American Sociological Review
77(3) 463–485
© American Sociological
Association 2012
DOI: 10.1177/0003122412440802
http://asr.sagepub.com
Throughout adolescence, boys are overrepresented among high school dropouts, special
education students, and every failed or special
needs category. Boys’ notorious underperformance in school and their tendency to disrupt
the learning process in classrooms has sparked
intense academic and public debates about
the causes of what many now call the “problem with boys.” Some see the gender gap as
largely biological in origin. Others blame
schools for a de-masculinized learning environment and a tendency to evaluate boys
negatively for fitting into this environment
less well than girls. Yet, the true impact of
school context on the size of the gender gap
in academic performance remains controversial. The 1966 Coleman Report raised the
profile of research on school effects, and
much attention since then has been motivated
by a concern for equality of educational
opportunity by social class and race. Now that
440802 ASRXXX10.1177/0003122412440802Lege
wie and DiPreteAmerican Sociological Review
2012
a
Columbia University
Corresponding Author:
Joscha Legewie, Columbia University,
Department of Sociology–MC9649, 606 W. 122nd
Street, New York, NY 10027
E-mail: [email protected]
School Context and the
Gender Gap in Educational
Achievement
Joscha Legewiea
and Thomas A. DiPretea
Abstract
Today, boys generally underperform relative to girls in schools throughout the industrialized
world. Building on theories about gender identity and reports from prior ethnographic
classroom observations, we argue that school environment channels conceptions of
masculinity in peer culture, fostering or inhibiting boys’ development of anti-school attitudes
and behavior. Girls’ peer groups, by contrast, vary less strongly with the social environment in
the extent to which school engagement is stigmatized as un-feminine. As a consequence, boys
are more sensitive than girls to school resources that create a learning-oriented environment.
To evaluate this argument, we use a quasi-experimental research design and estimate the
gender difference in the causal effect of peer socioeconomic status (SES) as an important
school resource on test scores. Our design is based on the assumption that assignment to
5th-grade classrooms within Berlin’s schools is as good as random, and we evaluate this
selection process with an examination of Berlin’s school regulations, a simulation analysis,
and qualitative interviews with school principals. Estimates of the effect of SES composition
on male and female performance strongly support our central hypothesis, and other analyses
support our proposed mechanism as the likely explanation for gender differences in the
causal effect.
Keywords
causal inference, education, gender, gender gap, peer effects
464 American Sociological Review 77(3)
a growing gender gap in educational attainment has emerged, it is important to extend
this line of research and ask whether schools
affect gender inequality, and if so, what are
the mechanisms by which this occurs.
Integrating theories about gender identity,
adolescent culture, and findings from prior
ethnographic classroom observations, we
argue that the school environment channels
conceptions of masculinity in the peer culture, fostering or inhibiting boys’ development of anti-school attitudes and behavior. An
academically oriented environment suppresses the construction of masculinity as
oppositional and instead facilitates boys’
commitment by promoting academic competition as an aspect of masculine identity.
Lower quality schools, by contrast, implicitly
encourage—or at least do not inhibit—development of a peer culture that constructs resistance to schools and teachers as valued
masculine traits. Girls’ peer groups, by contrast, vary less strongly with the social environment in the extent to which school
engagement is stigmatized as un-feminine. As
a result, boys, in particular, benefit from
school resources that create a learning-oriented peer culture, and the size of the gender
gap in educational performance depends on
environmental factors connected to the quality of schools.
We evaluate our argument with a quasiexperimental research design using reading
test scores as an outcome variable and the
socioeconomic composition of the student
body as the focal treatment variable. This
design is based on within-school variation
across classes using the ELEMENT data from
one German city-state (i.e., Berlin). In contrast to the United States, the lack of performance-based tracking in Berlin’s elementary
schools, and parents’ smaller influence on
classroom assignment, makes it plausible that
student assignment to elementary school
classrooms in Berlin is almost random. To
develop a detailed understanding of the actual
selection process, we examine official school
regulations, provide statistical evidence from
simulation analyses, and conduct qualitative
interviews with school principals. Results
suggest that randomness indeed plays an
important role in the assignment process, but
they also point at potential sources of bias.
We address these potential biases statistically
with targeted sensitivity analyses using instrumental variable and sample restriction methods. We supplement the ELEMENT analysis
with estimates from a large-scale nationally
representative German dataset (PISA-I-Plus
2003) to address potential concerns about
generalizability of the findings.
Results of our investigation support our
core hypothesis. In addition, a systematic
comparison of our preferred explanation with
alternative accounts suggests that our hypothesized mechanism brings about the gender
difference in the causal effect of SES composition on student achievement. Our findings
speak to recent political debates about the
educational shortcomings of boys by deepening our understanding of their notorious
underperformance. Our analytic strategy also
makes a methodological contribution by illustrating how a detailed study of selection processes using simulations and qualitative
interviews can assist estimation of causal
effects.
Educational Outcomes
and Schools
The 1966 Coleman Report (Coleman 1966)
claimed that family is the most important
determinant of achievement, but performance
improves when classroom peers have greater
socioeconomic resources and are racially
integrated (see also Coleman 1961; Jencks
and Mayer 1990; Kahlenberg 2001). As
Coleman and others have subsequently
argued, students are motivated to invest more
heavily in their studies when adolescent culture rewards academic performance and
thereby supports parents’ and teachers’ reward
systems. But when adolescent culture values
other behaviors more highly (e.g., sports,
being popular with the opposite sex, or opposition to school authority), and especially
when adolescent culture denigrates academic
Legewie and DiPrete 465
achievement, it inhibits academic investment
and weakens academic achievement. Simply
put, students who are highly motivated and
capable (attributes more common at higher
SES levels) create a learning-oriented peer
culture (Jencks and Mayer 1990; Rumberger
and Palardy 2005; Sewell, Haller, and Portes
1969).
For about 20 years following the release of
the Coleman Report, the literature reported
that school effects were relatively small in
comparison to family effects, and therefore
“schools are not an effective agent for the
redistribution of societal resources” (Hallinan
1988:255; see also Hanushek 1989). This pessimistic view of schools began to change with
the rise of the accountability and standards
movement to improve schools (Schneider and
Keesler 2007). Reanalysis of earlier studies
suggested a more consistently positive relationship between school resources and student achievement (Greenwald, Hedges, and
Laine 1996) and found that teacher quality, in
particular, was a major input into student
learning (see also Murnane 1983).
The renewed focus on schools’ impact on
learning has not obscured attention to the
central conclusion of the Coleman Report that
“the social composition of the student body is
more highly related to achievement, independent of the student’s own social background, than is any school factor”(Coleman
1966:325). Far more than was historically
appreciated, estimation of peer effects is challenging (Angrist and Pischke 2008) because
of non-random selection and unmeasured
confounding variables (e.g., teacher quality)
that affect student outcomes. The most persuasive recent studies use natural experiments
to estimate the impact of changes in class
composition on outcomes (e.g., Imberman,
Kugler, and Sacerdote 2009). A second strategy is to exploit potentially random assignment of students to classes within schools.
This strategy is only persuasive when applied
in school districts that make it difficult for
parents to teacher shop (Ammermueller and
Pischke 2009). A third strategy examines
arguably random fluctuations in adjacent
cohorts (e.g., of gender or race composition)
for the same school and grade (Gould, Lavy,
and Pawerman 2009; Hoxby 2000), although
these studies have not looked at peer effects
related to socioeconomic characteristics. The
magnitude of estimated effects is not large
(about .15 standard deviations), but it is about
the same as some of the most believable
estimates of teacher effects, whether for
academic, social, or behavioral outcomes
(Jennings and DiPrete 2010; Rockoff 2004).
Meanwhile, recent studies whose primary
estimation strategies control for observable
potential confounders have found a similar
effect size on test scores (Crosnoe 2009;
Rumberger and Palardy 2005).
