Question:
You are given the full text of a scientific research paper.

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    Scientific Research Paper:
    https://doi.org/10.1177/0022146520924810
Journal of Health and Social Behavior
2020, Vol. 61(2) 134–152
© American Sociological Association 2020
DOI: 10.1177/0022146520924810
jhsb.sagepub.com
Original Article
Childhood diagnoses of attention-deficit/hyperactiv-
ity disorder (ADHD) have increased 41% in the
United States over the past decade alone, with 6.4
million (11%) American children ages 4 to 17 having
been diagnosed as of 2016 (Xu et al. 2018). ADHD
is today’s most commonly diagnosed childhood
mental health disorder, surpassing anxiety, depres-
sion, oppositional defiant disorder, conduct disorder,
and autism spectrum disorders (Centers for Disease
Control and Prevention 2019). Without proper treat-
ment, ADHD can lead to more behavior problems,
poorer social relationships, and lower academic per-
formance (Hinshaw and Scheffler 2014).
Increasing medicalization through ADHD diag-
noses may partly reflect parents’ and educators’
awareness of the importance of behavioral/noncog-
nitive skills for long-term success (Conrad 1975).
An ADHD diagnosis can connect children to posi-
tive resources like proper treatment that effectively
manages inattention and hyperactivity/impulsivity
(Molina et al. 2009; Swanson, Baler, and Volkow
2010) and to educational accommodations in
schools like extra testing time (Gius 2007).
However, the effect of an ADHD diagnosis may
not be uniformly positive (Owens and Jackson
2017). Situating ADHD within the broader literature
on negative stereotypes surrounding children with
disabilities (Pescosolido et  al. 2008), diagnosed
children may experience negative feelings of being
unlike their peers (Hinshaw 2005) and increased
scrutiny and lower expectations from teachers
(Eisenberg and Schneider 2007). Depending on how
924810 HSBXXX10.1177/0022146520924810Journal of Health and Social BehaviorOwens
research-article2020
1Brown University, Providence, RI, USA
Corresponding Author:
Jayanti Owens, Department of Sociology and Watson
Institute for International and Public Affairs, Brown
University, 108 George St., Box 1916, Providence, RI
02912, USA.
Email: jayanti_owens@brown.edu
Social Class, Diagnoses of
Attention-Deficit/Hyperactivity
Disorder, and Child Well-Being
Jayanti Owens1
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed mental health disorder
among U.S. children. Diagnosis can bring positives, like proper treatment, extra testing time, and social
support, but may also trigger negatives, like stigmatization. Although rates of diagnosis are high across
socioeconomic status (SES) groups, the balance of positive and negative consequences of diagnosis may
differ by SES. In high-SES communities, mental health diagnoses are less stigmatized and parents have
greater ability to connect children to support resources, suggesting greater positive effects of diagnosis
for high-SES children. Alternatively, the greater academic pressure present in high-SES communities may
amplify the negative effects of mental health stigma, suggesting larger negative diagnostic effects. Using
the Early Childhood Longitudinal Study–Kindergarten Cohort of 1998–1999, I found that diagnosed and
medicated high-SES but not low-SES children exhibit significantly poorer future self-competence and
teacher-rated school behaviors than undiagnosed matches. Findings suggest that diagnosis may not always
be a net positive.
Keywords
ADHD, children, education, inequality, well-being

Owens
135
these positives and negatives balance out, processes
like labeling and stigma can produce negative per-
ceived self-competence and ultimately, higher levels
of the school behavior problems diagnosis is
intended to address.
Rates of childhood ADHD diagnoses are high
across social class groups, ranging from 13%
among children from families earning below the
federal poverty level to 10% of those earning 1 to
3.9 times the poverty level and 9% of those earn-
ing at least 4 times the poverty level (Xu et al.
2018). In spite of relatively high rates of diagno-
sis across social class groups, our understanding
of the relationship between social class privilege
and diagnosed children’s well-being remains
limited.
Variation across social class contexts can lead to
differences in the effects of a diagnosis on child
socioemotional well-being (i.e., children’s expo-
sure to the positive or negative consequences of
diagnosis noted previously). On one hand, research
in the tradition of Lareau (1989) would suggest that
high-socioeconomic-status (SES) parents’ ability to
activate social capital and other resources for edu-
cational intervention is predominantly beneficial
for children. For children with disabilities, high-
SES parents likewise may be better at navigating
schools and the health care system to connect their
child to proper treatment and other supports while
also systematically avoiding negative exposures,
like stigma (Blum 2015; Lareau 1989). For exam-
ple, high-SES families benefit from less mental
health stigma in high-SES communities compared
to less advantaged communities, including many
communities of color (Blum 2015; Gary 2005).
High-SES children may also experience earlier
detection and greater medication treatment and
adherence, strategic medication use, and educa-
tional accommodations like extra testing time com-
pared to low-SES children (King, Jennings, and
Fletcher 2014). As such, one prediction is that high-
SES children will exhibit greater positive effects of
diagnosis than their less advantaged peers.
On the other hand, the negatives associated with
an ADHD diagnosis may be larger for high-SES
than low-SES children. High-SES children might
suffer from the negative effects of mental health
stigma associated with ADHD diagnosis even if
overall levels of such stigma are lower in high-SES
than less advantaged communities (Francis 2012).
For example, whereas low-SES children are more
likely to experience multiple other negative stigmas
(e.g., from school suspension or family instability),
the stigma associated with an ADHD diagnosis may
be one of the few stigmas experienced by high-SES
children. Additionally, if high-SES families are bet-
ter able to access or adhere to medication treatment
for ADHD, any negative side effects of treatment
combined with the rise of polypharmacy might also
disproportionately negatively affect high-SES chil-
dren (Bussing and Winterstein 2012; Comer,
Olfson, and Mojtabai 2010). Thus, a competing
prediction is that the marginal negative effect of
diagnosis may actually be larger for high-SES than
low-SES children even if overall levels of stigma
are lower in high-SES than less advantaged com-
munities (Francis 2012).
Whether and under what conditions a childhood
ADHD diagnosis is associated with better or worse
behavioral outcomes remains an open empirical
question. This study helps reconcile these conflict-
ing expectations by investigating the following
questions:
Research Question 1: How does ADHD diagnosis
affect future perceived self-competence and
ultimately, teacher-rated social and academic
behaviors at school?
Research Question 2: Does an ADHD diagnosis
affect future perceived self-competence and
school behaviors differently for children from
high-SES as opposed to low-SES backgrounds?
The increase in diagnoses of many childhood
mental health disorders is motivated by the belief
that positive consequences, like proper treatment
that effectively manages symptoms, outweigh neg-
ative consequences, like social stigma. However,
empirically testing this belief requires disentan-
gling the implicated social, psychological, and
medical processes. This study introduces a general-
ized framework for contextualizing these poten-
tially positive and negative influences on children’s
well-being and how they might differ by social
class. I then apply this framework to the case of
childhood diagnoses of ADHD. Using matching
techniques among children in the Early Childhood
Longitudinal Study–Kindergarten Cohort of 1998–
1999, I find that diagnosed and medicated middle-
and upper-SES—but not lower-SES—children
exhibit significantly poorer future self-competence
and teacher-rated school behaviors than undiag-
nosed matches. Findings carry important implica-
tions for the reproduction of privilege within the
larger process of the medicalization of childhood
behaviors.

