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

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
    https://doi.org/10.1177/0022146520979664
Journal of Health and Social Behavior
2021, Vol. 62(1) 2–18
© American Sociological Association 2021
DOI: 10.1177/0022146520979664
jhsb.sagepub.com
Article
The connections between education and health are
one of the most widely studied and empirically robust
in the field of medical sociology (Adler et al. 1994;
Conti, Heckman, and Urzua 2010). Education has
been associated with assorted morbidities, mortality
risk, disability, general health, mental health, and an
array of health-related behaviors linked to various
health outcomes (Adams 2002; Arendt 2005; Cutler
and Lleras-Muney 2010; Lleras-Muney 2005; Turner
and Lloyd 1999). One important question about the
links between education and health that remains
unanswered, however, is whether these links are
robust for individuals who pursued schooling at a
stage of the life course when education has norma-
tively been completed (as reflected in formal defini-
tions of nontraditional students, as well as
institutional expectations and structures): prior to
having children (Macmillan 2005; Marini1984). This
question of whether “out-of-sequence” education can
promote greater health is particularly salient for
women with children, who (based on estimates from
2015 to 2016), represent over 15% of all students
enrolled in college and 70% of all student parents
(Cruse et al. 2019).
This question is also relevant to current public
health policy given that scholars from across disci-
plines have argued that one of the most powerful
policy levers for reducing population-level health
disparities is promoting higher education (Frieden
2010; Hahn and Truman 2015). This argument has
been especially directed toward lower-educated
979664 HSBXXX10.1177/0022146520979664 Journal of Health and Social BehaviorAugustine
research-article2021
1University of South Carolina, Columbia, SC, USA
Corresponding Author:
Jennifer March Augustine, Department of Sociology,
University of South Carolina, 911 Pickens St., Columbia,
SC 29208, USA.
Email: augustjm@mailbox.sc.edu
Mothers’ Out-of-Sequence
Postsecondary Education
and Their Health and Health
Behaviors
Jennifer March Augustine1
Abstract
Ample research suggests that the links between higher education and heath are robust and growing in
strength. This research, however, tends to assume education was completed prior to assuming other adult
roles. Importantly, the life course framework raises the question of whether “out-of-sequence” college
completion conveys similar health returns. I investigate this question among a population for whom out-
of-sequence schooling has grown more common: lower-educated mothers. This focus is also important
given the growing education gap in women’s health and the links between maternal and child health. Data
come from the Fragile Families and Child Wellbeing Study (N = 4,898). Analyses involve random intercept
and fixed effects models and diverse health measures. Findings suggest that postsecondary education
does not improve mother’s health, except for reduced smoking among mothers with high school degrees
or less that earned bachelor’s degrees. These findings inform health policy debates and theories linking
education to health.
Keywords
maternal education, maternal health, stratification

