1. Number of Siblings in Childhood and the Likelihood of Divorce in Adulthood
Mary Lopez
Economics of the Family
Fall 2014
Abstract
Despite a decline in fertility across many developed countries, relatively little is known
about the consequences of being raised with fewer children. Some studies indicate that
fewer siblings could have a positive effect, as children with few siblings tend to have
better educational outcomes. However, there has also been evidence that siblings could
increase social skills and lead to better long term relationships as an adult. With data from
the General Social Surveys 1972-2012, I attempt to quantify the effects of siblings on the
likelihood of relationship formation and dissolution. I find that there is no effect of
siblings on the likelihood of marriage or divorce, net of covariates.

2. 2
The question of whether there is a child quantity – quality tradeoff within a family
is one of the most studied topics in family economics. Most of these studies focus on the
dependent variables of educational attainment or wages but few question how the number
of siblings a person has affects that individual’s ability to develop long-term meaningful
relationships. Specifically, this paper will examine how the number of siblings affects the
likelihood of marriage and divorce. This topic has been underexplored despite being
particularly interesting from a policy standpoint. Especially as the fertility rate decreases
in many developed countries and families become smaller, whether by requirement as in
China, or by natural progression, it will be important to understand the social
consequences that may arise from this change in family structure.
There are multiple channels by which family size might affect marriage and
divorce. For example, the resource dilution perspective states that parents have a finite
amount of resources such as time and money to give to their children, so the more
children there are, the less each child gets. As such, children with more siblings should
have more negative outcomes than those with few or no siblings. Proponents of this view
often point to studies showing that children with few or no siblings perform better in
school and on cognitive ability tests than children with many siblings (Blake 1981).
Using Blake’s work as a foundation, Downey analyzed data from the National Education
Longitudinal Study and found a consistent negative relationship between sibship size and
parental resources such as money saved for college, having a computer in the house, and
time spent speaking about school-related matters (Downey 1995).
However, there are several important questions raised to the resource dilution
theory. Much of the research on resource dilution stems from cross-sectional data that
may not fully control for the differences between parents who have many children versus

3. 3
those who have few. This could affect the ability to interpret the effect of siblings on
educational outcomes as causal. Additionally, an important limitation to the dilution
model is its nearly exclusive focus on educational or wage outcomes. Siblings matter in
many other ways and the focus on education effects may have obscured the positive
aspects of sibling interaction, which are explored next.
An alternative to the dilution model is that siblings serve as resources themselves,
separate from those provided by parents. Sibling relationships are particularly unique,
given their long duration, shared familial environment, heritage, and experiences, and
similar expanded network of relationships (White 2001). Growing up with siblings could
provide individuals with an opportunity to develop conflict resolution skills, which could
facilitate the maintenance of future relationships in adulthood. In addition, siblings may
indirectly affect development by influencing how parents rear their children. After
success with previous children, parental confidence may increase along with the use of
effective parenting techniques.
Both the resource dilution model and the siblings as resources model are plausible
and further previous research on both models is presented in the next section, in the hopes
of creating a clearer picture of the expected effect of siblings on marriage and divorce
patterns. In this paper I attempt to contribute to the research on this topic by quantifying
the effects of the number of siblings on the likelihood of marriage and divorce, while also
considering the possible effect of the sex mix of the family.
Previous Literature
In much of previous economic research, such as Arleen Leibowitz’ article “Home
Investment in Children,” there has been a widely observed negative association between
family size and academic achievement (Leibowitz 1974). Despite previous beliefs