136
Journal of Health and Social Behavior 61(2)
Background
Theoretical Framework: Disentangling
Social, Psychological, and Medical
Influences on Children’s Well-Being
I interpret the effects of ADHD diagnosis on chil-
dren’s socioemotional well-being in the context of a
framework that encompasses broader interrelated
social, psychological, and medical factors. In the
following, I note how these factors can unfold in
families and schools in ways that have both positive
and negative influences on diagnosed children and
how these may differ by social class.
Positive social, educational, and medical processes
for diagnosed children compared to similar undiag-
nosed children.  Children may experience benefits
from a mental health diagnosis compared to their
similar undiagnosed counterparts. Diagnosis can
bring access to appropriate medications that help
control behaviors, reduce anxiety, and improve
mood. In schools, diagnosis may also bring positive
educational and social supports, such as extra test-
ing time through a 504 Plan, or connection to peers
who have similar mental health challenges, through
an Individualized Education Program (IEP) and
special education classes (Gius 2007). That is, diag-
nosis can help “level the playing field” compared to
otherwise similar undiagnosed children.
The education and disability literatures help to
explain why these positive effects may be larger for
children from high-SES families. High-SES parents
are more able to mobilize social capital and other
resources to successfully navigate school and medi-
cal bureaucracies in order to connect their child to
positive resources at the first signs of difficulty
(Lareau 1989). For children with disabilities, Ong-
Dean (2009:4) argued that “it is precisely because
schools often try to manage students with disabili-
ties in the easiest, cheapest ways that parents’ advo-
cacy is so important,” but “parents’ resources affect
how far they can shoulder this burden of being
involved in identifying and accommodating their
children’s disabilities” (p. 4). For example, Liu,
King, and Bearman (2010) show that residential
proximity to other children diagnosed with autism
shapes a child’s own subsequent diagnosis due to
the spread of information about how to secure diag-
noses and the benefits of doing so. Blum (2015)
also shows that social capital is one resource that
helps high-SES parents connect children to the best
teachers and proper treatments and educational
accommodations. Finally, mental health conditions
are also less stigmatized in high-SES than low-SES
communities, which can lead to better medication
adherence (Ohan et  al. 2013; Pescosolido et  al.
2008).
In addition to being better positioned to inter-
vene at school, high-SES parents have greater
knowledge of the behaviors and skills rewarded in
schools and are more likely to support development
of these skills through a style of parenting at home
known as “concerted cultivation” (Lareau 2003).
Concerted cultivation is oriented around fostering
children’s talents and ability to navigate social insti-
tutions through participation in organized activities,
including education-enhancing activities like tutor-
ing. Together, early detection, greater medication
adherence and social support, and less stigma in
high-SES compared to low-SES communities may
lead to even greater positive effects of diagnosis for
children from more advantaged than less advan-
taged backgrounds.
Negative social and psychological processes for
diagnosed children compared to similar undiagnosed
children.  Mental health diagnoses can also instigate
negative social and psychological processes. Litera-
ture focusing especially on labeling and stigma
helps to explain potential negative consequences
associated with diagnosis (Goffman 1963; Scheff
1974). Negative social feedback from teachers and
peers may even further exacerbate negative diag-
nostic effects on the child, especially when future
school behaviors are rated by teachers (Rist 1977).
The child may experience a sense of inadequacy
or low self-esteem based in beliefs that they have
less academic ability than their peers, or that their
teachers and parents do not believe they can succeed
at a high level (Eisenberg and Schneider 2007).
Moreover, if children internalize this shift in self-
competence as a result of exposure to negative soci-
etal stereotypes about individuals with disabilities,
stigma can instigate a self-fulfilling prophecy even
in cases where teachers and peers do not know about
the child’s diagnosis, resulting in poorer behavioral
and socioemotional outcomes (Link et al. 1989).
Diagnosis may also have a larger negative effect
on high-SES than low-SES children. In the case of
learning disabilities (LD), Hale (2011) argues that
disabilities are socially constructed in that “the
more contextual structures demand academic skills
and dispositions, the more children with learning
impairments become ‘learning disabled’” (p. 4). In
high-SES contexts where parents activate social
capital for school intervention (Lareau 1987) and
reflect school values in their home environment
(Lareau 2003), academic excellence is often not

Owens
137
only expected, but expected to come easily (Mueller
and Abrutyn 2016).
Moreover, Francis (2012) found that children
with “invisible disabilities,” like ADHD and LD,
experienced higher levels of stigma than those with
“visible disabilities,” like intellectual disability,
because their communities tended to accept invisible
diagnoses as legitimate while holding diagnosed
children and their families accountable to the same
high expectations of normally functioning children.
With the academic demands present in high-SES
contexts, even subtle negative feedback from teach-
ers, peers, or family, may lead diagnosed high-SES
children to believe that they fail to meet academic
expectations. Given that high-SES children are also
less likely to have as many other negative exposures
as low-SES children, the marginal effect of a mental
health label may loom even larger for high-SES than
low-SES children. High-SES children might believe
themselves to be even more “learning disabled” than
their low-SES counterparts (Hale 2011) while also
being more likely to tie their sense of self-worth to
their school performance (Conner and Pope 2013).
In sum, through the label that accompanies their
diagnosis, high-SES children might experience
larger negative effects of stigma, even though abso-
lute levels of mental health stigma are lower than in
low-SES communities. As a result, diagnosed chil-
dren may experience even poorer self-competence
and, through a self-fulfilling prophecy, poorer
school behaviors, than diagnosed low-SES children.
The Case of Childhood ADHD
Diagnosis and Child Well-being
Most prior research on children with ADHD has
examined the consequences associated with having
ADHD versus not having ADHD based on a com-
parison of outcomes of children with versus without
high levels of behavioral problems prior to a diag-
nosis (path a of Figure 1). This prior work finds that
having high levels of behavior problems is associ-
ated with a range of poorer social, educational, and
well-being outcomes (Charach et al. 2013; Currie
and Stabile 2006). By contrast, only one prior study
known to the author isolates the role of the ADHD
diagnosis (path d of Figure 1), as opposed to that of
the underlying behavior problems (path a) or other
factors (paths b–c). In that study, Owens and
Jackson (2017) compare diagnosed and undiag-
nosed children who had the same underlying pre-
diagnosis behavioral problems. They find that, even
among diagnosed and medicated children who had
mild behavior problems prior to diagnosis, an early
elementary school ADHD diagnosis is associated
with roughly .30 standard deviation (SD) lower
math and reading scores in 8th grade. Owens and
Jackson (2017) focus on long-term academic out-
comes and as such are unable to shed light on the
implicated social and psychological outcomes in
addition to medical processes.
The present study builds upon that prior work to
apply the theoretical framework elaborated above to
the case of childhood ADHD diagnosis. I disentangle
Figure 1.  Framework for Assessing the Relationship between Social Class, ADHD Diagnosis,
Medication Treatment, and Children’s Future Socioemotional Well-being.

138
Journal of Health and Social Behavior 61(2)
implicated social, psychological, and medical pro-
cesses and examine potentially heterogeneous diag-
nostic effects on children’s subsequent perceived
self-competence and teacher-rated school behaviors
by social class.
Social Class Differences in the Positive Effects
of Diagnosis Compared to Similar Undiagnosed
Children.  For all children, an ADHD diagnosis can
lead to the social and educational supports dis-
cussed above (Gius 2007). Moreover, in high-SES
families, parents are more likely to believe that an
ADHD diagnosis will be beneficial. Combined
with the fact that high-SES families are more able
to activate their social capital to intervene in
schools and the medical system, high-SES fami-
lies are consequently more inclined and able to
seek out diagnosis at the early signs of behavioral
difficulties. Thus:
Even without medication, diagnosis is associated
with positive effects on future self-competence
and school behaviors. Moreover, diagnosed and
unmedicated children from high-SES backgrounds
on average exhibit better future self-competence
and school behaviors than diagnosed and
unmedicated children from low-SES backgrounds,
each relative to their undiagnosed counterparts
(hypothesis 1).
In terms of medical processes, the stimulant
medications commonly prescribed for ADHD
(e.g., Ritalin and Adderall) have been shown to effec-
tively reduce inattention and hyperactivity in children
(Molina et al. 2009; Swanson et al. 2010). Greater
parental social capital and economic resources com-
bined with lesser stigma surrounding diagnosis may
mean greater medication access, adherence, and stra-
tegic use among high-SES children (Hinshaw and
Scheffler 2014). Indeed, research shows that chil-
dren from higher-SES backgrounds are more likely
than children from lower-SES backgrounds to
increase the use of the stimulant medications to
improve attention and concentration during months
with high levels of academic pressure, such as dur-
ing finals periods, and limit use during holiday
breaks (King et al. 2014). Thus:
Diagnosed and medicated children from high-
SES backgrounds on average exhibit even better
future self-competence and school behaviors
than diagnosed and medicated children from
low-SES backgrounds (hypothesis 2).
Social Class Differences in the Negative Effects
of Diagnosis Compared to Similar Undiagnosed
Children.  The rise in ADHD diagnoses is driven by
an assumption about diagnosis’ positive behavioral
effects, but an ADHD diagnosis might instead insti-
gate negative social feedback from teachers and
peers. Its status as an “invisible disability” may lead
diagnosed children and family members to internal-
ize a sense of culpability for the child’s difficulties,
which is consistent with experiences of labeling and
stigma. For children from high-SES backgrounds in
particular, parents’ ability to activate social capital
for school intervention (Lareau 1987) and reflect
school values in their home environment (Lareau
2003) combined with fewer other negative expo-
sures may further magnify diagnosed children’s
sense of being “disabled” and unable to meet high
academic expectations. Thus:
Without medication, diagnosis has negative effects
on future self-competence and school behaviors.
Moreover, diagnosed and unmedicated children
from high-SES backgrounds on average exhibit
worse future self-competence and school behaviors
than diagnosed and unmedicated children from
low-SES backgrounds, each relative to their
undiagnosed counterparts (hypothesis 3).
The benefits of proper medication discussed previ-
ously may help effectively manage symptoms, and
children from high-SES families may have greater
medication access or adherence. However, greater
medication access and adherence may also lead
high-SES children to be more likely to experience
negative side-effects of treatment or polypharmacy.
Thus:
Diagnosed and medicated children from high-
SES backgrounds on average exhibit comparable
future self-competence and school behaviors as
diagnosed and medicated children from low-
SES backgrounds (hypothesis 4).
These hypotheses are summarized in Figure 2.
The y-axis displays expected differences in the
future school behaviors of diagnosed vs. otherwise
comparable undiagnosed children. Positive bars
represent better future teacher-rated school behav-
iors among diagnosed compared to undiagnosed
children, while negative bars represent poorer
future behaviors, and the dotted line represents no
difference between diagnosed and undiagnosed
children.