Augustine
3
women, who have recently experienced greater risks
to their health than less educated men (Montez and
Zajacova 2013) and whose lower levels of education
and health have been implicated in the low levels of
health and development of their children and inter-
generational transmission of inequality more
broadly (Gardner, Brooks-Gunn, and Chase-
Lansdale 2017; Kaushal 2014; Turney 2011). It
remains unknown, however, whether promoting
mothers’ school reentry can remediate these issues.
This uncertainty is further underscored by a key
tenet of the life course perspective (Elder 1984),
which suggests that out-of-sequence life course pro-
gressions can create challenges and conditions that
undermine the efficacy of individuals in their roles
and statuses (Pearlin et  al. 2005) and reduce the
rewards and resources that such roles and statues are
supposed to generate (Furstenberg 2005; also see
Hogan 1978 for a key theoretical contribution).
Thus, this question also raises the broader theoreti-
cal issue of whether out-of-sequence schooling cre-
ates challenges that diminish the well-documented
returns to higher education (e.g., better labor market
opportunities, wider social networks, increased cog-
nitive skills; Mirowsky and Ross 2003; Zajacova
and Lawrence 2018) for women with children.
To investigate these substantive, theoretical,
and policy-relevant issues, I draw on data from a
national sample of largely lower-educated mothers
(i.e., without postsecondary degrees at the baseline
data collection) participating in the Fragile
Families and Child Wellbeing Study (FFCW). Not
only does the FFCW focus on this study’s popula-
tion of interest, it includes repeated measures of
maternal education and health subsequent to the
birth of child that allows for a dynamic investiga-
tion of the connections among these two life course
pathways. As part of this investigation, I attend to
issues of heterogeneity connected to the type of
postsecondary degrees mothers completed (e.g.,
vocational, associate’s, bachelor’s) and issues of
selection that might confound the results, and I
explore a range of maternal health outcomes that
have also been connected to children’s health
and development (Garbarski 2014; Hofhuis, de
Jongste, and Merkus 2003; Turney 2011). The
results of this investigation will provide greater
understanding of how sequencing education vis-à-
vis a key life course event, the birth of a child, is
associated with various dimensions of health,
extend research on the education–health gradient,
and inform policy debates aimed at promoting the
well-being of disadvantaged families.
Background
Education and the Health Gradient
In the United States, as in other parts of the world,
individuals with postsecondary levels of education
have a substantial health advantage. This health
advantage spans various health domains (e.g., phys-
ical health, mental health, health behaviors;
Mirowsky and Ross 2003), and has grown over the
past several decades (Meara, Richards, and Cutler
2008; Mirowsky and Ross 2008). The general
explanations for these patterns are that higher edu-
cation (1) promotes the accumulation of human
capital, which increases access to better paying and
less physically and emotionally demanding sectors
of the labor market; (2) enhances cognitive skills
(e.g., critical thinking) and capacities (e.g., self-
esteem, stress management), which improve one’s
ability to manage one’s health in both the short and
long terms; and (3) conveys socioeconomic advan-
tages that yield access to more supportive and stable
relationships, higher quality neighborhoods, and
better health care and buffers against exposure to
stressful environments and experiences (Link and
Phelan 1995; Mirowsky and Ross 2003; Zajacova
and Lawrence 2018).
Moreover, these effects of education on health
are considered to be causal (McFarland and Wagner
2015). Although some scholars have challenged
this causal interpretation of the education–health
gradient (Duke and Macmillan 2016; Lynch and
von Hippel 2016), the prevailing view of most
health scholars is that increases in one’s education
will result in better health outcomes. This general
notion has inspired many scholars to argue for pub-
lic health interventions that target the education of
traditionally disadvantaged groups (Frieden 2010;
Hahn and Truman 2015). Nevertheless, numerous
questions about the links between education and
health remain, including whether an increase in
mothers’ education—which would occur at a life
course stage in which schooling has traditionally
been completed (Marini 1984)—results in an
improvement in their health outcomes. There is a
strong theoretical and empirical basis for pursuing
this specific question.
Changes in the Structure of the Life
Course
In the past, the life course was more likely to follow
a “normative” sequence of role transitions (Marini
1984) and experiences. Prior research linking higher