4. 4
however, recent research has found that there is not significant evidence of a child
quantity – quality tradeoff within families. Agrist, Levy, and Schlosser use instrumental
variables to account for the likelihood of omitted variables in previous estimates of the
effect of family size and finds that the IV estimates are considerably smaller than the
corresponding ordinary least squares (OLS) estimates (Angrist 2010).
While there may be less of a child quantity – quality tradeoff than previously
thought, family size still seems to have some effect on educational achievement and
wages. Black, Devereax, and Salvanes argue that much of the negative relationship
between family size and economic outcomes seems to be driven by birth order. A higher
birth order is negatively associated with wages and average years of education, especially
for women. Consequently, as families get smaller, average outcomes will improve, but
only because there are fewer high birth order kids (Black 2005). Behrman comes to a
similar conclusion as Black, finding that birth-order differences occur despite parental
preferences or prices by birth order favoring later borns, apparently because of stronger
endowment effects that favor firstborns (Behrman 1986).
Clearly, family size and birth order have been shown to have an effect on
economic and academic factors, but little research has been done on the effect of siblings
on divorce. Doug Downey and Donna Bobbitt-Zeher explore this relationship in their
forthcoming paper, “Number of Siblings During Childhood and the Likelihood of
Divorce in Adulthood,” and find that more siblings means less chance of divorce as an
adult. Downey finds that each additional sibling a person has, up to about seven, reduces
the likelihood of divorce by 3% (Downey 2014). Even more intriguingly, it doesn’t
appear to be the difference between being an only child and having any siblings at all that
was significant, but rather how family dynamics change with the incremental effect of

5. 5
each additional sibling. One would expect that having any siblings at all would give an
individual the experience with personal relationships need to have a successful marriage,
but it appears that each additional sibling increases the ability of an individual to deal
with a marital relationship as an adult. Downey uses data from the General Social Survey,
which involves interviews with approximately 57,000 adults across the United States at
28 points between 1972 and 2012. To account for the argument that smaller families are
more likely to have a single parent or some other issue that might adversely affect
children in their future marriages, Downey controls for education, socioeconomic status,
family structure, race, age at marriage, whether the respondents had children, gender role
attitudes and religious affiliation of both the respondents and their parents. Even with
controlling for all of these factors, the relationship between siblings and later divorce
remained relatively unchanged.
One possible explanation Downey offers for this relationship between siblings
and later divorce is that having siblings allows for better social and interpersonal skills,
that later translate into helpful skills in a marriage. This idea is supported by an earlier
work by Downey, in which he found that kindergarteners negotiate peer relationships
better when they have at least one sibling (Downey 2004). Another possible explanation
is that sibling relationships are intimate, with both positive and negative emotions, and
could match the dynamics that many individuals have in marriage relationships.
Downey’s findings are very intriguing but further research is needed on the
relationship between siblings and later divorce, especially since this link has been
explored by so few. While Downey explores the effect of sibship size in general, it would

6. 6
be interesting to see if the effect of siblings on later divorce varies by gender or if the sex
mix of the family has any effect.
Methods
Sample
Data from the General Social Surveys (GSS) from 1972-2012 will be used in this study.
The GSS represents data from approximately 57,000 adults collected at 28 points over
four decades and the data are particularly well suited for this study because of the level of
detail on measures of marital and family composition outcomes. The key independent
variable is number of siblings, which is measured in a continuous fashion and includes
full, half, step, and adopted siblings. The primary dependent variable of interest is a
binary variable created for ever having been divorced. There are also a number of
variables that need to be controlled for, which are discussed below, and are included in
the vector term Xi. The basic OLS equation is as follows:
Ever having been divorcedi = β0 + β1number of siblingsi + XiB2 + εi
Independent variables
The fact that the kinds of parents who have many children are potentially different
than those that have few makes it difficult to isolate sibling effects. As a result, I attempt
to control for potential differences between family sizes that might also affect marriage
and divorce. Background factors are considered first. Respondent’s education is captured
with two dummy variables, high school or less and completed college, with high school
or less serving as the base case. Similarly, I measure years of mother’s education, which
serves as a proxy for socioeconomic status of family of origin, with corresponding
dummy variables mother high school or less and mother completed college. Again
mother high school or less serves as the base case. Race is gauged with three