School Context and the
Gender Gap in Education
The original focus on school effects developed out of a concern for equality of educational opportunity by social class and race.
Now that a growing gender gap in educational attainment has emerged, it is natural to
ask whether schools also affect gender
inequality, and if so, what are the mechanisms
by which this occurs. Starting in the 1970s
and early 1980s (Spender 1982; Stanworth
1984), ethnographic studies documented
girls’ and boys’ gendered behavior at school
as well as the different ways that teachers
treat girls and boys. Although overt discrimination against girls in the classroom has
declined over the past three decades, recent
studies suggest that boys still verbally dominate the classroom (Jovanovic and King
1998; Sadker and Zittleman 2009).
Meanwhile, the once celebrated coeducation
of boys and girls as a pivotal step toward
gender equality is now challenged by the
increasing popularity of single-sex private
schools, the opening of girls-only public
schools, and the claimed educational shortcomings of coeducation for girls (Morse
1998; Salomone 2003).
Despite these important strands of research
and the general recognition that schools are
an important context for socialization of
466 American Sociological Review 77(3)
young adolescents, literature on the educational gender gap has widely ignored the
school as a potential source of variation in
this gap. To our knowledge, Dresel, Stöger,
and Ziegler (2006), Machin and McNally
(2005), and Schöps and colleagues (2004) are
the only studies that examine variation in the
size of the gender gap across a number of
schools. Using data from a specific region in
Germany (i.e., Baden-Württemberg), Dresel
and colleagues (2006) found substantial variation in the educational gender gap across
schools and classes, while Schöps and colleagues (2004) obtained a similar finding
using the German PISA data. Machin and
McNally (2005), by contrast, argue that specific school-based characteristics, such as
school inputs, teaching practices, and the
examination system, have no effect on the
gender gap. We extend this line of research by
building on reports from prior ethnographic
classroom observations and theories about
gender identity to understand the role of
school context for boys’ underachievement.
Boys’ Underachievement, Gender
Identity, and School Climate
In a classic study, Willis (1981) argued that
working for academic success is in conflict
with adolescent conceptions of masculinity.
He portrayed working-class white boys’ antischool attitudes and behavior as arising from
peer dynamics and a belief that their opportunity to use education to achieve success in the
labor market was blocked (see also Kao,
Tienda, and Schneider 1996; MacLeod 2008).
In line with Willis’s early findings, much of
the literature on boys’ underachievement
focuses on disincentives to engage with
school that stem from adolescent conceptions
of masculinity, which are developed and reinforced in peer groups. Gender differentiation
and creation of stereotypical gender identities
begin in early childhood before children have
had any experience with school (Davies 2003;
Maccoby 1998; Thorne 1993). Genderdifferentiated childhood cultures become the
basis for gender-differentiated adolescent
cultures, which are important influences on
how children view school, whether they take
school seriously, and how hard they work as
students (Steinberg et al. 1997).
Classroom observations and other ethnographic studies document how gender identities are constructed in the classroom and how
these gender cultures affect boys’ and girls’
interactions and approach to education (Francis 2000; Pickering 1997; Salisbury and Jackson 1996; Skelton 1997). Boys tend to be
noisier, more physically active, and more
easily distracted than are girls (Francis 2000;
Howe 1997; Spender 1982; Younger, Warrington, and Williams 1999). Studies also find
that masculine stereotypes portray boys as
competitive, active, aggressive, and dominating, while girls are viewed as conciliatory and
cooperative (Francis 2000). Other scholars
argue that stereotypical gender identities perpetuate the belief that girls have to work hard
to learn in school, whereas boys are naturally
gifted (Cohen 1998; Epstein 1998; Mac an
Ghaill 1994; Power et al. 1998; Quenzel and
Hurrelmann 2010). Cohen (1998) shows that
these gendered beliefs are reflected in boys’
casual and detached attitude toward school,
which accords with the ethnographic studies
referenced earlier. Despite the transformation
of gender relations in modern societies, stereotypical gender identities continue to shape
orientations toward school and produce
behaviors that reinforce these identities while
potentially affecting children’s academic success. Morris (2008:736) observed this process
at a rural high school: “girls tended to direct
considerable effort and attention to school”
whereas “boys . . . took pride in their lack of
academic effort” as an aspect of their masculine identity.1
Peers and the adolescent reward system
reinforce gender identities and gendered
behavior patterns. In some contexts, disruptive behavior produces status gains in lower
SES students’ peer groups. Working for academic achievement, by contrast, is labeled as
feminine and thereby stigmatized. Girls, however, typically view school work as acceptable and sometimes even encouraged. In a lack
Legewie and DiPrete 467
of parallelism with male peer groups, working-class and lower-class female peer groups
do not consider resistance to authority and
disengagement from school to be core aspects
of feminine identity (Maccoby 1998). As a
result, girls’ peer culture more readily encourages attachment to teachers and school.2
A diverse group of studies supports the
role of peers in shaping attitudes toward
school. Coleman (1961), Eitzen (1975),
Steinberg and colleagues (1997), and, more
recently, Bishop and colleagues (2003) argue
that adolescents value attributes that make
one cool or popular because these attributes
are linked with high status. Based on her own
and others’ ethnographic work, Epstein
(1998:106) argues that “the main demand on
boys from within their peer culture . . . is to
appear to do little or no work” whereas for
girls “it seems as if working hard at school is
not only accepted, but is, in fact, wholly
desirable.” Morris (2008:738; for other examples, see Epstein 1998) documents this attitude in a conversation between three boys in
an English class:
Kevin: “I don’t want to put in a lot of extra
effort like that. I’ll just do the basic stuff and
get a B.” “I got an 87 in here,” he says
proudly. Warren chimes in, “Yeah, I hate
these pussies who make like an A minus and
then they whine about it.” Kevin says, “Yeah
it’s like why do you care? Why does it have
to be better? Nothin’ wrong with a normal
grade!”
Although ethnographic studies document
substantial within-gender diversity in the construction of gender identities, evidence on
typical gender differences is rather persuasive. Young boys tend to construct masculinity at least partly in terms of resistance to
school. This conception of masculinity may
be partially responsible for male underachievement (Francis 2000; Pickering 1997;
Salisbury and Jackson 1996; Skelton 1997).
The conception of female identity and female
peer culture, by contrast, is not as closely tied
to resistance to school, and indeed may even
support schoolwork as a positive attribute of
femininity. As a result, girls consistently have
better work habits and a stronger pro-school
orientation.
While Willis and others mainly focus on
consequences of lower- and working-class
backgrounds for anti-school attitudes among
boys, we are interested in the school and class
environment as a context that either encourages or limits development of anti-school
attitudes and behavior. High-status parents
generally foster an orientation for their boys
that is at least instrumentally focused on high
performance in school. These parents also
have resources to intervene in their children’s
lives to counter signs of educational detachment or poor performance. As Coleman and
others argue, schools can play a similar role
in enhancing students’ incentives to be
engaged with academics by creating a learning-oriented peer culture. In this line, many
scholars argue that the success of some charter schools, such as KIPP and the Harlem
Children’s Zone, comes from their ability to
foster a learning-oriented environment (Ravitch 2010).
We argue that boys gain more than girls
from a learning-oriented environment because
it channels how masculinity is constructed in
the school culture. Such an environment promotes academic competition as an aspect of
masculinity and encourages development of
adaptive strategies that enable boys to maintain
a show of emotional coolness toward school
while being instrumentally engaged in the
schooling process. In other words, academic
competition as one of the “different ways of
‘doing’ masculinity” (Francis 2000:60; see
also Mac an Ghaill 1994) becomes a more
important part of the construction of masculine
identity in certain environments.