4
Journal of Health and Social Behavior 62(1)
education to health, which tended to view education
as a static status, reflected this understanding.
Today, however, the life course has grown more
“destandardized” (MacMillan 2005). This phenom-
enon is especially true for women (vs. men; Braboy
Jackson and Berkowitz 2005; Brückner and Mayer
2005), whose trajectories into and through adult-
hood reflect greater diversity in the sequencing of
transitions into (and out of) various social roles,
including the student and parent roles (Augustine
2016). Indeed, as mentioned previously, among the
22% of U.S. college students with dependents
enrolled in 2015 to 2016, 70% were women (Cruse
et  al. 2019). In the aggregate, nearly 2.7 million
mothers were enrolled in college in 2015 to 2016
(Cruse et. al. 2019).
Several factors explain the large number of
mothers pursuing postsecondary schooling. The
first are demographic factors. Increases in mothers’
labor force participation, decreases in men’s labor
force participation, and changes in family structure,
including the rise in nonmarital fertility and greater
union instability, have brought about a rise in the
share of women who are unmarried parents and the
sole or primary breadwinners (Bianchi, Robinson,
and Milkie 2006). A second set of economic factors
includes greater instability in the labor market,
stagnating wages for lower-status work, and the
relative lack of adequate paying jobs for lower-
educated women (compared to men, who are more
likely to work in trades or manual labor). Thus,
greater education is also needed for women to
access stable work and adequate wages (Kalleberg
2011). Third, there has been an expansion of the
higher education system—which includes a rise in
for-profit schools, which have targeted their recruit-
ment efforts at lower-income mothers (Cottom
2017)—as well as federal student aid programs
over the past few decades. These factors have made
it easier for adults with fewer resources (e.g., moth-
ers with lower levels of education) to return to
school (Rosenbaum 2001). Outside of these large-
scale factors, disadvantaged mothers, who are often
their children’s sole caregiver, also report returning
to school in a renewed effort to improve their chil-
dren’s opportunities for mobility (Edin and Kefalas
2011; Katz 2013).
The Life Course Paradigm and Key
Conceptual Principles
Despite trends in mothers’ schooling, scholars have yet
to investigate whether mothers’ postnatal college
completion can result in an improvement in their health.
This lack of research is surprising given how lower-
educated mothers have become a primary focus of two-
generation policies and interventions, which are aimed
at simultaneously improving the well-being of parents
and children (Gardner et al. 2017; Kaushal 2014). The
promise of these interventions reflects the robust inter-
disciplinary body of research linking one’s health and
well-being with one’s education (Hout 2012) as well as
a key tenet of the life course: that of interlocking trajec-
tories (Elder 1984). This tenet highlights the interde-
pendencies among parent’s and children’s experiences
(Cooksey, Menaghan, and Jekielek, 1997; Yuan 2008)
and the potential for increases in mothers’ education to
improve the health and development of their children.
The concept of interlocking trajectories also highlights
the potential for, and importance of, examining whether
an increase in a mother’s education can improve her
own health outcomes.
At the same time, an additional life course
tenet suggests an alternate possibility that runs
counter to much thinking about the links between
higher education and health (Frieden 2010; Hahn
and Truman 2015). The tenet of role sequencing
highlights how “the demands and conditions
encountered in out-of-sequence or off-time transi-
tions interfere with the achievement and enact-
ment of other roles and statuses” (Pearlin et  al.
2005:212). This disruption has been called a life
course penalty. Importantly, a break in schooling
associated with the transition to parenthood or
need to work is argued to carry among the greatest
long-term life course penalties. The tenet of role
sequencing, therefore, suggests that mothers who
attain higher education after having a child may
have difficulty realizing the returns to their degree
and experiencing an improvement in their health.
There is also substantive research to support this
expectation.
Research on Out-of-Sequence Schooling
and Maternal Health Outcomes
The most direct evidence comes from a handful of
studies in medical sociology that found that nonnor-
mative life course sequences are linked to worse
health outcomes than traditional sequences. For
example, one study found that adults who married
before completing college had a 50% higher proba-
bility of being obese than those who married after
completing college (Miech et al. 2015). Two other
studies exploring a range of life course trajectories
found that nonnormative trajectories were associated