7. 7
dichotomous dummy variables based on self reporting of racial group: white, black, or
other, with other serving as the base case. Sex is captured with a dummy variable for
males, with females as the base case. The number of brothers the respondent reported
having is captured by the continuous variable, number of brothers. Respondent’s age is
captured with a continuous variable measured in years. Given the potential for non-linear
effects, models include a term for age squared as well. Finally, I include a continuous
variable for survey year to control for the time at which the survey was administered.
I also consider variables for economic status, family formation, and geography. In
all models I include family income, a logged, continuous variable that measures total
family income in constant 2000 dollars. I also include a dummy variable for home
ownership, where 1 indicates that the respondent owns their own home and 0 indicates
that they do not. Based on the respondent’s location at the time of survey, I created
dummy variables for geographic region, coded as North, East, West, and South, with
South serving as the base case.
Finally, since large families may foster more traditional worldviews that may
affect marriage and divorce patterns, I include measures for gender role attitudes and
religiosity. I created an index to measure gender role attitudes, based on the level of
agreement with the following statements: 1) “It is much better for everyone involved if
the man is the achiever outside the home and the woman takes care of the home and
family,” and 2) “A preschool child is likely to suffer if his or her mother works.” The
responses to each statement were captured on a scale of 1 (strong agreement) to 4 (strong
disagreement) and then the responses were added to create an index with possible values
of 2 to 8. To capture religion, I created two measures. Religious affiliation is a dummy
variable coded 1 for reporting any religious affiliation and 0 for reporting none. I also

8. 8
measure religious attendance based on answers to the question, “How often do you
attend religious services?” Dummy variables are created for attending religious services
less than monthly, less than weekly, and weekly, with weekly serving as the base case.
It is important to note that several of these variables, particularly respondent’s
education, family income, home ownership, and gender role attitudes, could be potential
mechanisms through which the effect of the number of siblings is working. As such, the
results will be discussed differently for models that control for these factors and those
that do not.
Dependent variables
I consider two dependent variables. First, I constructed a binary variable for ever
having been married. The reference category is never been married. Second, for
respondents who have ever been married, I created a binary variable for ever having been
divorced. Never having been divorced serves as the reference category. Together, these
two outcomes allow for a view into the effect of the number of siblings on relationship
formation and dissolution.
Table 1 shows the descriptive statistics from the sample, and has been broken
down into those who have ever been divorced and those who have never been divorced,
to see if there are any obvious differences between the two groups that might indicate a
larger story. Those who have been divorced are generally older than those who have not,
with a mean of about 50 compared to 43, which is not too surprising considering it takes
time to get married and subsequently divorced. Both categories have grown up with
multiple siblings (mean = 3.8 for divorced and 3.7 for never divorced) though there is
significant variation in sibship size (standard deviation = 3.2 and 3.0, respectively).
Considering current fertility trends in the U.S., this seems like an unusually high number

9. 9
of siblings but that could possibly be explained by the fact that it includes step and
adopted siblings as well. The mean number of brothers is low for both categories, though
there is some variation in that as well which could account for the low mean. Those who
have been divorced have more children on average, though there is also significant
variation in number of children as well. Surprisingly, on average there does not seem to
be a difference in religious affiliation or gender role attitudes between those who have
been divorced and those who have not. There are only 5,504 responses for those who
have ever been divorced, which seems low considering there are nearly 40,000 for never
having been divorced but that is likely due to the fact that the second category includes
those that have never been married in the first place.
Analytic Strategy
I test the effect of sibling-ship size on the likelihood of ever marrying using the
full sample, considering first the bivariate and then the multivariate models. I then
predict the likelihood of ever having been divorced, specifically testing for the effect of
the number of siblings. Again, I test first the bivariate and then the multivariate models.
In the multivariate models, I focus on controlling for variables that have the potential to
affect the relationship between siblings and the marriage or divorce outcome. Finally, I
examine the effect of the sex mix of the family by testing the effect of the proportion of
brothers on the likelihood of divorce. For the regressions regarding sex mix, I report total
results, as well as separate results for both men and women.