As is true in the family, production of an
academically oriented environment in school
is not effortless. It requires resources. Better
facilities, better curriculum, better teachers,
and better support staff all can produce more
value-added in school. Both boys and girls
will generally benefit from better schooling,
of course, but we expect school inputs that
468 American Sociological Review 77(3)
strengthen a learning orientation in the student culture have the potential to enhance
educational outcomes especially strongly for
boys. Teachers, for example, can promote a
learning-oriented student culture. Accordingly, we expect teachers with the right collection of skills might have especially positive
effects on boys’ achievement.
Here, the school resource of central interest is the socioeconomic composition of the
student body. Peers’ impact on school climate
and student achievement has played a crucial
role in the literature on schools ever since
Coleman (1966:325) claimed that “the social
composition of the student body is more
highly related to achievement, independent of
the student’s own social background, than is
any school factor.” The mechanism behind
this association is cultural; students with high
motivation and achievement from high-SES
backgrounds create a learning-oriented peer
culture and assist teachers in the process of
education (Jencks and Mayer 1990; Rumberger and Palardy 2005; Sewell et al. 1969).
We expect the disadvantages of low-SES
composition to be larger for boys than for
girls because of the evidence that lower SES
student bodies create a stronger oppositional
culture in male than in female peer groups.
Conversely, an academically oriented environment in schools channels conceptions of
adolescent and pre-adolescent masculinity,
suppresses boys’ negative attitudes toward
school, and facilitates academic competition
as an aspect of masculine identity. Girls’ peer
groups, on the other hand, more readily and
independently of school context encourage
attachment to teachers and school, and do not
identify femininity with disengagement from
school. Based on this argument, we hypothesize that the female advantage in academic
achievement is bigger in schools with a lower
SES composition in their student body.3
Data and Methods
We address our core hypothesis with the
German ELEMENT dataset, using reading
test scores as an outcome variable and SES
composition of classroom peers as our focal
treatment variable. The ELEMENT dataset is
a longitudinal study that assessed development of reading and math ability in the 4th,
5th, and 6th grades in Berlin schools (Lehmann
and Lenkeit 2008). It includes about 3,300
students who attended 4th grade during the
2002 to 2003 school year in 71 randomly
selected elementary schools in Berlin, and all
1,700 students who attended 5th grade in 2003
to 2004 in one of Berlin’s 31 upper-secondary
schools that begin with 5th grade.4
In our final
models, we combine these two ELEMENT
samples and control for school type through
school-level fixed effects. We also examine
whether relevant effects vary by school type
using interaction terms (they do not). Part A in
the Appendix provides a short introduction to
the German educational system.
The ELEMENT dataset includes at least
two classrooms for every school.5
This feature provides the basis for a quasi-experimental design. It allows us to estimate contextual
effects of 5th-grade class composition by
gender using school-level fixed-effects models, because the original assignment to elementary school classes in 1st grade within
schools is not subject to self-selection or
parental control.6
This estimation strategy
provides a clear advantage over similar estimates based on data from U.S. schools, where
performance-based tracking in elementary
schools and parents’ influence on assignment
to classes are more pronounced.
Our quasi-experimental research design
provides high internal validity and allows us
to make a strong case for causal inference, but
the analysis is geographically limited to a
single German state. To address this limitation, we supplement the ELEMENT data with
the German PISA-I-Plus 2003 data, a German
extension of the international PISA study.7
The PISA-I-Plus includes a nationally representative sample of 9,000 students in at least
two 9th-grade classrooms in 220 schools
(PISA-Konsortium Deutschland 2006).8
The
two datasets complement each other and provide strong internal and external validity for
the estimation of causal effects.
Legewie and DiPrete 469
School-Level Fixed Effects as a
Quasi-Experimental Identification
Strategy
Regression or matching estimates of school
effects based on conditioning on observable
variables as an identification strategy potentially suffer from endogeneity problems.
These strategies rely on the assumption that
students are randomly assigned to schools
conditional on observable covariates in the
model (Sørensen and Morgan 2006). This
common identification strategy is especially
problematic for estimation of school effects
with cross-sectional data. Students clearly are
not randomly assigned to schools, and it is
unlikely that this non-random assignment can
be perfectly modeled with the observed
covariates.
To avoid these potential endogeneity problems, we estimate school-level fixed-effects
models using ELEMENT and PISA-I-Plus
data. Both datasets contain an additional level
of analysis, the classroom. We argue that most
students are randomly assigned to classrooms
conditional on their school in Berlin’s elementary and 5th-grade upper-secondary schools
(for a similar strategy, see Ammermueller
and Pischke 2009). Assuming students’ random assignment to classes within schools, we
can estimate the causal effect using school
fixed-effect models and a measure of SES
composition on the classroom level (we discuss the variables in detail below). We specify
these models as
y female SES Comp
SES Comp female
y
ijk j i k
k i
i
= + +
+ ×
+
α θ
δ
β
γ( ) ( )
(( ) )
1
4th grade
i k ijk
+ + X U β β +
2 3
ε
(1)
where i, j, and k are indices for individuals,
schools, and classes, respectively; αj
are
school fixed-effects; y
i
4th grade is a student’s
prior achievement measured in 4th grade; and
Xi
and Uk
are sets of control variables on the
individual and class level, respectively.9
Due
to data limitations, analysis with the PISA-IPlus dataset omits the variable of prior
achievement on the right-hand side.10
These models examine whether class-toclass variation in performance is systematically related to class-to-class variation in
socioeconomic composition, controlling for
all unobserved school characteristics (and
therefore the non-random selection of students into schools). Coefficients of interest
are θ, which captures the causal effect of the
socioeconomic class composition, and δ,
which captures the difference in this effect
between boys and girls. We expect to find a
positive effect of SES composition, as previously documented, and, more important for
our theory, a negative estimate of the interaction term, indicating that boys are more sensitive than girls to peer SES. Pre-treatment
control variables on student and class levels
are of secondary interest; we include them to
increase balance between the treatment and
control groups (for a description of the control variables, see Table 1).
Assignment of Students to
Classrooms within Schools
Our estimation strategy relies on the assumption that selection of students into different
classes within schools is practically random.
While students self-select into schools, their
allocation to different classes within schools
is arguably less selective, but it might not be
completely random. In particular, the allocation process, and therefore selection into
treatment, might involve three potential
biases: (1) parents might influence which
class their children attend; (2) schools might
allocate students based on certain characteristics (e.g., performance-based tracking or subject choice); and (3) children might self-select
over time when certain children have to
repeat a class or change schools. Even if students are randomly assigned to classes, certain teachers might be assigned to specific
classes based on a classroom’s composition,
which could create a bias in relevant estimates of classroom composition.
470 American Sociological Review 77(3)
To develop a deeper understanding of the
actual selection process, we conducted a
three-part analysis. First, we studied official
school regulations in Berlin. Second, we used
a simulation-based approach to compare
observed composition of classes with simulations involving random assignment of students to classrooms within schools. Third, we
conducted qualitative interviews with school
principals in Berlin. The resulting detailed
picture of the actual selection process allows
us to evaluate our argument that self-selection
is practically random and to design targeted
statistical sensitivity analyses that address
potential sources of biases.
School regulations and general considerations. Primary school regulations in
Berlin (Grundschulverordnung Berlin, §8)
prohibit allocation of students based on gender,
Table 1. Variables in Main Analysis at Individual and Class Level
Variables Description Mean SD
Mean
(Male)
Mean
(Female)
Diff. in
Means
Dependent Variable
Achievement 5th-grade reading test
scores
109.92 13.65 108.79 111.05 −.17**
Independent Variables (Individual Level)
Prior achievement 4th-grade reading test
scores
103.22 15.96 101.39 105.07 −.23**
Female 0 = Male; 1 = Female .50 .50
Family background ISEI scale 51.45 16.63 51.09 51.80 −.04
Migration background Categorical Variable:
1 = both parents born
in Germany
.64 .48 .64 .65 −.01
2 = one parent not
born in Germany
.12 .33 .12 .13 −.01
3 = both parents not
born in Germany
.17 .38 .18 .16 .02*
4 = child not born in
Germany
.06 .24 .06 .06 −.01
Class repeater 0 = not repeated; 1
= repeated at least
once
.06 .23 .07 .04 .03**
Interaction terms Female x Family
background
Female x Migration
background
Female x SES
composition
Independent Variables (Class Level)
Size of class Number of students
in the class
23.33 3.41 23.33 23.34 −.00
SES composition Average ISEI at class
level (aggregated)
51.45 9.40 51.08 51.10 −.01
Source: ELEMENT data.
