bearman for valuable comments
on preliminary versions of this paper. An earlier version of this paper was
presented at the 2007 annual meeting of the Eastern Sociological Society in
Philadelphia, where I benefited from Chuck Willie’s helpful feedback.
Notes
1.
The SAF staff then attempted to contact this sibling for additional questions;
telephone interviews were conducted with 1,155 of those siblings (for more
information, see Warren 2001); this additional information on this smaller
sample is not used in this paper.
2.
Where multiple families shared the same exact SES rank, I broke ties
randomly. I kept the GSS-respondents in rand order, but shifted down their
randomly-chosen siblings by one family.
3.
Since the GSS does not provide exact birth dates, these ages are imprecise
within (at most) 12 months.
4.
Using the formula:
5.
“High SES” and “low SES” refer to above and below mean parental SES,
respectively.
6.
Additional possible objections concern: (A) measurement error.
Measurement error is not likely driving these results, since it is not thought
to be especially high with regard to siblings. Hauser and Wong (1989) note
that “proxy reports of status variables by adult offspring about one another
are just about as accurate as are self-reports” (168). (B) Parental SES is affected
by age of the parents. I ran models adding in maternal and paternal age, and
PARENTAL SOCIOECONOMIC STATUS AND SIBLING EDUCATIONAL...
199
while those variables were significant, they do not alter my results
appreciably (results available upon request). (C) Other measures of sibling
inequality would produce different results. I run models with other measures
of sibling inequality, like the absolute difference in educations. Results were
equivalent (results available upon request). (D) The top-coding on educational
attainment artificially induced lower overall educational inequality among
high SES children, and siblings, but when I remove all the top-coded cases,
the results are equivalent (results available upon request).
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