Augustine
5
with lower self-reported health, higher rates of
smoking and drinking (Barban 2013), and worse
mental health (Braboy Jackson 2004). A fourth study
found that the sequencing of motherhood prior to
marriage was associated with worse health at midlife
(Williams et al. 2011).
Research examining the mechanisms linking
education and health also indicate fewer health
returns to out-of-sequence schooling. Research in
the human capital tradition indicates that postsec-
ondary educational attainment in adulthood is
associated with fewer labor market opportunities
and lower levels of wages compared to postsec-
ondary education completed earlier in the life
course (Elman and O’Rand 2004). Research on
time use and role strain finds that college student
mothers have less time to devote to schooling than
their peers without dependents and greater stress
and fatigue than similar mothers who were not in
school (Augustine, Prickett, and Negraia 2018).
Research on social networks and support suggests
that student mothers have less support from family
and friends than traditional students, less social
engagement with their college peers, and less mal-
leable social networks because many of their prin-
ciple relationships (e.g., their child’s parent,
neighbors) have already been established (Carney-
Crompton and Tan 2002; Laumann 1973). Lastly,
research in education suggests that student mothers
may be more oriented toward receiving credentials
necessary for specific jobs than the experiential
aspects of schooling (an emphasis of traditional stu-
dents; McDonough 1997) or may attend lower-qual-
ity educational institutions (Seefeldt 2016). Such
factors may mitigate the nonpecuniary returns to
education, for example, its impact on lifestyle
choices related to health (Cockerham 2005) or
health management skills.
Finally, there is indirect evidence from studies
examining the link between increases in mothers’ edu-
cation and their children’s outcomes (e.g., cognitive
skills, behavior), which find no significant positive
associations, irrespective of potentially moderating
factors, such as degree type or the child’s age
(Augustine and Negraia 2018; Gardner, Martin, and
Petitclerc 2019; Harding 2015). These findings sup-
port the suggestion that the health returns to maternal
education—which largely include the same set of
human capital, socioeconomic, and social psychologi-
cal factors shown to promote children’s development
(Augustine and Negraia 2018; for a detailed concep-
tual framework, see Harding, Morris, and Hughes
2015)—may not accrue to mothers who pursed their
education after having children.
Current Study
Given such factors, this study tests the hypothesis
that increased maternal education will not result in a
significant improvement to mothers’ health. In
doing so, I examine four maternal health measures:
self-reported health, global mental health, parenting
stress, and smoking. These measures represent an
array of health domains, follow an educational gra-
dient (Cooper et al. 2009; Cutler and Lleras-Muney
2010; Mirowsky and Ross 2003), and have implica-
tions for public health as well as the health and well-
being of children and the two-generation policy
framework more broadly (Beck et al. 2010; Kaushal
2014; Turney 2011).
More specifically, self-rated health provides a
holistic way of capturing population health dispari-
ties that encapsulates a broad range of health-related
factors, from disease to lifestyle (Parrish 2010).
Global mental health provides a broad assessment of
psychological functioning that reflects a variety of
psychosocial mechanisms linked to education, such
as social isolation or nutrition (Mirowsky and Ross
2003). Parenting stress captures a dimension of
stress—how mothers manage and respond to daily
parenting challenges in concert with other con-
straints, such as work and income (Cooper et  al.
2009)—that has been associated with psychological
distress, depression, and mothers’ physical health
(Crnic, Gaze, and Hoffman 2005; Eisenhower,
Baker, and Blatcher 2009; Gershoff et  al. 2007).
Finally, smoking represents an important maternal
health behavior, public health indicator, and poor
health risk factor (Bandeira et  al. 2011; Hofhuis
et al. 2003).1
To test these associations, I adhere to the life
course principle of interlocking trajectories by
examining the linkages between maternal education
and maternal health dynamically, drawing on six
panels of data that span 15 years after the birth of a
child and random intercept models. I also examine
patterns by institution type (i.e., vocational, associ-
ate’s, bachelor’s) and the robustness of any signifi-
cant findings to unobserved heterogeneity using a
fixed effects modeling approach.
Data and Methods
Data
Data came from the Fragile Families and Child
Wellbeing Study: a national birth cohort study of 4,898
children born between 1998 and 2000 in 20 U.S. cities.
Mothers were recruited from hospitals immediately
after the study child’s birth and interviewed about