10. 10
Results
Do siblings affect the likelihood of ever marrying? I predict the likelihood of marrying in
Table 2. Results indicate a significant but small positive of the number of siblings on the
likelihood of marriage, with each additional sibling increasing the likelihood of marriage.
Specifically, with no controls in the model as represented in Model 1, each additional
sibling is associated with a one percent increase in the odds of ever marrying. However,
that effect decreases with the addition of background, economic, and geographic controls
in Model 2, and decreases again to half a percent with the addition of religious and
gender role controls in Model 3, though the effect remains significant.
Does the number of siblings one has affect the likelihood of divorcing? Table 3 shows
results related to the effect of siblings on the likelihood of divorce. There is essentially no
effect of the number of siblings on the likelihood of divorce in any of the models,
whether bivariate as in Model 1 or with the controls as in Models 2 and 3.
Does the number of brothers one has affect the likelihood of divorcing? Table 4 shows
the results related to the proportion of brothers on the likelihood of divorce. In a one
sibling family, having a brother is associated with a 5.3% increase in the likelihood of
divorce, an effect that is statistically significant. As the number of siblings increases, this
effect decreases and is no longer statistically significant. All regressions include controls
for background, economic, and geographic variables, as well as religious affiliation and
attendance, and gender role attitudes.
Table 5 similarly shows the results related to the effect of the proportion of
brothers on the likelihood of divorce, considering only male respondents. The effect has a

11. 11
similar magnitude as the total results in a one sibling family, but is not statistically
significant. The effect is only statistically significant in three and five sibling families,
where an additional brother is associated with about a 3% increase in the likelihood of
divorce.
Table 6 shows the results when considering only female respondents. The effect
of an additional brother is again a similar magnitude as the total results in a one-sibling
family, about 5%, but again is not statistically significant. In fact, the effect is not
statistically significant in any family size.
Discussion
As the fertility rate decreases in many developing countries, more and more
children are being raised without siblings but social scientists know little about the
consequences of this change. I attempt to advance the literature in this area by examining
the relationship between the number of siblings and the likelihood of marriage and
divorce. My results indicate that siblings do not have a significant effect on the likelihood
of marriage or divorce. Even if the effect were working through one of the potential
mechanisms mentioned earlier, such as respondent’s education or income, the effect
should have been visible, statistically significant, and even potentially overestimated in
Model 1, considering that it includes no controls.
However, in a one-sibling family, having a brother is associated with a 5.3%
increase in the likelihood of divorce, which is rather surprising. Perhaps only interacting
with a brother does not lead to the same social skills as interacting with sisters does, and
leads to fewer conflict resolution skills and thus less successful long term relationships.
This interpretation is consistent with the other regressions within Table 4 as well,

12. 12
considering the effect of an additional brother decreases and becomes statistically
insignificant as the number of siblings increases. However, the results from Table 5 and 6
do not support this interpretation, as the effect is statistically insignificant, even in a one-
sibling family. Women’s likelihood of divorce especially does not seem to be affected by
the number of brothers, as the result is not statistically significant in any family size.
Men’s likelihood of divorce is still affected, though only in sibling sizes of three and five.
Further research into this phenomenon is needed to illuminate the inconsistencies within
these results.
The result that siblings have no effect on relationship formation and dissolution is
different from what Downey and Bobbitt-Zeher found in their 2014 paper, where they
found that each additional sibling decreases an individual’s chance of divorce by 3%.
This is likely due to the fact that Downey and Bobbitt-Zeher handle missing data with
multiple imputations. They replace missing values on independent variables with
plausible estimates developed from an imputation model constructed from all variables in
their regression models (Downey 2014). Had I pursued a similar strategy, I also may have
found a larger effect of siblings on relationship formation and dissolution.
Another reason for the perceived lack of effect could be that the resource dilution
effect and the siblings as resources effect are offsetting each other, making it seem that
siblings have no effect on long term relationship formation and dissolution. Future
research should attempt to separate those effects and quantify their effects. It would also
be interesting to find out if spacing has any effect, as one might expect that respondents
might benefit most from having a widely spaced older sibling who is relatively mature.
The GSS also represents data only from the United States, where the decline in fertility
has been less steep than in European countries so it would be interesting to expand this