Owens
139
Data And Methods
Research Design: Disentangling the
Effects of Behavior, Diagnosis, and
Medication
Data and sample.  To estimate differences between
the teacher-rated school behaviors and perceived self-
competence outcomes of diagnosed and undiagnosed
children, this study draws on the restricted-use Early
Childhood Longitudinal Study-Kindergarten Cohort
of 1998 (ECLS-K), an initially nationally representa-
tive sample of kindergarteners followed through mid-
dle school. The analytic sample used here consisted of
the 7,330 children who remained in the study through
5th grade, were not missing data on either the out-
comes or ADHD diagnosis, and were either diag-
nosed (N = 380) or plausible undiagnosed matches
who had comparable levels of early ADHD-related
behavioral problems in spite of not having been diag-
nosed (N = 6,950). Details on the longitudinal sample,
attrition, and trimming are detailed in the Online
Appendix; cell sizes were rounded to the nearest 10
per the restricted-use data agreement.
These data offered several substantial strengths for
isolating the net “marginal effect” of an ADHD diag-
nosis (path d of Figure 1)—defined as the average dif-
ference between the outcomes of diagnosed and
otherwise comparable undiagnosed children who had
the same observed pre-diagnosis behavioral problems,
cognitive skills, family/demographic contexts and
overall propensities to be diagnosed even though one
child was diagnosed and the other was not. First, given
that ADHD diagnoses require functional impairment
from inattention and/or hyperactivity/impulsivity in at
least two settings (American Psychiatric Association
2013), both teachers and parents rated children’s
behaviors. Moreover, ratings occurred in children’s
naturalistic settings (school and home), independent
of diagnostic evaluation and less subject to reporting
bias.
Second, to directly measure the core behaviors
of ADHD, access to certain individual-level behav-
ior items were made available through copyright
approval from the publisher, allowing diagnosed
and undiagnosed children to be matched on the indi-
vidual subscales of inattention and hyperactivity/
impulsivity. Although imperfect, these reports
resemble those used in ADHD screeners such as the
Connor’s, SNAP-IV, and DSM-IV (limitations are
discussed in the Discussion section) (Currie and
Stabile 2006; Swanson et  al. 2010). Moreover, to
address potential confounding from conduct/opposi-
tional defiance disorder and depression, the two
most common co-morbid conditions of ADHD
(Hinshaw and Scheffler 2014), I also matched on
relevant pre-diagnosis anti-social behaviors and
internalizing behaviors in addition to inattention and
hyperactivity. Third, ratings were for all sample
children, including the roughly 95% of children with
developmentally normal behaviors who are never
diagnosed, thus providing a wide range of suitable
undiagnosed comparison cases, or “matches.”
Fourth, behavior ratings were collected in the
wave prior to diagnosis with ADHD, offering a
substantial strength over measuring pre-diagnosis
behaviors in a static period. For example, exclu-
sively measuring behaviors in kindergarten would
understate behavior problems for children whose
Figure 2.  Hypothesized Differences in the Future Self-concept and Behavior of Diagnosed vs.
Undiagnosed Children, by Social Class and Whether Receiving Medication

140
Journal of Health and Social Behavior 61(2)
problems increase after school entry and before
diagnosis. Conversely, drawing exclusively on third
grade measures (the end of the diagnostic observa-
tion period) could introduce reverse causality issues
if diagnosis prompts behavior change. For undiag-
nosed children, behavior problems come from first
grade, the near mid-point of the Kindergarten
through third grade diagnostic observation period.
Since behavior problems typically decrease with
age, the use of kindergarten behaviors for undiag-
nosed children would likely overstate problems,
while third grade measures would understate them.
Treatment of missing data.  Of the working ana-
lytic sample of 7,330 children, 15.4% (N = 1,130)
lacked complete information on matching variables.
Item-missingness on these covariates was more
common among boys, African-Americans, and chil-
dren of mothers with less than a college degree.
Multiple imputation of twenty datasets was used to
address item-missingness on matching variables.
ADHD diagnosis and the outcomes are included in
the imputation equation but children originally
missing on these outcomes are excluded from all
analyses, per Von Hippel (2007).
Analytic Strategy: Matching for Sample
Balance
Because ADHD diagnosis is not randomly assigned,
there are a number of potential child, family, and
classroom/school confounders that may influence
both diagnosis and outcomes. These include ADHD-
related behaviors prior to diagnosis, cognitive skills,
sex, age-for-grade (whether they are older or younger
than their classmates) (Layton et al. 2018), internaliz-
ing behavior problems score (a common co-morbidity
of ADHD), insurance coverage status, race/ethnicity
(Morgan et al. 2013), parents’ concerted cultivation
parenting styles, type of kindergarten classroom, and
average classroom behavior based on teacher ratings
on a scale from zero (“students are typically extremely
poorly behaved”) to four (“students are typically
extremely well behaved”), which might help shape
reference group effects. If unobserved, these factors
may lead to biased estimates of the marginal effect of
diagnosis.
To help address some of these potential con-
founders, standard multiple regression techniques
would introduce controls to adjust for consequential
observable differences between diagnosed and undi-
agnosed children. However, standard controls within
OLS regressions may not be adequate if there is
insufficient overlap, or balance, in the distributions
of characteristics between diagnosed and undiag-
nosed children (Imbens and Rubin 2015).
Researchers have turned to matching techniques
to help achieve sample balance on key variables for
which OLS models may yield estimates that lack
sample support/balance (Gangl 2010). This study
used two types of matching to obtain estimates that
are “doubly robust” to confounding between diag-
nosed and undiagnosed children: coarsened exact
matching (CEM) and propensity score matching
(PSM) (Stuart et al. 2009). First, I use CEM to pre-
process the data and ensure sample balance on three
key factors that shape both diagnosis and the out-
comes: family social class group (given the study’s
theoretical motivation), severity quartile of pre-
diagnosis behavioral problems (a key confounder
of diagnostic effects), and child sex (since sex dif-
ferences in children’s presentation of ADHD-
related behaviors could produce improper matches
given greater diagnosis of the hyperactive subtype
among boys).
The advantages of CEM include that it is not
a method of estimation but, rather, a way to pre-
process a data set. As a result, estimation based on
the matched data set will be less model-dependent
(i.e., less a function of indefensible modeling deci-
sions) than when based on the original full data set
(Iacus, King, and Porro 2011). Because the degree
of model dependence is bounded ex ante by the
researcher, estimates are less vulnerable to mis-
specification of functional form. Second, CEM bal-
ances the nonlinearities and interactions between
variables that exist in the data. Third, CEM restricts
the matched data to areas of common empirical sup-
port, helping to ensure that the researchers are not
extrapolating beyond the data. Finally, the trimming
described earlier leads to lower bias during subse-
quent estimation, and (by removing heterogeneity)
increased efficiency (Iacus et  al. 2011). Once the
CEM samples have been generated, subsequent data
analysis resumes use of the original values of all
covariates (King et al. 2010).
Nonetheless, CEM is limited by the number of
exact matching dimensions it can accommodate.
Even after CEM, still there may be additional
observable child, family, and classroom/school dif-
ferences between diagnosed and undiagnosed chil-
dren even within coarsened groups (Imbens and
Rubin 2015). Thus, PSM was used second, after
CEM, to help address additional possible confound-
ing by ensuring that diagnosed and undiagnosed
children were comparable, or balanced, on 37
potential child, family, and classroom matching
variables included in the PSM equation: all six