6
Journal of Health and Social Behavior 62(1)
family background and demographic characteristics,
including highest level of schooling (for information
about the sample design, see Reichman et al. 2001).
Follow-up interviews conducted one (Wave 2), 3
(Wave 3), 5 (Wave 4), 9 (Wave 5), and 15 (Wave 6)
years after the birth using telephone and in-home sur-
veys provided data on maternal health factors and
mothers’ schooling since the last interview. The avail-
ability of repeated measurements of health and school-
ing across six data collections spanning 15 years make
the FFCW data particularly well suited to this study. A
second advantage is the FFCW largely sampled
women with lower levels of education at the baseline
wave (89% was without a bachelor’s degree) who
were more likely to pursue higher education subse-
quent to a birth. The FFCW sample is also regionally,
racially, and ethnically diverse, although not represen-
tative of the entire U.S. population. More information
about the sociodemographic characteristics of the
sample appear in Table 1 (presenting baseline/Wave 2
reports) and Appendix A in the online version of the
article (reports from each wave).
The analytic sample for the study was based on
the full sample of families enrolled in the FFCW
(N = 4,898), although modeling procedures
(explained in the following) limited the multivariate
analyses to mothers with at least one health report
(ns = 4,710 for models predicting overall health;
4,697 for parenting stress; 4,709 for global mental
health; and 4,689 for smoking). Missing data impu-
tation techniques (described in the following as
well) were used to retain mothers with missing
values on independent variables.
Variables
Maternal education.  Mothers’ reports of baseline
education were coded into one of four categories:
less than a high school degree, high school degree/
General Education Development (GED), some col-
lege, bachelor’s or higher. Unfortunately, the base-
line reports did not differentiate mothers with “some
college” from those with an associate’s degree. It
did distinguish mothers with a technical/trade
degree, although this group was small (6% of the
sample). Thus, mothers with technical/trade degrees
were included in the “some college group,” but I did
use this information in models that restrict the sam-
ple based on mothers’ baseline education (explained
in the following). At each follow-up wave, women
reported whether they had earned a higher educa-
tion degree among a variety of institution types
(e.g.,
technical
degree,
secretarial
school,
associate’s degree, etc.) since the last interview.
Using this information, I created three time-varying,
binary variables for completed a vocational pro-
gram, completed an associate’s degree, and com-
pleted a bachelor’s degree. Once mothers reported
degree completion, mothers were assigned a value
of 1 on that measure for all subsequent waves. This
coding technique captured a pooled estimate of the
association between additional education and health
that used all available data and provided an average
of mothers’ health before versus after completing
additional education. Mothers were also asked if
they had enrolled in school since the last interview,
but only where they had enrolled during Waves 2 to
5. I used this information to create a parallel set of
bivariate measures of enrolled in a vocational pro-
gram, enrolled in an associate’s program, and
enrolled in a bachelor’s degree program for ancil-
lary analyses exploring the impact of school enroll-
ment (restricted to Waves 2–5).
Maternal health.  Mothers’ health reflected four
domains. Overall health was based on a measure of
self-reported health in which respondents described
their health on a scale ranging from 1 (poor health)
to 5 (excellent health). Global mental health was
captured by mothers’ reports of whether they felt
sad, depressed, or anxious for a two-week period
over the past year (1 = yes, 0 = no). Unfortunately,
the FFCW did not include more nuanced measures
like depressive symptomatology based on the Cen-
ter for Epidemiological Studies–Depression Scale.
It did include an indicator of depression based on
the Composite International Diagnostic Indicator–
Short Form (CIDI–SF), but the CIDI–SF is a fairly
specific tool designed to capture one’s probability of
meeting clinical levels of depression. The global
indicator used here is seen as a less restrictive mea-
sure that taps a wider range of mental health strug-
gles. Parenting stress, which was based on the
average of mothers’ responses (on a scale of 1 to 4
tapping different levels of agreement) to four ques-
tions (e.g., I often feel tired, worn out, or exhausted
from raising a family), was a shortened version of
the original Parenting Stress Index (see Abidin
1983), as used in other national studies (e.g., the
Panel Study of Income Dynamics). Smoking was
based on self-reports of whether the mother smoked
in the last month (1 = yes, 0 = no). All four measures
were assessed at Waves 2 to 6. More details about
the univariate properties of these variables as well
as the time-varying education variables appear in
Table 2.