Note: N = 4,372. The difference in means refers to the mean for boys minus the mean for girls divided
by the pooled standard deviation. The difference in means is not standardized for the binary variables
(i.e., migration background dummies and indicator for class repeater). All continuous variables are
standardized for the final analysis.
* p < .05; ** p < .01; *** p < .001 (two-tailed t- and z-tests).
Legewie and DiPrete 471
first language, or performance and emphasize
heterogeneity of classes in regard to these
characteristics. These legal constraints rule out
performance-based tracking, set limits on
parental influence over classroom assignment,
and provide guidelines for classroom assignment of grade repeaters and newcomers. As a
consequence, allocation of students to classrooms based on family background is unlikely.
Regulations also mention, however, that
schools can consider existing friendships
between new students and assign them to the
same classroom. This practice, if common,
might create a bias in the assignment process
that could pose a problem for estimation of the
causal effect.
In secondary schools such as those in the
PISA-I-Plus data, class-specific tracking
based on subject choice (e.g., foreign language) is more common, and a higher number
of students have to repeat a class compared
with elementary school. This creates potentially non-random allocation of students to
classrooms, so the selection problem might be
more pronounced in secondary schools. In
Berlin, however, 5th-grade upper-secondary
schools (grundständige Gymnasien) are different from other secondary schools. Their
student population is more homogeneous
compared to other secondary schools, which
makes a purposeful allocation to different
classes relatively inconsequential. In addition, assignment to 5th grade is not subject to
selection over time through grade retention
because students enter these schools for the
first time at 5th grade.
Based on these considerations, we expect
that assignment to 5th-grade classrooms is
practically random in Berlin’s elementary and
upper-secondary schools, whereas assignment to 9th-grade classrooms in secondary
schools is subject to more pronounced selection processes.
Simulation of random assignment. We
use a simulation-based approach to evaluate
whether within-school variation in socioeconomic composition across classrooms created
by the actual (unknown) allocation process is
consistent with random assignment. Figure 1
compares socioeconomic composition across
classrooms obtained from simulations that
randomly assign students to classrooms (histogram) with the observed composition (vertical
line), that is, the average variation of class
means within schools (see Part B in the Appendix for details on the simulation).
For the two ELEMENT samples, the
observed mean is consistent with a random
assignment process. This is in line with our
expectation about assignment to classrooms
in 5th grade. As expected, however, the
Figure 1. Comparison of Observed Classroom Composition with Simulations Involving
Random Assignment
Note: Graphs show average variation of class means within schools for the observed samples (vertical
line) together with the sampling distribution of this statistic obtained from 1,000 random simulations
(histogram). Vertical grey lines indicate 95% confidence intervals from the simulations.
472 American Sociological Review 77(3)
observed value for secondary schools in the
PISA-I-Plus is relatively unlikely to occur
under random assignment. Similar simulations for the proportion of students with
migration backgrounds suggest that assignment in regard to this characteristic is consistent with randomness for all three datasets.
Finally, variation in gender composition
across classrooms within schools is smaller in
the actual data than in the simulated distribution (see the online supplement [http://asr.
sagepub.com/supplemental]). This result suggests that schools distribute boys and girls
equally across classrooms.
These results provide statistical evidence
to support the previously described institutional evidence that assignment to classrooms
within schools with respect to family background is as good as random in the ELEMENT dataset. By contrast, some non-random
selection process seems to play a role for
9th-grade classrooms in secondary schools.
Interviews with school principals.
Although simulations are informative, they do
not provide information about the actual
assignment process. It is still conceivable that
non-random selection processes are at work
that produce a distribution of students in terms
of socioeconomic status that is consistent
with a random assignment process. To develop
a deeper understanding about the actual
assignment process, we conducted 12 interviews with school principals in Berlin as the
central actors in the allocation process (nine
for elementary schools and three for uppersecondary schools). We selected schools
using a random sample that we then supplemented with specific schools to ensure
diversity in regard to neighborhood and ethnic
composition. Interviews lasted about 15 to 20
minutes and focused on the actual procedure
schools use to assign students to classes, criteria that play a role in assignment, the extent to
which parents try to influence this process,
and how schools deal with parental requests.
Interviews also solicited information about
how schools assign students who repeat a
class or who transfer from other schools and
about how teachers are assigned to classrooms. The online supplement contains a
detailed description of the sampling procedure and a translation of the interview
questions.
While the schools under study use different procedures to assign students to classes, a
number of findings emerged from the interviews. First, none of the principals reported
that they directly take family background or
performance into account in the assignment
process, and most schools do not respond to
parents who try to influence the process (for
an exception, see below). Second, schools try
to have classes of similar size. This plays an
important role in assignment of students who
either repeat a grade or transfer from another
school. Third, assignment of teachers to classrooms is generally not connected to socioeconomic composition or other characteristics of
the class. Teacher assignment is based on
scheduling issues and past experience with
the teacher.11
A number of potential biases, however, do
exist. First, while all school principals emphasized that a desire to equalize classroom size
is the main criterion, principals also reported
that students who repeat a grade are sometimes assigned to specific classes based on
expectations about social dynamics. Second,
some principals reported that they take into
account whether groups of children attended
the same kindergarten and try to assign these
students to the same 1st-grade classroom.
Other principals said they follow parent
requests when they are related to friendships
between two new students, which often developed because the children attended the same
kindergarten. Third, while most principals
reported distributing children with immigration backgrounds equally across classes, two
principals said they create a separate class for
children who are learning German. While the
simulations suggest the contrary, this finding
makes it unclear how common the practice of
sorting students by migration background or
language skills is. We take special care to
Legewie and DiPrete 473
address this potential issue statistically.
Fourth, all principals reported that they try to
ensure gender balance between the classrooms. This practice is consistent with results
from the simulation insofar as variation in the
proportion of female students across classes
within schools is smaller than what we would
expect from random assignment.
Except for the last criterion, which is irrelevant because boys and girls are equally distributed across families, these selection
criteria might induce some systematic bias in
the composition of classrooms. The importance of these selection criteria, however,
seems to be limited. Most school principals
independently and without knowledge of our
study concluded that randomness plays an
important role in the assignment process
because they simply have little prior knowledge about entering students and because the
whole process is not very systematic. One
elementary school assistant principal and
teacher, for example, emphasized that even
decades of experience at elementary schools
could not remove the inherent unpredictability about classroom dynamics, given the limited prior knowledge about entering students
that schools have to work with:
We have realized again and again that even if
we try to make sense of the classroom composition based on names or other attributes
we know about, there is no way to know how
the class actually turns out in regard to its
social composition. Even though I have been
working at schools for 40 years now, there
are always unexpectedly difficult or balanced classes, which really depends on the
personalities of the students inside the classroom so that in the end randomness plays a
big role. (translation by authors)
We elicited these and similar concluding
remarks from interviewees at the end of interviews by asking how they would weigh the
importance of different criteria and whether
they thought randomness also plays a role.
These observations are particularly interesting considering that we expected a social
desirability bias in favor of principals reporting a sophisticated assignment procedure.
Conclusions about selection process.