Owens
141
teacher- and parent-rated behavioral dimensions
and 31 other variables mentioned above and
detailed in Table 1 and the Online Appendix.
Altogether, matching allowed me to isolate the
marginal effects of diagnosis by comparing diag-
nosed and undiagnosed children with comparable
underlying pre-diagnosis behavior problems, cog-
nitive skills, sex, and other child, family, and class-
room characteristics. I examined variation by
family social class. Finally, I examined the sensitiv-
ity of results to modeling approach (see the Online
Appendix).
Models
First, differences by family social class in the mar-
ginal effects of diagnosis were estimated overall and
then separately by whether or not the child subse-
quently received medication following diagnosis. In
the latter analyses, diagnosed children were further
classified as those who “additionally receive medi-
cation” and those who “do not receive medication,”
both relative to undiagnosed children. This modera-
tion analysis (first by family social class and then
also by medication status) occurred after CEM but
before PSM (Stuart et al. 2009). To guard against
reverse-causality issues, sensitivity analyses relied on
medication receipt reported only in third grade; result-
ing estimates remained substantively unchanged.
Baseline models pooling across social class are shown
in Appendix Table A.1.
The CEM package in Stata 14 was used to inde-
pendently and ex ante ensure that diagnosed and
undiagnosed children were “exact matches” on
three characteristics on which there are well-
established differences in diagnosis and future
behaviors: (1) family social class (3 groups); (2)
quartile of pre-diagnosis behavioral problems based
on parent- and teacher-rated subscales for inatten-
tive or hyperactive/impulsive behavioral type (16
groups); and (3) child sex (2 groups). The continu-
ous variables for social class and pre-diagnosis
ADHD-related behaviors were first temporarily
“coarsened” into the above categories so that match-
ing occurred within the 96 broad groups above (3 ×
16 × 2 = 96). All 380 diagnosed children were suc-
cessfully matched to otherwise comparable undiag-
nosed children. By contrast, 340 (.5% of)
undiagnosed children were pruned for reasons of
achieving balance described in the Online Appendix.
For the pre-diagnosis ADHD-related behaviors
mentioned above, there was substantial overlap in
the distributions of both diagnosed and undiag-
nosed children: for both groups, pre-diagnosis
behavioral ratings spanned the full range from
“never” to “almost always.” As mentioned earlier,
with 5.2% of sample children ages 4 to 10 diag-
nosed with ADHD, nearly 95% of children are not
diagnosed, offering a large number of potential
matches who were not diagnosed but nonetheless
had the same propensity to be.
PSM occured after the CEM and is detailed in
the Online Appendix (balance statistics are in
Appendix Table A.2). This approach does not
remove the risk of bias from omitted variables and
measurement error. The term marginal “effect” is
used with this important caveat.
Key Measures
Table 1 displays all variables used in the analyses.
Dependent variables: child perceived self-competence
and teacher-rated school behaviors in fifth grade.
Teacher-rated “positive approaches to learning”—
or attentiveness, task persistence, eagerness to learn,
learning independence, flexibility, and organiza-
tion—and teacher-rated “externalizing behavior
problems”—or social problems like the frequency
of arguing, fighting, getting angry, acting impul-
sively, and disturbing ongoing activities—were sub-
scales from the Social Rating Scale; the child-rated
outcome was from the Self-Description Question-
naire (Tourangeau et al. 2009). Each scale averaged
across items for behavioral frequency that ranged
from 0 = “rarely” to 3 = “always.”1
I focused on fifth grade behavioral outcomes,
first, because I anticipated the largest diagnostic
effects here and, second, because I believed there
was a reciprocal relationship between perceived
self-competence and teacher-rated school behav-
iors. Although student self-reported measures were
available in eighth grade, teacher ratings of school
behaviors were not. Relying on the eighth grade
measures would have introduced a (false) assumed
causal ordering to the outcomes.
Primary independent variables.  ADHD diagnosis
between kindergarten and third grade (“treat-
ment”) was identified based on when the parent
answered “yes” to all three of the following ques-
tions in a given wave: (1) “Has the child been evalu-
ated by a professional in response to a problem in
paying attention, learning, behaving, or in activity
level?” (2) “Has the child received a diagnosis by
this professional?” and; (3) “Was the diagnosis for
ADHD, ADD, or hyperactivity?” (see Morgan et al.
[2013] and Tourangeau et al. [2009] for advantages

142
Journal of Health and Social Behavior 61(2)
Table 1.  Means and Standard Deviations (or Proportions) for Variables Used in the Main Text (N = 7,330).
Upper SES
(N = 1,850)
Middle SES
(N = 3,630)
Lower SES
(N = 1,850)
Overall
Min
Overall
Max

Mean
SD
Mean
SD
Mean
SD

Outcomes (5th Grade)
  Positive approaches to learning behaviors
score (teacher report)–5th grade
2.29b
.69
2.10a,b
.75
1.91a
.76
0
3
  Negative externalizing behavior problems
score (teacher report)–5th Grade
.42b
.57
.56a,b
.65
.63a
.69
0
3
  Perceived self-competence score–5th grade
(child report)
2.19b
.77
2.05a,b
.80
2.01a
.81
0
3
ADHD Diagnosis (K–3rd Grades) and Possible Medication Receipt (3rd–5th Grades)
  Diagnosed with ADHD
.04
.05
.04
0
1
  Receiving medication in 3rd–5th Grades
(of those diagnosed)
.80
.80
.75

Pre-diagnosis ADHD-related and Co-occurring Behaviors (Measured in the Wave Prior to Diagnosis
or 1st Grade for Undiagnosed)
  Inattentive behaviors score (teacher report)
–.18b
.80
–.01a
.86
.18a,b
.90
-1
2
  Hyperactivity behaviors score (teacher report)
–.09b
.63
.01a
.69
.07a,b
.74
-1
2
  ODD or CD behaviors score (teacher report)
–.07b
.65
–.01a
.66
.09a,b
.68
-1
2
  Internalizing problems score (teacher report)
–.08b
.61
–.01a
.61
.09a,b
.64
-1
2
  Inattentive behaviors score (parent report)
–.11b
.66
–.01a
.69
.11a,b
.71
-1
2
  Hyperactivity behaviors score (parent report)
–.07b
.57
–.01a
.65
.10a,b
.73
-1
2
  ODD or CD behaviors score (parent report)
–.07b
.42
–.04a
.46
.14a,b
.57
-1
2
Early Cognitive Development (Kindergarten)
  Average reading achievement scores (std.)
.50b
.97
–.06a,b
.91
–.68a
.89
-3
4
  Average math achievement scores (std.)
.54b
.89
.04a,b
.87
–.65a
.91
-3
4
  Fine motor skills score
6.45b
1.75
6.10a,b
1.94
5.37a
2.14
0
9
Early School Context (Kindergarten)
  Average behavior of students in kindergarten
class (teacher report)
2.60b
.75
2.47a,b
.80
2.33a
.80
0
4
  Child received any special education services
in kindergarten
.08b
.10b
.11
0
1
  School in state with consequential educational
accountability standards
.70
.68a
.73a
0
2
Early “Concerted Cultivation” at Home and Parental Involvement in Education/Schooling
(Kindergarten)
  Parental participation in educational
institutions
4.87b
1.28
4.20a,b
1.50
2.96a
1.59
0
7
  Child activities and leisure time
3.76b
1.83
2.76a,b
1.70
1.71a
1.53
0
10
  Parent perceptions of responsibilities towards
child cognitive and social development
11.05b
2.71
10.41a,b
2.95
9.45a
3.43
0
18
  Number of books at home
109.31b
58.25
82.15a,b 56.76
41.82a
45.09
0
200
  Parent educational expectations for child
1.93b
.30
1.69a,b
.58
1.44a
.78
0
2
  Child age (months) at kindergarten entry
65.49
4.24
65.65a
4.34
65.28a
4.44
37
81
Family Demographic and Other Child Characteristics (Kindergarten)
  Age of social mother
36.68b
4.97
33.72a,b
5.94
31.87a
7.42
18
80
  Mother has CES-D Score >9 (clinically
depressive symptoms)
.10b
.17a,b
.25a
0
1
  Child born weighing less than 5.5 lbs (low
birthweight)
.06b
.08a,b
.10a
0
1
  Child not covered by insurance
.15
.15a
.20a
0
1
  Number of other children in household
1.51b
.97
1.44a,b
1.06
1.79a
1.32
0
11
  Child been in childcare outside home
.53
.53a
.44a
0
1
  Male
.52
.51
.49
0
1
  Black
.03b
.10a,b
.20a
0
1
(continued)