Augustine
7
Covariates.  To account for sources of confound-
ing among mothers’ education and health, I included
several covariates. The first set of factors (subse-
quently referred to as Set 1) included background
factors exogenous to mothers’ baseline education.
These included the mothers’ race-ethnic background
(dummy coded white, Black, Hispanic, and other),
age at first birth (continuous), current age (continu-
ous), her mother’s education (dummy coded as less
than high school, high school degree, more than high
school), immigrant status (1 = immigrant, 0 = native
born), whether she grew up with both parents in the
home (1 = yes, 0 = no), and whether the study child
was her first birth (1 = yes, 0 = no). The second set
(Set 2) included time-invariant factors taken from
the baseline or, when not available at baseline, Wave
2, potentially exogenous to mothers’ school reentry.
These included family structure (dummy coded
married, cohabiting, and single), family income-to-
needs (continuous), number of children in the home
Table 1.  Means/Frequencies of Background Characteristics and Selected Baseline Characteristics of
Mothers in the Fragile Families and Child Wellbeing Study Sample (N = 4,898).
Means/Frequencies
Demographic characteristics
  Maternal age
25.27 (6.04)
  Age at first birth
21.56 (5.24)
  Race
    White
21%
    Black
48%
    Hispanic
27%
    Other race
4%
  Mother immigrant status
17%
Background characteristics
  Grandmother education
    Less than high school
21%
    High school
54%
    More than high school
25%
  Mother lived with both parents
43%
Family/child factors
  Focal child mother’s first birth
39%
  Family structure
    Married
24%
    Single
40%
    Cohabiting
36%
  Child low birth weight
10%
Family resources
  Family income-to-needs
2.24 (2.41)
  Mother employment
    Not employed
14%
    Part-time work
32%
    Full-time work
46%
  Welfare receipt
36%
  Health insurance
71%
  Emergency child care
92%
  Can borrow $200
90%
  Owned home
34%
Note: Due to missing data, sample sizes range between 4,842 and 4,897 for all variables, with the exception of age
at first birth (N = 4,311), grandmother’s education (N = 3,993), and health insurance (N = 4,337), which were taken
from the Wave 2 reports in which 534 cases were not in the wave.

8
Journal of Health and Social Behavior 62(1)
(continuous), maternal employment status (dummy
coded not working, working part-time, and working
full-time), child low birth weight (0 = no, 1 = yes),
the family received public assistance (0 = no, 1 =
yes), the mother had insurance coverage (0 = no, 1 =
yes), the mother owned her home (0 = no, 1 = yes),
and two indicators of social support based on moth-
ers’ responses to whether (a) she had friends or fam-
ily available that she could borrow $200 from (0 =
no, 1 = yes) and (b) an emergency child care option
(0 = no, 1 = yes). A third set of factors (Set 3) included
time-varying factors that may be exogenous to moth-
ers’ additional education but also endogenous to it.
These factors were included to assess the robustness
of the links between mothers’ postsecondary degree
completion and health. They included the child’s
health (range = 1–5, 1 = poor, 5 = excellent); whether
the mother had a birth between waves (0 = no, 1 =
yes), coresided with a parent or in-law (0 = no, 1 =
yes), or moved since the previous interview (0 = no,
1 = yes); and time-varying versions of Set 2 variables
(except child low birth weight and home ownership,
which was a proxy for residential stability and cap-
tured by the measure of having recently moved).
Analysis Plan
The main analytic technique involved random inter-
cept models. These models took advantage of the
longitudinal data by incorporating all six waves,
accounted for the hierarchical structure of the data in
which individual observations are nested within
time, adjusted for nonindependence among the
repeated observations, allowed for the modeling of
time as a fixed effect (thereby capturing time trends),
modeled time-invariant key variables, like maternal
education at the baseline wave, and accounted for
random variability in mothers’ health (i.e., the ran-
dom intercept; Laird and Ware 1982). This model in
its unconditional form is expressed as follows:
Table 2.  Descriptive Statistics for Main Study Variables by Wave and Pooled across Waves, Fragile
Families and Child Wellbeing Study.
Mean/% (n)
Within
Between

Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
SD/%
SD/%
Health measures
  Self-rated health
3.77 (1.06)
3.75 (1.05)
3.64 (1.03)
3.55(1.04)
3.42 (1.05)
.61
.81
    n
4,362
4,225
4,131
3,512
3,144

  Global mental health
20%
25%
22%
22%
24%
46%
50%
    n
4,357
4,215
4,125
3,497
3,135

  Parenting stress
2.18 (.67)
2.25 (.67)
2.18 (.69)
2.03 (.69)
2.06 (.70)
.44
.54
    n
3,787
4,205
4,100
3,616
3,145

  Smoking
27%
24%
31%
30%
23%
72%
38%
    n
4,361
3,129
4,131
1,032
732

Wave-specific education
  Vocational degree
5%
5%
7%
13%
11%
31%
26%
    n
4,360
4,221
4,126
3,509
3,571