13. 13
question to countries like Italy and France where fertility rates have dropped well below
replacement.

14. 14
Table 1
Descriptive Statistics, General Social Survey 1972-2012
Year of survey 1992 11.5 1991 11.7
Age 49.7 14.1 43.2 16.9
Number of siblings 3.8 3.2 3.7 3.0
Number of children 2.4 1.7 1.7 1.7
Family income 50686 39783 41673 36547
Number of brothers 0.09 0.51 0.08 0.49
White 0.86 0.33 0.81 0.38
Black 0.10 0.30 0.13 0.33
Other 0.02 0.16 0.04 0.21
Male 0.43 0.49 0.44 0.49
Homeowner 0.37 0.48 0.27 0.44
North 0.24 0.43 0.26 0.44
East 0.13 0.33 0.20 0.40
West 0.21 0.41 0.19 0.39
High school or less 0.56 0.49 0.49 0.49
Completed college 0.18 0.39 0.25 0.43
Mother high school or less 0.83 0.37 0.77 0.41
Mother completed college 0.07 0.25 0.10 0.30
Attends religious services less
than monthly
0.54 0.49 0.49 0.49
Attends religious services less
than weekly
0.15 0.36 0.16 0.37
Attends religious services weekly 0.30 0.45 0.34 0.47
Religiously affiliated 0.90 0.28 .89 0.31
Composite of gender role questions 2.4 2.7 2.3 2.8
N=5504 N=39251
Notes: Sample taken from General Social Survey from 1972-2012. The sample includes 57,000 adults but
is reduced to 44,755 after dropping missing observations.Data is reported separately by the binary variable
of ever having been divorced, regardless of current marital status.Religiously affiliated is a binary variable
that accounts for identifying with any religion at all, regardless of what that religion is. The composite of
gender role questions is an index of the responses to questions regarding genderroles, which can be found
in the description of data. The higher the gender index, the more progressive the respondent is regarding
traditional gender roles.
Ever Been Divorced
Standard
DeviationMean
Never Been Divorced
Mean
Standard
Deviation

15. 15
Table 2
OLS Estimates of Probability of Ever Marrying
Notes:Sample is described in notesofTable 1.Dependentvariable is a binary indicatorforevermarrying.
Column 1 reports OLSestimates frommodel described in Equation 1.Column 2 reports OLSestimates from
Model 1 Model 2 Model 3
Number of siblings 0.011 0.006 0.005
(0.0006)** (0.0005)** (0.0005)**
Age 0.053 0.052
(0.0005)** (0.0005)**
Age squared -0.000 -0.000
(0.00)** (0.00)**
Male -0.061 -0.055
(0.003)** (0.003)**
White 0.054 0.050
(0.007)** (0.007)**
Black -0.077 -0.083
(0.008)** (0.008)**
Completed college -0.041 -0.038
(0.004)** (0.004)**
Mother completed
college
-0.070
(0.005)**
-0.064
(0.005)**
Income 0.000 0.000
(0.00)** (0.00)**
North -0.015 -0.012
(0.004)** (0.004)**
East -0.054 -0.049
(0.004)** (0.004)**
West -0.038 -0.027
(0.004)** (0.004)**
Religious 0.080
(0.005)**
Attends religious services
less than monthly
-0.029
(0.003)**
Attends religious services
less than weekly
-0.005
(0.004)
Gender role attitude
composite
-0.007
(0.0005)**
_cons 0.745 -0.570 -0.595
(0.003)** (0.013)** (0.014)**
R2 0.01 0.34 0.35
N 44,755 44,755 44,755
estimates fromModel3, which additionally adds controls forgenderrole attitudes,religious affiliation and
attendance.Genderrole attitude variable explained in notes ofTable 1, as well as in description ofdata.All
regressionsare weightedusingthe composite weight variable provided by theGSS, which deals with
experimental randomization and blackoversamples.Non-robuststandarderrors are presented in parentheses.
*p<0.05;
** p<0.01.