Owens
143
of this conservative approach to identifying diag-
nosed children). Children whose parents answered
“no” to any of these questions were coded as “not
diagnosed with ADHD” between respective waves.
Diagnosis during the kindergarten through third
grade diagnostic observation period was confirmed
using parent report of “year of first [ADD/ADHD]
diagnosis.” The 14% of diagnosed children who
were diagnosed by kindergarten were retained and
coded “1” (diagnosed), but sensitivity analyses
excluding these children for whom measurement of
ADHD-related behaviors did not precede diagnosis
yielded consistent substantive results. Children first
diagnosed with ADHD after third grade were also
retained but were coded “0” (undiagnosed). Their
inclusion as potential “undiagnosed matches”
yielded conservative (lower-bound) estimates of the
effect of diagnosis as they may have had undiag-
nosed ADHD during the kindergarten through third
grade diagnostic observation period.
Medication treatment receipt between 3rd and
5th grades (“treatment moderator”) was ascer-
tained based on parent report of whether the child
was “taking medication to control his/her behav-
ior” in third or fifth grade. In fifth grade, parents
were asked to name these medications. 90% of the
children “receiving medication” were taking one
of three medications frequently prescribed for
ADHD: Ritalin, Adderall, or Concerta. Of the 50
children diagnosed by kindergarten, 40 (80%) were
receiving medication by fifth grade. Of the 210
additional children first diagnosed after third
grade, 150 (71%) were taking medication in fifth
grade.
Family social class background in kindergarten
(“predictor/moderator”) was categorized, follow-
ing Cheadle (2008), using a composite, standard-
ized scale consisting of mother/female guardian’s
and father/male guardian’s educational attainment,
household income, and parental occupational pres-
tige (see chapter 7, pages 8–11 of Tourangeau et al.
[2009]) and conceptualized the bottom quartile as
“lower SES,” the middle two quartiles as “middle
SES,” and the top quartile as “upper SES”. There
are, of course, multiple approaches for measuring
social class. The composite gives precedence to
family income, although I note that the results were
similar when using maternal educational level
(mom has a bachelor’s degree or higher versus
mom has less than a bachelor’s degree).
Other variables in the PSM equation. To help
ensure that diagnosed and undiagnosed children
were as comparable as possible on observable char-
acteristics, a number of other key measures, includ-
ing pre-diagnosis ADHD-related behaviors and
commonly co-occurring (“comorbid”) behaviors,
were included as PSM variables and are described
in the Online Appendix.
Upper SES
(N = 1,850)
Middle SES
(N = 3,630)
Lower SES
(N = 1,850)
Overall
Min
Overall
Max

Mean
SD
Mean
SD
Mean
SD

  Hispanic
.07b
.13a,b
.36a
0
1
  White
.79b
.67a,b
.32a
0
1
  Other race/ethnicity
.11
.10
.12
0
1
  Single parent household
.07b
.14a,b
.29a
0
1
  Social father present in household
.01b
.07b
.07
0
1
  Other family type in household
.04b
.05a,b
.08a
0
1
  Two biological parents in household
.88b
.75a,b
.56a
0
1
  Lives in Midwest
.31
.32a
.18a
0
1
  Lives in West
.20b
.18a,b
.27a
0
1
  Lives in Northeast
.22b
.19a,b
.13a
0
1
  Lives in South
.26b
.30a,b
.40a
0
1
Source: Early Childhood Longitudinal Study-Kindergarten Cohort, 1998–1999.
Note: ADHD = attention-deficit/hyperactivity disorder; CD = compulsive disorder; ODD = oppositional defiant
disorder; SD = standard deviation; SES = socioeconomic status.
aSignificant difference between lower SES and middle SES at p < .05. All significant/non-significant differences between
lower SES and middle SES also apply to the difference between lower SES and upper SES (except child age, which is
not significant between lower SES and upper SES).
bSignificant difference between middle SES and upper SES at p < .05.
Table 1.  (continued)

144
Journal of Health and Social Behavior 61(2)
Results
The primary goals of this analysis are to: (1) deter-
mine whether differences in the future perceived
self-competence and teacher-rated school behaviors
of diagnosed and undiagnosed children are located
among those from middle- or upper-SES rather than
lower-SES backgrounds; (2) examine whether any
social class differences hold even for students who
receive medication treatment following diagnosis,
and; (3) explore variation in these relationships by
child sex.
Descriptive Differences by Family
Social Class
To understand the differing contexts within which a
childhood ADHD diagnosis occurs in this sample,
Table 1 displays descriptive statistics for analyzed
variables by family social class. There is a statisti-
cally significant social class gradient on all three
outcomes: lower-SES children exhibit the poorest
positive approaches to learning, perceived self-
competence, and negative externalizing problems,
followed by middle- and then upper-SES children.
However, on diagnosis and medication use as
well as many predictors, middle-SES and upper-
SES children are quite similar. Consistent with
national statistics interpolated for children ages five
to nine, roughly 4% to 5% of lower-SES, middle-
SES, and upper-SES children are each diagnosed
with ADHD (see Appendix Table A.3 for counts)
(Xu et al. 2018). 80% of both diagnosed middle-
and upper-SES children receive medication follow-
ing diagnosis, compared to 75% of diagnosed
children from lower-SES families. Additional
descriptive differences are discussed in the Online
Appendix.
Social Class Differences in the
“Marginal Effect” of an ADHD
Diagnosis
Given this study’s theoretical emphasis on moderat-
ing differences by family social class, Table 2 pres-
ents estimates of differences in outcomes between
diagnosed children and their undiagnosed matches
of the same social class group. For clarity, results
are presented sequentially for each social class
group before making explicit comparisons across
social class groups.
Overall, results are consistent with hypothesis 3
and inconsistent with hypotheses 1 and 2: an ADHD
diagnosis has a negative “marginal effect” on future
child self-competence and teacher-rated school
behaviors, but only among children from middle-
and upper-SES families (Table 2). Both diagnosed
children from upper-SES and middle-SES back-
grounds exhibit .36 points (or .36 / .69 = .52 SD)
statistically significantly lower future positive
learning-related behaviors in fifth grade compared
to their undiagnosed counterparts who had similar
propensities for diagnosis but were not diagnosed
Table 2.  Average Marginal Relationships between an ADHD Diagnosis and Future Social and Academic
Behaviors and Child’s Perceived Self-competence, by Family Social Class (N = 7,330).
Positive Approaches to Learning
(Teacher Report) – 5th Grade
Externalizing Behavior Problems
(Teacher Report) – 5th Grade
Perceived Self-competence
(Child Report) – 5th Grade

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)

Upper
SES
(N = 1,850)
Middle
SES
(N = 3,630)
Lower
SES
(N = 1,850)
Upper
SES
(N = 1,850)
Middle
SES
(N = 3,630)
Lower
SES
(N = 1,850)
Upper
SES
(N = 1,850)
Middle
SES
(N = 3,630)
Lower
SES
(N = 1,850)
Diagnosed
with ADD/
ADHD
–.36***a
(.08)
–.36***b
(.06)
–.10a,b
(.09)
.25***
(.07)
.18*
(.06)
.08
(.08)
–.25**a
(.09)
–.27***b
(.06)
.08a,b
(.09)
Source: Early Childhood Longitudinal Study-Kindergarten Cohort, 1998–1999.
Note: Displaying propensity score matching estimates of the “average marginal effect” of ADHD diagnosis with
coarsened exact matching to preprocess the data (see text). Standard errors in parentheses. ADD = attention deficit
disorder, ADHD = attention-deficit/hyperactivity disorder, SES = socioeconomic status.
aSignificant difference between lower SES and upper SES at p < .05.
bSignificant difference between lower SES and middle SES at p < .05.
cSignificant difference between middle-SES and upper-SES at p < .05.
*p < .05, **p < .01, ***p < .001.