Based on evidence from school regulations,
the simulations, and interviews with school
principals, we conclude that the role of potential selection biases is limited. Results justify
our quasi-experimental design and support
our argument that using within-school variation across classrooms in Berlin elementary
schools greatly improves our estimates compared to estimates based only on betweenschool variation. We also recognize the potential selection biases documented by the
interviews, and we address these problems
statistically by conducting a set of targeted
sensitivity analyses. These robustness checks
are based on instrumental variable analyses
and sample restrictions specifically designed
to address each potential source of bias.
Finally, we note that in contrast to most
research on compositional school effects, we
are not fundamentally interested in school
performance as an outcome. Rather, we
address contextual determinants of the gender
gap in school performance. While evidence
from the interviews indicates that students
might select into certain classrooms, it seems
unlikely there is differential selection of boys
and girls into different classrooms. Non-random assignment to classrooms only matters
for our key estimation results to the extent
that schools treat boys and girls differently
during the assignment process. Interviews did
not provide any indication of differential
treatment of boys and girls, even though
school principals were asked directly about
such a possibility. This fact enhances our confidence in the validity of our estimates.
Variables and Treatment of
Missing Data
Our analysis uses reading test scores in 5th
grade (ELEMENT) and 9th grade (PISA-IPlus) as the main outcome variable (see Table 1
for descriptive statistics). Campbell and colleagues (2001:1) describe reading scores as
474 American Sociological Review 77(3)
“one of the most important abilities students
acquire through their early school years. It is
the foundation for learning across all subjects.” Reading literacy also figures importantly in research on the gender gap in
education, because boys’ reading test scores
lag notably behind that of females (Buchmann,
DiPrete, and McDaniel 2008). Some researchers even argue that boys’ failure in general is
due to their deficits in reading (Whitmire
2010). We measure test scores on a common
scale using item response theory and standardize them with a mean of zero and a standard deviation of one.
Our focal treatment variable is the student
body’s socioeconomic (SES) composition,
which we measure at the classroom level as
the average social status on the ISEI scale
(Ganzeboom, Treiman, and Ultee 1991).12
One could argue that peers’ prior achievement
is a more natural contextual measure for testing our core hypothesis. However, peer
achievement is endogenous in our data
because it is measured after random assignment. Moreover, the correlation between peer
achievement and SES is too high to reliably
distinguish effects of the two variables.
Accordingly, SES composition provides a
stronger test (i.e., one resting on weaker
assumptions) of our theory than could be
obtained using peer achievement. In addition,
a long tradition in sociology, going back to
the Coleman Report, sees SES composition as
connected to a peer group’s learning orientation because attributes such as high motivation and capability are more common among
students from high-SES families. Consequently, a student body’s SES composition is
a school resource that fosters a learning orientation, and it is highly relevant for our study.
Along with SES composition, we use a
comprehensive set of control variables, including 4th-grade test scores, as a measure of prior
performance. Table 1 describes these variables
together with descriptive statistics. All independent continuous variables are standardized
to have a mean of zero and a standard deviation of one across the combined sample of
males and females in both datasets.
The Forschungsdatenzentrum at the IQB
provides five imputed versions of the ELEMENT dataset (see Lehmann and Lenkeit
2008). We performed each analysis separately
for the five imputed datasets and then combined the different estimates to obtain the
final results presented here. We employed a
similar imputation strategy based on the
chained equations approach for the PISA-IPlus dataset.
Results
Variation of the Gender Gap across
Schools
In an average school, the female advantage in
reading scores is about .12 standard deviations
in 5th grade and .21 standard deviations in 9th
grade. It ranges from –.04 to .28 standard
deviations in 5th grade and from .07 to .35
standard deviations in 9th grade for 95 percent
of schools. Expressed in terms of years of
education, girls are .36 school years ahead in
5th-grade reading test scores in an average
school, but the gap ranges across schools from
a male advantage of .12 years to a female
advantage of .83 years.13 Figure 2 plots this
variation in the gender gap on the school level
against average performance at a school. The
striking pattern in the figure indicates that
schools with higher than average performance
also have the smallest gender gap. This finding is consistent with our theoretical prediction; it suggests that boys do not fall as far
behind in performance-oriented schools.
SES Composition and the Gender
Gap in Education
Table 2 presents estimates from school-level
fixed-effect regression of reading test scores
in 5th grade on classroom-level SES composition, 4th-grade scores, and other control
variables. The table shows the main effects of
gender and SES composition on the classroom level together with the interaction
between SES composition and gender (all
coefficients are in standard deviation units).
Legewie and DiPrete 475
Other coefficients are omitted from the table
(for the full regression results, see Table S1 in
the online supplement). The table also shows
fixed-effect (FE) estimates from the PISA-IPlus data for 9th-grade reading test scores
without a measure of prior performance and
estimates from a multilevel (MLM) model on
the school level with a broad set of control
variables. We include MLM estimates as a
comparison because they reflect one of the
most common estimation strategies (i.e., conditioning on observable covariates) used in
sociology to identify compositional peer
effects (e.g., Rumberger and Palardy 2005).
Results in Table 2 show that SES composition has a positive and highly significant
effect on reading test scores in all models for
gain scores (top row) and raw scores. This
Figure 2. Gender Gap and Average Performance across Schools in Standard Deviation
Note: Estimates shown in the figure are based on a multilevel model with two levels (student and
schools) and with a random intercept and a random slope for female on the school level so that average
performance and effect of gender are allowed to vary across schools. Dots represent empirical Bayes
predictions for the random intercept (i.e., average school performance) against the prediction for the
random slope (i.e., the female advantage).
Table 2. Effect of SES Composition for Boys and Girls in Standard Deviations
Female SES Comp.
SES Comp.
x Female
Model
Prior
Performance Coef. (se) Coef. (se) Coef. (se)
1. FE Estimate
(ELEMENT)
yes .007 (.02) .091* (.04) −.060** (.02)
2. FE Estimate
(ELEMENT)
no .120*** (.03) .178*** (.06) −.057* (.03)
3. FE Estimate
(PISA-I-Plus 2003)
no .196*** (.03) .237*** (.03) −.052* (.02)
4. MLM Estimate
(PISA-I-Plus 2003)
no .143 (.11) .303*** (.05) −.099* (.04)
Note: FE = fixed effect. Table 1 describes control variables. The full set of coefficient estimates for
Models 1 and 2 are in Table S1 in the online supplement. The number of students for models based on
ELEMENT is 4,372, the number of schools is 101, and the average number of students per school is 43.3.
N for PISA-I-Plus is 8,559.
* p < .05; ** p < .01; *** p < .001 (two-tailed tests); standard errors adjusted for clustering on class level.
476 American Sociological Review 77(3)
result conforms with previous findings in the
literature on effects of SES composition
(Jencks and Mayer 1990; Rumberger and
Palardy 2005). In all models, the point estimate for the interaction between SES composition and female is negative and significant.
Most important, estimates from the fixedeffect model using the ELEMENT data along
with a control variable for prior performance
show that boys learn more in classes with
higher average SES. Adding additional peer
characteristics, such as the proportion of foreign-born students, to this specification does
not affect this finding (results not shown
here). Results from the two FE models based
on ELEMENT and PISA-I-Plus data without
4th-grade performance show the same results
(we include ELEMENT results for direct
comparison). In particular, the main effect of
SES composition in the model based on
PISA-I-Plus data seems to be upwardly biased
(.237 compared to .178), and both estimates
are somewhat larger than the .15 effect size
estimated by Crosnoe (2009). However, the
estimated size of the interaction between
female and SES composition is very similar
across the three fixed-effect models. This
finding supports our argument that even if
students self-select into classes (and selfselection appears to be more important in 9th
grade), boys and girls are unlikely to differ in
this selection process, which increases our
confidence in the ELEMENT estimates.