  Associate’s degree
1%
1%
2%
4%
6%
26%
10%
    n
4,360
4,221
4,126
3,509
3,571

  Bachelor’s degree
1%
1%
1%
2%
4%
26%
7%
    n
4,360
4,221
4,126
3,509
3,571

Cumulative education
  Vocational degree
5%
9%
15%
26%
33%
59%
26%
    n
4,360
4,221
4,126
3,509
3,571

  Associate’s degree
1%
2%
4%
8%
13%
48%
10%
    n
4,360
4,221
4,126
3,509
3,571

  Bachelor’s degree
1%
2%
3%
6%
9%
47%
7%
    n
4,360
4,221
4,126
3,509
3,571

Note: Within percentages describes among mothers who ever reported a 1 on a variable (e.g., low mental health) the
percentage of reports in which they reported a value of 1. The between percentages captures the percentage of all
sample reports that were a 1 on that measure.

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Yij =
0i
i
1i
j
ij
+ U
+
wave +
β
β
ε
(
)
(
)
β0 represents the intercept for respondent i at Wave
2, and β1 is the coefficient for study wave j ranging
from 2 to 6. The β coefficients reflect the fixed
effects, or parameters of central values (i.e., popula-
tion averages) observed for each mother. Ui repre-
sents the random intercept, which captures
individual-level deviations from the central values.
Random effects are assumed to be normally distrib-
uted, with a mean of zero (Ghisletta et al. 2015). The
εij term is the random within-person error of predic-
tion for respondent i at time j. This term is assumed
to be reflective of the random effects, which are
assumed to be independent of the vector of covari-
ates (Singer and Willet 2003).
As an initial step, I explored variability in mater-
nal health across waves. First, for all four outcomes,
I calculated the within- and between-person vari-
ance components. This step revealed that variances
for overall health and parenting stress were similar
in magnitude within mothers as between mothers
(results in Table 2; estimates reflected standard
deviations). For global mental health, the within
estimate suggested that of the mothers who reported
a mental health struggle, mothers reported a mental
health struggle in 46% of their reports. Among
mothers who smoked, this number was 72%.
Second, transition probabilities provided a different
perspective on these two categorical outcomes.
They revealed that 22% of women who smoked at
one wave did not smoke at a later wave, and 52% of
mothers who reported a mental health struggle the
past year did not do so in a later wave. These mod-
els indicated the potential for health to change
across time and provided the foundation for the
subsequent modeling steps.
These steps involved (1) providing evidence of
the link between a static measure of maternal educa-
tion (baseline education) and maternal health as
found in previous research and (2) building on this
model by examining whether, net of this association,
a postsecondary degree completed after the birth of
the focal child is associated with more optimal
health outcomes. Thus, I first extended the uncondi-
tional model to incorporate parameters for baseline
maternal education (β3 Educi) and X′i, which is a
vector of the Set 1 background factors (i.e., those
that may select mothers into different baseline levels
of education). This step laid the foundation for the
main analysis, which incorporated the time-varying
measures for increased education (i.e., β4 Additional
Educationij).
For this step, I estimated a different model for each
additional education measure (e.g., vocational, associ-
ate’s), predicting the four health outcomes, for a total
of 12 models. Model coefficients for the additional
education variables are interpreted as the difference in
the average of mothers’ health across all waves in
which mothers had completed an additional degree
compared to mothers’ health in waves for which no
additional education was reported. To ensure I was
capturing an educational increase (e.g., some mothers
with a vocational degree completed a second voca-
tional degree; see Table 3), I made restrictions based
on the mothers’ baseline education. For models esti-
mating the “effect” of a postnatal vocational degree,
the sample was restricted to mothers with a high
school degree or less at the baseline wave. Models
including associate’s degree completion were restricted
to mothers with a technical/trade degree or less. I
made this restriction, rather restricting the sample to
mothers’ with some college, because this group
included mothers with associate’s degrees at baseline,
as explained previously. I did examine models using
this alternative restriction, however. Models measur-
ing bachelor’s degree completion were restricted to
mothers with some college (which includes those with
associate’s degrees) or less. All sample sizes are
reflected in the tables (see Table 6). Here, I also added
in Set 2 covariates to address sources of selection into
additional degree completion.
To examine the robustness of any significant asso-
ciations between maternal health and the time-varying
measures of mothers’ postnatal college attainment to
unobserved, fixed sources of heterogeneity (e.g.,
personality trait, genetic factor), as well as potentially
confounding time-varying factors, I turned to fixed
effects modeling techniques. This approach provided
estimates of within-person residualized change,
thereby addressing a key limitation of random intercept
models—the assumption that all unobserved fixed
sources of heterogeneity were orthogonal to the predic-
tors (Allison 2009). As a class of hierarchical models,
they also accommodated the longitudinal structure of
the data, including time-varying Set 3 confounds. As a
final analysis step, I conducted a series of robustness
analyses that addressed the potential for population
heterogeneity based on mothers’ race-ethnicity, parity
(i.e., number of live births) at baseline, and education at
baseline; that better tease out issues of lagged effects
and bidirectionality; and that explore additional health
outcomes and the potential for indirect effects.
Models were estimated using Stata 15.1. To
account for modest missingness on the independent
variables (see Appendix A in the online version of
the article), multiple imputation (with 20 iterations)