Owens
145
(Models 1–2). Diagnosed upper-SES children also
exhibit .25 points (.25 / .57 = .44 SD) statistically
significantly higher externalizing problems in fifth
grade than their undiagnosed counterparts (Model
4). Diagnosed middle-SES children likewise exhibit
statistically significant .18 points (.18 / .65 =
.28 SD) higher externalizing problems than their
undiagnosed counterparts (Model 5). Finally, diag-
nosed upper-SES children report statistically sig-
nificantly lower perceived self-competence by .25
points (.25 / .77=.32 SD). Diagnosed middle-SES
children likewise report .27 points (.27 / .80 = .34 SD)
statistically significantly lower perceived self-com-
petence relative to undiagnosed matches who had a
similar propensity to be diagnosed (Models 7–8).
Consistent with prior qualitative work (Lareau
2003), differences in outcomes between diagnosed
and undiagnosed middle- versus upper-SES children
are not statistically significant from one another.
One explanation for the poorer future school
behaviors and perceived self-competence of diag-
nosed middle- and upper-SES (but not lower-SES)
children is that diagnosis triggers a social process
that objectively leads to poorer behaviors in diag-
nosed middle- and upper-SES children versus their
undiagnosed matches. Alternatively, teachers may
also have biased reports, for example from aware-
ness of a child’s diagnosis. Although this study is
unable to distinguish between these processes, even
subjectively rated behaviors have important conse-
quences for self-fulfilling prophecies as discussed
previously.
Moreover, the lower perceived self-competence
of diagnosed middle- and upper-SES children but
not diagnosed lower-SES children points to the
salience of an internalized psychological process
consistent with labeling. Internalization may be one
key mechanism underlying poorer future school
behaviors, as explored in the Online Appendix.
Results in Table 3 differentiate between diag-
nosed upper-SES children who did versus did not
receive medication following diagnosis. Results are
consistent with hypothesis 3 on two of the three
outcomes and run counter to hypothesis 4. Among
diagnosed upper-SES children, ADHD diagnosis is
associated with poorer approaches to learning and
externalizing problems irrespective of medication
use, but is only significantly tied to poorer aca-
demic self-competence for those who are receiving
medication. Among middle-SES children, diagno-
sis is tied to poorer outcomes on all three measures
irrespective of medication receipt. Specifically,
both “diagnosed and medicated” and “diagnosed
and unmedicated” upper-SES children respectively
exhibit .46 points (.67 SD) and .30 points (.43 SD)
significantly poorer positive approaches to learning
in fifth grade than their undiagnosed counterparts
(model 1). They also respectively exhibit .18 points
(.32 SD) and .38 points (.67 SD) significantly
poorer externalizing problems in fifth grade (model
4). Similarly, both “diagnosed and medicated” and
“diagnosed and unmedicated” middle-SES children
respectively exhibit .31 points (.41 SD) and .42 points
(.56 SD) significantly poorer positive approaches to
learning (model 2) and .17 points (.26 SD) and .20
(.31 SD) significantly worse externalizing problems
than their undiagnosed matches (model 5).
When it comes to perceived self-competence,
both “diagnosed and medicated” and “diagnosed
and unmedicated” middle-SES children again score
significantly worse than their undiagnosed matches,
by .22 points (.28 SD) and .36 points (.45 SD),
respectively (model 8). However, among upper-
SES children, only those who are diagnosed and
medicated score significantly worse than their undi-
agnosed matches, by .41 points (.53 SD) (model 7).
For diagnosed upper-SES children not receiving
medication, the relationship between diagnosis and
perceived self-competence is directionally similar
and statistically non-significantly smaller (–.24
points or .31 SD) compared to upper-SES children
receiving medication (–.41 points) (model 7). But
this .24 points poorer reported self-competence is
not itself statistically significantly different from 0.
Likewise, none of the other differences between
“diagnosed and medicated” and “diagnosed and
unmedicated” children are significantly different
within social class groups either.2
By contrast, for children in lower-SES families,
ADHD diagnosis is not significantly tied to any of
the outcome variables. That is, diagnosed lower-
SES children have statistically indistinguishable
levels of the fifth grade outcomes as their undiag-
nosed counterparts (Models 3, 6, and 9 of Table 2).
Substantively, the estimated effects of diagnosis are
at least 50% to 66% smaller for lower-SES com-
pared to middle- and/or upper-SES children. On
perceived self-competence and positive approaches
to learning, differences are statistically significantly
smaller among lower-SES compared to both middle-
SES and upper-SES children (model 3 vs. 1 and 2,
and model 9 vs. 7 and 8 of Table 2). On future
teacher-rated externalizing problems, the differ-
ences between lower SES and middle SES or upper
SES are not statistically significant but follow in the
expected direction (model 6 vs. 4 and 5).
Medication receipt does not further moderate
differences in the marginal effects of diagnosis for

146
Table 3.  Average Marginal Relationships between an ADHD Diagnosis and Future Social and Academic Behaviors and Child’s Perceived Self-competence, by
Family Social Class and Medication Treatment Status (N = 7,330).
Positive Approaches to Learning
(Teacher Report) – 5th Grade
Externalizing Behavior Problems
(Teacher Report) – 5th Grade
Perceived Self-competence (Child Report) –
5th Grade

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)

Upper
SES
(N = 1,850)
Middle SES
(N = 3,630)
Lower
SES
(N = 1,850)
Upper
SES
(N = 1,850)
Middle
SES
(N = 3,630)
Lower
SES
(N = 1,850)
Upper
SES
(N = 1,850)
Middle SES
(N = 3,630)
Lower
SES
(N = 1,850)
Diagnosed with
ADD/ADHD,
receiving
medication
–.46***a
–.31***
–.18a
.18*
.17*
.04
–.41**a
–.22**
–.04a

(.10)
(.08)
(.10)
(.08)
(.08)
(.04)
(.15)
(.07)
(.12)
Diagnosed with
ADD/ADHD,
not receiving
medication
–.30***
–.42***
–.06
.38***a
.20**
.11a
–.24
–.36**b
.16b

(.04)
(.11)
(.20)
(.08)
(.07)
(.09)
(.17)
(.11)
(.17)
Source: Early Childhood Longitudinal Study-Kindergarten Cohort, 1998–1999.
Note: Displaying propensity score matching estimates of the “average marginal effect” of ADHD diagnosis with subsequent medication treatment/non-treatment (each compared to
undiagnosed children) with coarsened exact matching to preprocess the data (see text). Standard errors in parentheses. ADD = attention deficit disorder; ADHD = attention-deficit/
hyperactivity disorder, SES = socioeconomic status.
aSignificant difference between lower SES and upper SES at p < .05.
bSignificant difference between lower SES and middle SES at p < .05.
cSignificant difference between middle SES and upper SES at p < .05.
None of the within-model (i.e., internal moderation) differences by medication treatment/non-treatment status reach statistical significance at p < .05.
*p < .05, **p < .01, ***p < .001 (two-tailed t-tests).