Results from the MLM model point in the
same direction but appear to be upwardly
biased. In particular, the estimate for the
interaction is about 90 percent higher in the
MLM model compared to the corresponding
FE model. This could reflect the fact that the
MLM estimate is based on non-random
school-level variation, while the fixed-effect
estimate is based on almost-random classroom-level variation within schools. The
school-based estimate’s larger size might also
reflect spillover effects between SES composition of two classrooms in the same school.
Given the possibility of selection bias in the
MLM estimates, we consider the fixed-effects
classroom-based estimates to be a more
definitive test of our theoretical prediction.
Overall, our estimates provide strong evidence that boys are more sensitive than girls
to the important school resource of classroom
SES composition. Our statistical evidence is
strengthened by the fact that institutional,
simulation-based, and qualitative evidence
indicates that randomness plays a central role
in the allocation of students to 5th-grade
classrooms in Berlin.
Targeted Sensitivity Analysis
In this section, we investigate whether our
results are sensitive to three potential selection
biases documented in interviews with school
principals. Our detailed knowledge about the
assignment process allows us to design a set of
sensitivity analyses based on instrumental
variables (IV) and certain sample restrictions
targeted to address these potential biases. The
FE model specified in Equation 1 and shown
in the top row of Table 2 serves as the starting
point. Table 3 presents results from the different sensitivity analysis and repeats estimates
from the school FE model based on ELEMENT
data for direct comparison.
The first selection process documented in
the interviews refers to the non-random
assignment of students who have repeated a
grade to specific classrooms. While all principals reported that classroom size plays an
important role, some principals also mentioned that they take potential implications
for classroom culture into account. To address
this potential selection problem, we treat SES
composition on the class level as endogenous
and instrument it with the average SES of the
subset of students who never repeated a grade.
This instrument is highly correlated with the
total composition (the treatment indicator),
and is arguably not affected by potentially
non-random selection of grade repeaters
because it is based only on students who
never repeated a grade. The instrument should
only be connected with the outcome through
the actual class composition (i.e., it satisfies
the exclusion restriction). Model 1 in Table 3
presents results and shows that the interaction
between SES composition and female remains
negative and significant. This indicates that
Legewie and DiPrete 477
selection of students who repeat a grade into
specific classes does not significantly bias the
estimated effects.
The second potential selection process is
assignment of students to the same class
who attended the same kindergarten or who
were friends before entering school. Using a
similar strategy as in the last sensitivity analysis, we instrument peer SES by SES composition calculated for the subset of students
who either did not attend kindergarten or
who skipped a grade or transferred from
another school. This set of students was
certainly not assigned to classrooms based
on the kindergarten criterion, and students
who skip a class or transfer from a different
school are most likely assigned to classrooms based on the number of students in
different classrooms. For these reasons, the
instrument is unaffected by the kindergarten
criteria and (for the most part) by friendship
self-selection. Results, presented in Model 2
of Table 3, again support our previous finding and indicate that the estimated causal
effect is not sensitive to selection of connected students (either through the same
kindergarten or through friendship) into the
same class.
Finally, some principals reported that, in
violation of school regulations, they assign
students with migration backgrounds to the
same class. To address this potential selection
bias, we estimated the fixed-effect model
reported earlier on a restricted sample. For
this purpose, we assessed which schools allocate students with migration backgrounds
non-randomly to classes, and we exclude
these schools from the analysis.14 Results,
presented in Model 3 of Table 3, show that
self-selection of students with migration
backgrounds into specific classrooms in some
schools does not affect our results.
Overall, results from the targeted sensitivity analyses specifically designed to address
the potential selection processes identified in
the interviews provide strong evidence that
our estimates of gender-specific effects of
classroom composition are not biased by
these selection processes.
Explaining the Observed Difference
in the Causal Effect between Boys
and Girls
The theoretical argument presented earlier suggests that school context plays an important
Table 3. Sensitivity Analysis
Female SES Comp.
SES Comp.
x Female
Model Coef. (se) Coef. (se) Coef. (se)
FE Estimate (full sample) .007 (.02) .091* (.04) −.060** (.02)
(1) FE/IV Estimate
Instrument: SES comp. of students who
never repeated a class
.008 (.02) .108* (.05) −.066** (.02)
(2) FE/IV Estimate
Instrument: SES comp. of students who
did not go to kindergarten, skipped a
class, or transferred to school
.009 (.02) .113 (.06) −.068* (.03)
(3) FE Estimate (restricted sample)
Sample restriction: only schools that
do not allocate based on ethnicity (24
schools excluded)
.008 (.03) .117* (.05) −.052* (.03)
Note: N = 4,372. First stage results show that the two instruments are highly correlated with SES
composition (i.e., the treatment). F-statistics are over 700 (highly significant), which is far above the
commonly used threshold of 10. Table 1 describes control variables.
* p < .05; ** p < .01; *** p < .001 (two-tailed tests); standard errors adjusted for clustering on class level.
478 American Sociological Review 77(3)
role for the size of the gender gap. An academically oriented environment in schools
with high-SES peers shapes how masculinity
is constructed; this suppresses boys’ negative
attitudes toward school, facilitates their commitment, and enhances students’ incentives to
be engaged with academics. Other mechanisms, however, may account at least in part
for the observed difference in the causal effect
of SES composition for male and female
students.
The literature on compositional school and
classroom effects offers an alternative explanation for the relationship between SES composition and student performance, which focuses
on social comparison processes (Jencks and
Mayer 1990; Rumberger and Palardy 2005;
Thrupp, Lauder, and Robinson 2002). This
alternative account argues that students use
their classmates as a reference group to evaluate their own performance and thereby develop
academic self-perceptions that in turn may
affect their performance (Crosnoe 2009; Dai
and Rinn 2008). To adjudicate between our
proposed explanation and this alternative
account, we estimate models based on ELEMENT data that are identical to the schoollevel fixed-effects regression described in
Equation 1, but that replace the reading score
dependent variable with measures of student
attitudes, student behavior, and self-perception
about academic ability.15 Our core hypothesis
implies that class environment has a more pronounced effect on attitudes toward school,
learning orientation, and academic effort for
boys than for girls. Accordingly, a higher positive effect of SES composition on these outcomes for boys than for girls would provide
further evidence for this mechanism. An explanation for gender differences based on reference group processes, however, would imply
that a class’s SES composition affects boys’
and girls’ academic self-perceptions differently. In other words, this alternative account
suggests that boys and girls react differently to
their reference group.
Table 4 shows results from school-level
fixed-effect models of the indicated variables
on classroom socioeconomic composition,
controlling for variables described in Table 1.
Panel A, which reports regression results
using attitudes toward school, learning orientation, and work habits as dependent variables, provides further evidence for our core
hypothesis. Point estimates for SES composition and the interaction with female are not all
significant but consistently point in the
expected direction. This pattern of results
suggests that boys’ attitudes toward school,
their learning orientation, and their work
Table 4. Effects of Gender and SES Composition on School-Related Attitudes and Behavior
Female SES Comp.
SES Comp. x
Female
Coef. (se) Coef. (se) Coef. (se)
Panel A Attitudes toward
school
.300*** (.04) .057 (.07) −.075* (.04)
Learning
orientation
.131*** (.04) .044 (.07) −.033 (.03)
Work habits .166*** (.04) .147* (.07) −.086* (.04)
Panel B Self-evaluation
reading
.140*** (.04) −.098 (.06) −.028 (.03)
Self-evaluation
German
.207*** (.04) .012 (.08) −.056 (.03)
Self-evaluation
general
−.294*** (.04) −.020 (.07) −.025 (.03)
Note: N = 4,372. Table 1 describes control variables.
*p < .05; **p < .01; ***p < .001 (two-tailed tests); standard errors adjusted for clustering on class level.
Legewie and DiPrete 479
habits are more sensitive to the school environment than are girls’ attitudes and work
habits. Panel B, by contrast, reports small and
insignificant interaction effects between gender and social classroom composition on selfevaluations of performance in reading, performance in German, and performance “in
general.” The lack of gender differences in
the effect of SES composition on self-perceptions of ability favors our preferred explanation over the alternative account based on
reference group processes.