16. 16
Model2, which adds controlsforbackground,economic,and geographic variables.Column 3 reports OLS
Table 3
OLS Estimates of Probability of Ever Divorcing
Model 1 Model 2 Model 3
Number of siblings 0.001 -0.001 -0.001
(0.0005)* (0.0005)* (0.0005)
Age 0.017 0.017
(0.0005)** (0.0005)**
Age squared -0.000 -0.000
(0.00)** (0.00)**
Male -0.003 -0.008
(0.003) (0.003)*
White 0.048 0.048
(0.007)** (0.007)**
Black 0.025 0.029
(0.008)** (0.008)**
Completed college -0.066 -0.064
(0.003)** (0.003)**
Mother completed college -0.008 -0.009
(0.005) (0.005)
Income 0.000 0.000
(0.00)** (0.00)**
North -0.036 -0.037
(0.004)** (0.004)**
East -0.073 -0.077
(0.004)** (0.004)**
West -0.010 -0.015
(0.004)* (0.004)**
Religious 0.012
(0.005)*
Attends religious services
less than monthly
0.055
(0.003)**
Attends religious services
less than weekly
0.029
(0.004)**
Gender role attitude
composite
0.002
(0.005)**
_cons 0.127 -0.305 -0.359
(0.002)** (0.013)** (0.014)**
R2 0.00 0.05 0.06
N 44,755 44,755 44,755
Notes:Sample is described in notesofTable 1.Dependentvariable is a binary indicatorforeverdivorcing.
Column 1 reports OLSestimates frommodel described in Equation 4.Column 2 reports OLSestimates from
Model5, which adds controlsforbackground,economic,and geographic variables.Column 3 reports OLS
estimates fromModel6, which additionally adds controls forgenderrole attitudes,religious affiliation and
attendance.Genderrole attitude variable explained in notes ofTable 1, as well as in description ofdata. All
randomization and blackoversamples. Non-robust standarderrors are presentedin parentheses.
*p<0.05; ** p<0.01.

17. 17
regressionsweighted usingthe compositeweight variable providedby the GSS,which deals with experimental
Table 4
OLS Estimates of Probability of Ever Divorcing Based on Proportion of Brothers
1 Sibling 2 Siblings 3 Siblings 4 Siblings 5 Siblings 6 or more
Siblings
Number
of
brothers
0.053
(0.02)**
0.019
(0.012)
0.009
(0.009)
0.003
(0.009)
0.012
(0.009)
-0.006
(0.004)
_cons -0.435 -0.350 -0.365 -0.389 -0.252 -0.301
(0.034)** (0.032)** (0.036)** (0.042)** (0.049)** (0.032)**
R2 0.09 0.06 0.07 0.07 0.05 0.04
N 7,909 8,701 7,231 5,178 3,600 9,791
Notes: Sample is described in notes of Table 1. Dependent variable is a binary indicator for ever divorcing.
All regressions control for background,economic, and geographic variables, as well as religious affiliation
and attendance,and gender role attitudes.Gender role attitude variable explained in notes of Table 1, as
well as in description of data.Non robust standard errors are presented in parentheses.*p<0.05; ** p<0.01.
Table 5
OLS Estimates of Male Probability of Ever Divorcing Based on Proportion of Brothers
Notes: Sample is described in notes of Table 1. Dependent variable is a binary indicator for ever divorcing.
All regressions control for background,economic, and geographic variables, as well as religious affiliation
and attendance,and gender role attitudes.Gender role attitude variable explained in notes of Table 1, as
well as in description of data.Non-robust standard errors are presented in parentheses.*p<0.05; ** p<0.01.
1 Sibling 2 Siblings 3 Siblings 4 Siblings 5 Siblings 6 or more
Siblings
Number of
brothers
0.058
(0.031)
0.018
(0.018)
0.033
(0.014)*
-0.008
(0.012)
0.030
(0.013)*
-0.002
(0.006)
_cons -0.479 -0.427 -0.409 -0.497 -0.370 -0.355
(0.049)** (0.045)** (0.054)** (0.062)** (0.070)** (0.048)**
R2 0.09 0.08 0.07 0.08 0.06 0.04
N 3,658 3,937 3,184 2,215 1,551 4,056