Owens
147
lower-SES children (Table 3). The magnitudes of
the differences in outcomes between “diagnosed
and medicated” and undiagnosed lower-SES chil-
dren are likewise much smaller than the analogous
differences for middle- and upper-class children
and, in fact, are statistically significantly smaller on
future positive approaches to learning and per-
ceived self-competence compared to upper-SES
children (model 3 vs. 1 and model 9 vs. 7 of
Table 3). Without medication receipt, “diagnosed
and unmedicated” children from lower-SES back-
grounds report significantly better future perceived
self-competence than middle-SES children (.16 vs.
–.36) and exhibit significantly better future teacher-
rated school externalizing problems than upper-
SES children (.11 vs. .38), each relative to their
undiagnosed counterparts. Between “diagnosed and
unmedicated” lower-SES vs. higher-SES children,
however, neither differences in self-competence
(.16 vs. –.24) nor positive approaches to learning
(e.g., –.06 vs. –.42) differ significantly, though each
follows the expected pattern. This may be due to
cell size limitations for “diagnosed and unmedi-
cated” children, per endnote 3. Together, overall
results indicate that diagnosed and medicated
upper-SES and middle-SES children—but not
diagnosed and medicated lower-SES children—
experience significantly worse future outcomes,
each relative to their otherwise comparable undiag-
nosed counterparts.
The Overall Winners and Losers: An
Examination of Predicted Scores
Figure 3 displays predicted outcome scores for
diagnosed and undiagnosed children by family
social class and medication treatment status. For
undiagnosed children on all three outcomes (panels
a through c), there is a social class gradient that most
advantages the upper SES (white bars), followed by
middle SES (grey bars), then lower SES (black
bars).
The most important finding from Figure 3 is
that, on all three outcomes, “diagnosed and medi-
cated” upper-SES and middle-SES children fare
comparably to undiagnosed lower-SES children.
“Diagnosed and medicated” upper- and middle-
SES children also fare slightly worse than their
“diagnosed and unmedicated” counterparts on
future positive approaches to learning and per-
ceived self-competence. The opposite is true for
future externalizing problems.
That “diagnosed and medicated” middle- and
upper-SES children fare comparably to undiagnosed
lower-SES children is surprising. One interpretation is
that medication more effectively improves the hyper-
activity/impulsivity that largely comprises externaliz-
ing problems than it does the inattention that partly
constitutes approaches to learning. Alternatively, med-
ication may have larger negative side effects for atten-
tion and concentration than hyperactivity, as is
indicated for depression medications.
A third possibility is that medications may effec-
tively act on the nervous system to improve both
inattention
and
hyperactivity/impulsivity,
as
designed, but that there may be an additional psy-
chological mechanism in addition to the biologic
ones. Even if fully effective at their stated aim,
medications are not designed to interrupt the psy-
chological effects of feeling “marked,” which may
have their own independent effects on children’s
perceived self-competence and school behaviors.
Medication may even slightly increase a child’s
awareness of being “marked,” or affect the way she
or he is treated by others.
Finally, on future perceived self-competence
(panel c), “diagnosed and unmedicated” lower-SES
children are predicted to fare as well as undiag-
nosed upper-SES children and slightly higher than
undiagnosed middle-SES children. For these lower-
SES children, diagnosis may provide a “legitimate”
(i.e., medical) explanation for the child’s difficul-
ties and ease some of the burden posed by other
structural barriers to school success. The lack of
medication treatment may diminish any effects of
stigma experienced by the child or family. Together,
the result may be the high subjective levels of per-
ceived self-competence observed.
Robustness Checks
The lower perceived self-competence and poorer
school-related behaviors of “diagnosed and medi-
cated” children from middle-SES and upper-
SES backgrounds appears robust and systematic.
Nonetheless, I additionally examine whether results
are driven by differential selection into: evaluation,
diagnosis by age, medication treatment, schools,
and remission of symptoms between the third and
fifth grades. Results discussed in the Online
Appendix and displayed in Appendix Tables A.4
through A.8 lend confidence that selection along
these dimensions does not drive results. Moreover,
estimates would need to be biased by more than
57% in order to invalidate findings.
For example, if the lower medication rate among
diagnosed lower-SES than higher-SES children reflects
under-medication rather than less severe clinical

148
Journal of Health and Social Behavior 61(2)
symptoms (for example due to financial barriers or lack
of insurance coverage), “diagnosed and unmedicated”
lower-SES children actually may be more similar to
“diagnosed and medicated” lower-SES children than is
the case among higher-SES children. This similarity
could lead to an underestimate of the moderating effect
Upper SES (N=1,850)
Middle SES (N=3,630)
Lower SES (N=1,850)
0
0.5
1
1.5
2
2.5
3
Undiagnosed
Diagnosed, Receiving
Medicaon
Diagnosed, Not Receiving
Medicaon
Predicted Perceived Self-Competence
Score
 Perceived Self-Competence (Child Report) - 5th Grade
0
0.5
1
1.5
2
2.5
3
Undiagnosed
Diagnosed, Receiving
Medicaon
Diagnosed, Not Receiving
Medicaon
Predicted Externalizing Behavior
Problems Score
Externalizing Behavior Problems (Teacher Report)-5th Gr.
(Higher is
r
e
h
gi
H
(
)r
e
tt
e
b
 is worse)
(Higher is beer)
0
0.5
1
1.5
2
2.5
3
Undiagnosed
Diagnosed, Receiving
Medicaon
Diagnosed, Not Receiving
Medicaon
Predicted Posive Approaches to
Learning Score
 Posive Approaches to Learning (Teacher Report) - 5th Gr.
B
A
C
Figure 3.  Predicted Future School Behaviors and Perceived Self-competence Between ‘Undiagnosed,’
‘Diagnosed and Medicated,’ and ‘Diagnosed and Unmedicated’ Children, by Family Social Class.

Owens
149
of medication status among lower-SES children.
However, I calculate that the difference between “diag-
nosed and unmedicated” and “diagnosed and medi-
cated” lower-SES children would have to be at a
minimum 2.05 times (105%) greater (for perceived
self-competence) and 3.67 times (267%) greater (for
positive approaches to learning) for there to be a signifi-
cant difference between “diagnosed and unmedicated”
and “diagnosed and medicated” lower-SES children at
p < .05 (two-sided t-test).
I also investigate whether results are driven by
boys or external visibility of the diagnosis by teach-
ers/peers (rather than internalized stigma by the
child). Results discussed in the Online Appendix
are inconsistent with both claims.
Discussion
This study shifts our understanding of the relation-
ship between social class privilege, including the
ability of high-SES parents to activate the types of
social capital rewarded by schools, and children’s
well-being and educational outcomes. Using the
case of childhood ADHD diagnoses, this study
offers a conceptual framework for disentangling the
conditions under which the interrelated social, psy-
chological, and medical factors associated with a
childhood mental health diagnosis can trigger posi-
tive, neutral, or negative effects on children’s later
well-being and school behaviors. The use of match-
ing strategies helps to disentangle the effects of high
levels of behavior problems from those of the diag-
nosis itself or the social class factors that may drive
differential diagnosis.
This study uncovers three major findings. First,
ADHD diagnosis is tied to worse approaches to
learning, more behavior problems, and poorer
academic self-competence in fifth grade, but only
for children in upper- and middle-SES families.
Second, for children in low-SES families, ADHD
diagnosis is not significantly tied to any of the out-
comes considered here. Third, when differentiating
between diagnosed upper-SES children who are
versus are not receiving medication following diag-
nosis, ADHD diagnosis is significantly tied to
poorer learning approaches and greater externaliz-
ing problems regardless of medication receipt. By
contrast, ADHD diagnosis is only significantly tied
to academic self-competence for those upper-SES
children who are receiving medication.
When it comes to the relationship between
social class privilege and children’s well-being,
work in the tradition of Lareau (1989) may be taken
to assume that high-SES parents’ activation of
social capital and other resources for school inter-
vention is predominantly beneficial to their chil-
dren. However, the present findings indicate that
there is also a need to consider that there could be a
negative side to the consequences of this social cap-
ital and privilege in schools through children’s
increased susceptibility to negative social labeling.
The ability of parents to convert social capital may
be highly influenced by the ways in which broader
social structures (e.g., schools) differentiate and
stigmatize disability differently among high-SES
and low-SES families. For example, the blame
assigned to parents and children with disability may
be heightened in the high-SES schools attended by
many high-SES children.
The findings also contribute to our understand-
ing of social labeling processes. While key litera-
tures in sociology of education and medical
sociology have highlighted potential negatives
associated with labeling (Link et  al. 1989; Rist
1977), the present study extends this work by show-
ing how parental social capital (e.g., school and
medical intervention) can have negative conse-
quences for children’s well-being.
Although this study cannot be certain of the
mechanisms, one interpretation is that in the high-
pressure environments of many middle- and upper-
SES children, negative stereotypes applied to
members of advantaged groups may elicit even
greater self-stigma, perhaps due to the greater aca-
demic demands and expectations in their local con-
texts.3 This internalization can produce poorer
subjective ratings of self-competence, as observed.
Even subtle negative feedback from teachers and
peers may exacerbate this effect. As with internalized
stigma, lower perceived self-competence may result
in a self-fulfilling prophecy that leads to poorer
school-related behaviors, instigating a reciprocal
process (Mueller and Abrutyn 2016). Crucially, this
interpretation would suggest that ADHD diagnoses
would continue to have a large negative effect even
if diagnoses become more common and normalized
in high-SES communities.
Finally, this study shows that the observed
poorer outcomes of diagnosed middle- and upper-
SES children can manifest at an earlier age than
previously documented. Here, patterns of negative
effects of diagnosis appear similar for both middle-
and upper-SES children, and quite distinct from
lower-SES children, consistent with the patterns of
“concerted cultivation” Lareau (2003) observed
among middle- and upper-SES parents. Altogether,
for medical sociology, education, and social stratifi-
cation scholars, these findings add nuance to a large