We further extend this examination of
mechanisms by building on the initial FE
model for 5th-grade performance (defined in
Equation 1), and add school-related attitudes
and behavior as independent variables in a
stepwise fashion. Compared to the models
presented so far, the elaborated model is less
rigorous from a causal point of view because
the causal ordering of performance and
school-related attitudes and behavior is not
clear-cut. Nonetheless, it can be informative
about potential mechanisms. Results in Table 5
suggest that the effect of SES composition is
clearly reduced by the addition of variables
for school-related attitudes and behavior
(Model 2). They also suggest that part of the
gender difference in the effect of SES composition (33 percent) may be explained by its
gender-specific effect on school-related attitudes and behavior; this provides further support for our proposed mechanism.
Finally, we investigate the possibility that
boys benefit from a stronger academic peer
culture not because they are boys, but rather
because underperforming students benefit in
general, and boys are a disproportionate fraction of underperforming students. Accordingly, we again extend the model described in
Equation 1 by adding an interaction term
between performance in 4th grade (the year
prior to our measured outcomes in the regressions) and SES composition in 5th grade.
Results (available from the authors) show that
the impact of SES composition is significantly stronger for low-performing students,
which is in line with findings from other studies (Bryk, Lee, and Holland 1993; Coleman
1966, 1970). Inclusion of this interaction also
weakens the direct benefit of being male in a
high-SES class by about 27 percent (from
–.060 to –.044). However, the interaction
between SES composition and gender remains
statistically significant ( p = .021) and substantively important. Results suggest that
boys do indeed benefit indirectly from a
stronger academic climate because they are
disproportionately low-performing students.
Nonetheless, the bulk of the effect stems from
Table 5. Fixed-Effects Models with School-Related Attitudes and Behavior
Model 1 Model 2 Model 3
Variable Coef. (se) Coef. (se) Coef. (se)
Female .007 (.02) −.009 (.03) −.009 (.03)
SES Composition .091* (.04) .037 (.04) .033 (.04)
SES Composition x Female −.060** (.02) −.061** (.02) −.040* (.02)
Attitude Toward School .041* (.01) .040* (.02)
Learning Orientation .019 (.01) .017 (.02)
Work Habits .072*** (.01) .092*** (.01)
Attitude Toward School x Female −.047* (.02)
Learning Orientation x Female .002 (.02)
Work Habits x Female −.059*** (.02)
Control Variables yes yes yes
Constant −.673*** (.35) −.538 (.27) −.533* (.25)
Note: N = 4,372. Table 1 describes control variables.
*p < .05; **p < .01; ***p < .001 (two-tailed tests); standard errors adjusted for clustering on class level.
480 American Sociological Review 77(3)
boys’ greater sensitivity than girls to classrooms’ academic orientation.
Discussion
Throughout the industrialized world, girls
have made dramatic gains in educational
attainment, while boys’ underperformance
and their tendency to disrupt the learning
process have sparked intense academic and
public debates about the causes of what many
now call the “problem with boys.” Some
scholars and pundits blame schools for fostering a de-masculinized learning environment.
Yet, the role of school context and the connection between school resources and the
gender gap is underdeveloped in the literature
to date. In this article, we have extended
research on schools’ effect on class and race
inequality by asking whether schools affect
gender inequality as well, and if so, what are
the mechanisms by which this occurs.
Building on theories about gender identity,
adolescent culture, and prior ethnographic
classroom observations, we developed a theoretical argument about the role of environmental factors for the educational gender gap
and boys’ underachievement. In particular,
we argue that school and class environments
shape how masculinity is constructed in peer
culture and thereby influence boys’ orientation toward school. Resources that create a
learning-oriented environment raise the valuation of academics in adolescent male culture
and facilitate commitment. Girls’ peer groups,
by contrast, do not vary as strongly with the
social environment in the extent to which
they encourage academic engagement, and
they are less likely to stigmatize school
engagement as un-feminine. As a consequence, boys differentially benefit from these
school resources, and the female advantage in
test scores shrinks in higher quality schools.
Results from our analysis of German ELEMENT and PISA-I-Plus 2003 data provide
clear support for this hypothesis. We find
substantial variation in the gender gap in academic performance across schools and that this
variation is related to average school performance. We then used a quasi-experimental
research design to establish that boys are
more sensitive to peer SES composition as an
important dimension of school quality related
to the learning environment. This quasiexperimental research design is based on the
argument that randomness plays an important
role for student assignment to classes within
Berlin’s elementary and 5th-grade uppersecondary schools. To evaluate this argument,
we examined Berlin’s school regulations,
compared observed classroom composition
with simulations of random assignment, and
conducted qualitative interviews with school
principals in Berlin. Findings from this evaluation of the selection process generally support our argument but also point at potential
biases, which we addressed with targeted
sensitivity analyses. Results from these analyses show little effect of potential selection
biases on our core results. In addition, we
considered alternative mechanisms that might
explain the observed difference in the causal
effect between boys and girls. Results from
this analysis provide further support for our
own explanation. They suggest that boys
benefit indirectly (because low-performing
students benefit in general) and directly
(because the effect is bigger for boys than for
girls) from being in a classroom with highSES composition.
Our findings contribute to several areas of
research. First, our study makes an important
contribution to the debate about boys’ wellpublicized underperformance. The outlined
cultural mechanism explains why boys are
more sensitive to human and cultural capital
resources in schools, which plays an important role for their underperformance and the
gender gap in educational achievement. This
argument suggests that boys’ resistance to
school is not purely a function of either their
class background—as many studies suggest—or the fact of their masculinity—as
other research suggests—but instead depends
on schools’ and classrooms’ local cultural
environment. As such, the findings broaden
our understanding of boys’ notorious underperformance. Results point at an important
mechanism connected to how school and
class environments shape boys’ and girls’
Legewie and DiPrete 481
learning orientations, and in the process
reveal a pattern similar to what has been
found in families (Buchmann and DiPrete
2006). In both cases, boys seem to be more
sensitive to the level of resources in the local
environment, so that the size of the gender
gap is a function of environmental resources.
Second, our results point to useful directions for new research on policies to raise
boys’ achievement levels. It is obviously
important to know that boys respond especially positively to an academic orientation
among their peers. However, while local governments could decide to invest more
resources in their schools, they cannot, as a
practical matter, produce more high-SES children for their school systems. An important
unanswered question raised by our research
concerns whether schools can accomplish the
same cultural enrichment through alternative
means. The most obvious alternative resource
would be better teachers. Teachers directly
influence schools’ academic environment and
can raise academic performance. They have
the potential to modify student behavior and
produce a stronger academic student culture,
even without socioeconomic enrichment of a
school’s student body. At present, however,
too little is known about what makes a quality
teacher, or the extent of the effect of better
teachers on higher academic performance and
the academic climate. Our research suggests,
for example, that teaching methods that
emphasize academic competition are particularly beneficial for boys. These are important
questions for future research.
Finally, we make a methodological contribution to the literature on estimation of causal
effects. Our work illustrates how a detailed
study of the relevant selection process—in
our case, examination of official regulations,
statistical simulations, and qualitative interviews—can facilitate the estimation of causal
effects. This detailed understanding of the
actual selection process not only allows
researchers to evaluate the extent of bias but
also enables the design of targeted sensitivity
analysis (in our case, based on instrumental
variables and sample restrictions). Overall,
we believe that knowledge about the selection
process can help researchers improve the
accuracy of causal effect estimates. Considering these benefits, we invite sociologists to
take selection processes seriously as an independent object of study—an argument previously made by Sampson (2008:189), who
conceptualizes “selection bias as a fundamental social process worthy of study in its own
right rather than a statistical nuisance” (for an
earlier statement of this argument, see DiPrete
1993).