18. 18
Table 6
OLS Estimates of Female Probability of Ever Divorcing Based on Proportion of Brothers
Notes: Sample is described in notes of Table 1. Dependent variable is a binary indicator for ever divorcing.
All regressions control for background,economic, and geographic variables, as well as religious affiliation
and attendance,and gender role attitudes.Gender role attitude variable explained in notes of Table 1, as
well as in description of data.Non-robust standard errors are presented in parentheses.*p<0.05; ** p<0.01.
1 Sibling 2 Siblings 3 Siblings 4 Siblings 5 Siblings 6 or more
Siblings
Number of
brothers
0.052
(0.027)
0.021
(0.016)
-0.013
(0.012)
0.014
(0.013)
-0.004
(0.012)
-0.008
(0.005)
_cons -0.389 -0.285 -0.328 -0.314 -0.146 -0.271
(0.048)** (0.046)** (0.049)** (0.056)** (0.068)* (0.043)**
R2 0.08 0.05 0.07 0.07 0.05 0.05
N 4,251 4,764 4,047 2,963 2,049 5,735

19. 19
Works Cited
Angrist, Joshua, Victor Lavy, and Analia Schlosser. "Multiple Experiments for the
Causal Link between the Quantity and Quality of Children." Journal of Labor
Economics 28.4 (2010): 773-824. JSTOR. Web. 5 Oct. 2014.
Behrman, Jere R., and Paul Taubman. "Birth Order, Schooling, and Earnings."Journal
of Labor Economics 4.S3 (1986): 121-45. JSTOR. Web. 5 Oct. 2014.
Black, Sandra E., Paul J. Devereux, and Kjell G. Salvanes. "The More the Merrier? The
Effect of Family Size and Birth Order on Children's Education*." Quarterly
Journal of Economics 120.2 (2005): 669-700. Web. 5 Oct. 2014.
Blake, J. “The Only Child in America: Prejudice Versus Performance.” Population and
Development Review 7(1): 43-54. (1981).
Downey, D. B. “When Bigger Is Not Better: Family Size, Parental Resources,
and Children’s Educational Performance.” American Sociological Review
60(5) (1995): 746-761.
Downey, Douglas B., and Donna Bobbitt-Zeher. “Number of Siblings During
Childhood and the Likelihood of Divorce in Adulthood.” Manuscript accepted
at Journal of Family Issues. Forthcoming as of 6 Oct. 2014.
Downey, Douglas B., and Dennis J. Condron. "Playing Well with Others in
Kindergarten: The Benefit of Siblings at Home." Journal of Marriage and
Family66.2 333-50. (2004). JSTOR. Web. 5 Oct. 2014.
Leibowitz, Arleen. “Home investment in children.” Journal of Political
Economy 82:S111–S131. (1974).
White, L. "Sibling Relationships Over the Life Course: A Panel Analysis.
Journal of Marriage and Family 63(2): 555-568. (2001).