150
Journal of Health and Social Behavior 61(2)
body of evidence finding that social stigmas have
greater effects among disadvantaged populations,
such as the mark of a criminal record and hiring
(Pager 2003) or a school suspension (Owens and
McLanahan Forthcoming).
At first glance, one might think that the results
could be an artifact of class-specific reference group
effects: perhaps middle- and upper-SES children
have better-behaved classmates than low-SES chil-
dren such that slight deviations in behavior may lead
to artificially inflated teacher and child ratings of
problem behaviors. However, these results appear
robust and systematic even among teachers who
similarly rate the average behavior of students in
their classrooms and when using fifth grade teacher
ratings or an average across elementary school.
Additionally, descriptive patterns suggest that
results are not driven by differences in the schools
attended by diagnosed and undiagnosed children:
mean levels of the outcomes are similar across
diagnosed and undiagnosed children in the same
schools. To help address the possibility that ADHD-
related behavior problems may have worsened
leading up to diagnosis, pre-diagnosis behavior
measures come from the wave just prior to diagno-
sis, offering a substantial advantage over measuring
pre-diagnosis behaviors at a static time point, such
as kindergarten entry.
This study also has a number of limitations and
possible extensions. The ADHD-related behav-
ioral scales (and co-morbid internalizing and
oppositional defiant behavioral scales) used here
measure frequency of behaviors, not other rele-
vant factors like their intensity or duration, and
also do not perfectly map on to DSM criteria.
These unobserved clinical factors, if correlated
with both diagnosis and outcomes, could lead to
omitted variables bias. Given the null results
among low-SES children, there would have to be
differential omitted variables between diagnosed
and undiagnosed children by family social class in
order to yield biased estimates among the middle-
and upper-SES children for whom negative effects
are estimated. Additionally, the sample balance
achieved on key concerted cultivation measures,
early parental education expectations, and child
cognitive skills, helps protect against such class-
specific omitted variables. Nonetheless, measure-
ment error in pre-diagnosis behavioral problems
may be larger for children from lower-SES than
higher-SES families; adjusting for parenting styles
may not entirely address this issue. Depending on
the outcome, estimated diagnostic effects would
have had to be 9% to 566% larger among lower-
SES children in order for there to have been
statistically significant diagnostic effects among
lower-SES children. Given this range, an alterna-
tive interpretation of results is that, rather than
self-stigma concentrated among higher-SES chil-
dren, the diagnosis and clinical care of ADHD
among lower-SES children may be less consistent
and reflective of actual symptoms.
These data also lack a direct measure of stigma
and/or internalized shame, as mentioned above.
However, prior work finds that children diagnosed
with ADHD experience substantial labeling and
stigma (Pescosolido et al. 2008). This prior work
suggests that stigma may be a reasonable mecha-
nism underlying observed relationships. Future
experimental or qualitative work should directly
examine this possible mechanism. Finally, this
study is limited by the small sample of unmedicated
diagnosed children (100 children total).
This study also carries practical implications. It
might caution against parents, educators, and medi-
cal providers considering an ADHD diagnosis for
middle- and upper-SES children at the first signs of
behavioral difficulties. For example, prior research
points to potential negative diagnostic effects on
later school behaviors and academic achievement
among children who had only mild pre-diagnosis
behavioral problems (Owens 2020; Owens and
Jackson 2017). Further research is needed to under-
stand diagnostic effects on academic outcomes
among high-SES children, but these studies together
suggest that both potential positive and potential
negative consequences of diagnosis should be con-
sidered. Findings should not dissuade ADHD diag-
nosis for high-SES children who have a clear-cut
need for diagnosis.
Acknowledgments
I thank three anonymous reviewers, Editor Amy Burdette,
Jordan Conwell, Laura Doering, Heide Jackson, Margot
Jackson, Susanna Loeb, John Logan, John Mullahy,
Chandra Muller, Zhenchao Qian, Paul Rathouz, Emily
Rauscher, Stephanie Robert, Kelley Smith, and Michael
White for invaluable feedback during the preparation of
this manuscript. Earlier versions of this manuscript were
presented at the Robert Wood Johnson Foundation Health
and Society Scholars Program Annual Meeting in 2015,
the University of Wisconsin–Madison in 2015, the
American Sociological Association Annual Meeting in
2019, and the Population Association of America Annual
Meeting in 2019.
Funding
The author disclosed receipt of the following financial
support for the research, authorship, and/or publication of

Owens
151
this article: The author is grateful for support from the
Brown Population Studies and Training Center, which
receives funding from the NICHD (P2CHD041020);
Brown University; the National Academy of Education/
Spencer Foundation Postdoctoral Fellowship Program;
the Robert Wood Johnson Foundation Health and Society
Scholars Program; and the University of Wisconsin–Madison.
Supplemental Material
Supplemental material for this article is available online.
Notes
1.
The data provided only averaged scales in 5th grade,
preventing standardization of summed indexes.
Regardless, standardizing was less preferred because
it unreasonably assumes that teachers follow a stable
logic across contexts when rating behaviors.
2.
The lack of significant internal moderation by
medication status may reflect small sample sizes
and large standard errors among “diagnosed and
unmedicated” children. Replication analyses for
the two available outcomes using the more recent
ECLS-K:2010–11, which includes more “diagnosed
and unmedicated” children (n = 70 for upper-SES
and n = 80 for lower-SES children, up from n = 20
and n = 30, respectively, in the ECLS-K: 1998–99
cohort), reveal descriptively (and in some cases,
significantly) larger negative effects of diagnosis
for middle- and upper-SES children and “diagnosed
and unmedicated” lower-SES children, but not for
“diagnosed and medicated” lower-SES children.
3.
While stereotype threat faced by students of color or
of women in STEM may seem like counter-exam-
ples, these negative stereotypic “marks” only target
structurally disadvantaged groups.
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Author Biography
Jayanti Owens is the Mary Tefft and John Hazen White,
Sr. Assistant Professor of Sociology and International and
Public Affairs at Brown University. Her research interests
include sociology of education, social stratification, social
demography, organizations, and population health.

    Scientific Research Findings:

Answer:
• ADHD diagnosis is tied to worse approaches to learning, more behavior problems, and poorer academic self-competence in fifth grade but only for children in upper- and middle-SES families.
• For children in low-SES families, ADHD diagnosis is not significantly tied to any of the outcomes considered here.
• For upper-SES children, ADHD diagnosis is significantly tied to poorer learning approaches and greater externalizing problems regardless of whether they are receiving medication for ADHD. By contrast, ADHD diagnosis is only tied to academic self- competence for those upper-SES children who are receiving medication.
• Across all three outcomes (child-perceived self-competence and teacher-rated positive approaches to learning and externalizing problems), diagnosed and medicated upper-SES and middle-SES children fare comparably to undiagnosed lower-SES children.