Our findings are limited in some regards.
Most important, our theoretical argument
applies to all kinds of school resources that
create a learning-oriented environment. Our
empirical analysis, however, only focuses on
one (although important) dimension, peer
socioeconomic composition. Given this limitation, future studies should establish the
extent to which conclusions from this study
apply to other kinds of school-based resources.
Additionally, due to lack of adequate data,
our study neglects teachers’ role in shaping
boys’ and girls’ learning orientations. While
our interviews indicate that teachers are not
assigned to classrooms based on classroom
composition, teachers might react to classroom dynamics in certain ways that play an
important role for the processes studied here.
Finally, our study focuses on only one major
dimension of cognitive achievement, reading.
On average, boys do as well or better than
girls in mathematics, and the male advantage
is larger on the right tail of the distribution.
Whether boys gain a stronger advantage than
girls from being in a classroom with higher
mean SES, or whether their special advantage
occurs only for academic subjects where they
otherwise lag behind girls, is an important
question for future research.
Appendix
Part A. Education and the
Educational Gender Gap in Germany
Although our main focus here is the theoretical argument, background information can
help contextualize findings from the German
case. In Germany, children usually attend
482 American Sociological Review 77(3)
elementary school from age 6 until age 10 or
12, depending on state (Bundesland) regulations. After finishing elementary school, students transfer to secondary schools, which are
distinct from U.S. middle and high schools
because of performance-based tracking on the
school level. Although the system has become
more differentiated in recent decades, three
school types have traditionally been of great
importance. The Gymnasium is the highest
secondary school type, the Realschule is for
intermediate students, and the Hauptschule is
the low secondary school track. As a response
to critiques of this tripartite secondary school
system, some states have introduced comprehensive schools that either integrate all three
school tracks or just the Haupt- and Realschule
(Gesamtschule and Schule mit mehreren
Bildungsgängen). After finishing secondary
school, students have the option to obtain a
higher education degree, to continue their
education in one of the vocational programs
(which figure importantly in the German educational system), or to enter the labor market
immediately. Overall, the German educational
system is distinct from the U.S. system and
other countries primarily because of the early
school-based tracking in secondary school,
the strong vocational track as an alternative to
higher education, and the limited role of the
federal government, which is evident in the
many differences in the specific structure of
German schools across German states. Similar
to other industrialized countries, the gender
gap in Germany has closed over the past
decades. Legewie and DiPrete (2009) emphasize, however, that the female advantage in
higher education is less pronounced than in
the United States due in large part to women’s
failure to converge with men in rates of
obtaining degrees from Fachhochschulen
(universities of applied sciences).
Part B. Simulation of Random
Assignment
Our simulation-based approach allows us to
evaluate whether within-school variation in
the composition of classes is consistent with a
random allocation process. To compare
observed composition with composition
obtained under complete randomization, we
proceed in the following way. For each
school, we randomly allocate students to
classrooms in the school they attend, keeping
the number and size of classrooms constant.
We then compare socioeconomic composition across classes obtained from the simulation with the observed composition.
Accordingly, the simulation evaluates whether
the actual (unknown) allocation process is
consistent with a completely randomized
classroom assignment. The statistic to compare the actual and simulated distribution for
some variable x (e.g., SES, migration background, or gender) for classroom k in school j
is defined as the average square deviation of
the classroom means from the school mean
t
n
x x
k
n
j
j
jk j
j
= − ( ) =
∑ 1
1
2
where j and k are indices for schools and
classrooms, respectively; x
–
j
is the average for
school j; x
–
jk is the average for classroom
k in school j; and nj
is the number of classrooms in school j. If the number of students is
the same in each classroom within a school,
this measure is simply the variance of the
class specific means in a school.
Acknowledgments
We thank Claudia Buchmann, Jennifer L. Jennings,
Merlin Schaeffer, and participants at seminars at the
Mailman School, Columbia University, the University of
Wisconsin, Madison, and the 2010 RC28 conference in
Haifa, Israel for helpful comments and suggestions. We
thank the Institut zur Qualitätsentwicklung im Bildungswesen (IQB) for providing access to data and Prof.
Rainer Lehmann for his help with ELEMENT data.
Notes
1. Of course, stereotypical gender identities also affect
girls. Correll (2001), for example, shows how cultural beliefs about gender can bias women’s
self-perceptions of math ability, controlling for
actual performance, and thereby deter women from
careers in science, math, or engineering.
Legewie and DiPrete 483
2. These assertions do not imply that girls are always
engaged in the learning process. Many studies document how girls resist teachers and school (e.g.,
Francis 2000). Nevertheless, one of the most
common findings in ethnographic studies is that boys
more actively resist the learning process.
3. Our expectations mainly relate to wealthy OECD
countries because prior research finds that the role of
school context (Chudgar and Luschei 2009) and
gender relations differ substantially between highand low-income countries.
4. In contrast to most other states in Germany, students
in Berlin usually attend elementary school until 6th
grade, so the 31 5th-grade upper-secondary schools—
the grundständige Gymnasien—are different from
normal secondary schools.
5. In Berlin, elementary school students assigned to the
same classroom take virtually all their classes
together, so we use the terms “classroom” and “class”
interchangeably in the text.
6. For 5th-grade upper-secondary schools in ELEMENT, class assignment occurs in 5th grade because
students transfer from elementary school after 4th
grade.
7. We obtained both datasets from the Forschungsdatenzentrum at the Institute für Qualitätsentwicklung
im Bildungswesen (IQB) HU-Berlin.
8. As a substantive matter, 5th-grade culture differs
from 9th-grade culture in the obvious sense that 5thgrade students are pre-adolescent while 9th-grade
students have generally passed through puberty. At
the same time, studies of childhood and adolescent
culture find continuity in the emerging masculine
culture between middle childhood and high school
(Maccoby 1998; Thorne 1993). Thus, for substantive
and methodological reasons, we expect comparison
of results from 5th and 9th grades to be informative
about our core hypothesis.
9. The three-level data structure might imply that error
terms of students in the same classroom are correlated even after controlling for school fixed-effects.
We address this problem by correcting the standard
error for clustering on the class level using the
Moulton factor (Angrist and Pischke 2008).
10. Although the PISA-I-Plus is a panel study and collected achievement data in 9th and 10th grades, the
panel component of these data is not yet available.
11. In addition, all schools reported that class changes
within a grade level are extremely rare, and resources
are generally allocated equally across classes.
12. We explored alternative specifications of SES composition effects, such as allowing separate effects of
SES composition of male and female peers. These
alternative specifications yield essentially the same
results as those reported in the tables.
13. One additional school year corresponds to the estimated test score difference between 5th and 6th
grade in the ELEMENT dataset.
14. We use a simple z-test to identify schools in which
the difference in the proportion of students with
migration backgrounds between classes is higher
than what we would expect under randomness. Using
conservative criteria, we exclude schools with a
p-value smaller than .1 (24 schools).
15. We constructed the measures from a range of indicators using exploratory factor analysis (see the online
supplement).
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Joscha Legewie is a PhD student in the Department of
Sociology at Columbia University. His dissertation
research focuses on how school and peer environment
shape gender inequalities in education using two quasiexperimental case studies from Germany and the United
States. Other research looks at the persisting gender gap
in science and engineering education, the role of peer
relations and social support for academic work in school,
and the effect of terrorist events on the perception of
immigrations using a natural experiment.
Thomas A. DiPrete is Giddings Professor of Sociology at Columbia University. His research interests
include social stratification, demography, education,
economic sociology, and quantitative methodology.
Recent research projects include causes of the widening
gender gap in higher education in favor of women, the
persisting gender gap in science and engineering education, social polarization in the United States and its link
with segregation in social networks along several potential dimensions of social cleavage, and the role of social
comparison and cumulative advantage processes in the
trend toward rising inequality at the top of the earnings
distribution.