10
Journal of Health and Social Behavior 62(1)
via chained equations was performed. Dependent
variables were part of the imputation model but not
imputed (von Hippel 2007). To analyze the multi-
ply imputed data, the Stata mi estimate suite of
commands was used. Random intercept models
predicting continuous outcomes (overall health,
parenting stress) were estimated using the xtreg, re
procedure. The xtlogit, re procedure was used for
categorical outcomes (global mental health, smok-
ing). Assuming data are missing at random (Allison
2012), these procedures also helped address cases
with missingness on the dependent variable (i.e.,
maternal health) in which there was at least one
observation through the use of maximum likelihood
estimation. For the fixed effects models, the xtreg,
fe (for continuous outcomes) and clogit (for cate-
gorical) procedures were used.
Results
A Descriptive Overview of Maternal
Education and Its Linkage to Maternal
Health
Table 3 provides a description of mothers’ education
at the baseline and postnatal college completion
between Waves 1 and 6. At the baseline wave, 40%
of the FFCW sample did not have a high school
degree, 25% reported a high school degree or GED,
24% reported some college (i.e., a vocational degree,
associate’s degrees, or college schooling that did not
result in a degree), and 11% had a bachelor’s degree.
Between the baseline interview and sixth wave, 33%
(n = 1,099) of the Wave 6 sample (n = 3,146 mothers)
had completed a vocational degree, 13% (n = 417)
had completed an associate’s degree, and 9%
(n = 295) had completed a bachelor’s degree. Among
mothers who completed a vocational degree, 67% of
cases (n = 733) resulted in an increase in education.
Among mothers who completed an associate’s
degree, 54% of cases (n = 224) resulted in an observ-
able increase in education (this estimate is likely
conservative because of how some college is coded).
Among mothers who completed a bachelor’s degree,
there was an increase in education in 85% of cases (n
= 251). More information about the number and per-
centage of mothers (based on the Wave 6 sample)
who earned various degrees by their starting level of
education (thus providing details on cell sizes) is
also presented in Table 3.
To provide a preliminary picture of the link
between maternal health and education, Table 4
Table 3.  Frequencies and Percentages of Maternal Education at Baseline and Increases in Education
through Wave 6 in the Fragile Families and Child Wellbeing Study Sample.
Baseline Sample
Completion by Wave 6a

(n = 4,898)
(n = 3,286)
(n = 3,182)
(n = 3,162)

Vocational Degree
Associate’s Degree
Bachelor’s Degree
1. Frequencies
Education at baseline
  Less than high school
1,949
413
94
27
  High school degree
1,237
320
130
53
  Some college
1,189
313
185
171
  College or more
524
53
8
43
Total
4,898
1,099
417
295
2. Percentagesb
Education at baseline
  Less than high school
40%
13%
3%
<1%
  High school degree
25%
10%
4%
<2%
  Some college
24%
10%
6%
5%
  College or more
11%
<2%
<1%
<2%
Total
33%
13%
9%
aWave 6 measure based on an aggregate of all reports of additional education, within degree types, across waves
among mothers in Wave 6. Thus, sample sizes vary slightly across columns because of differential rates of dropout
and reentry into the sample across waves among mothers who completed different degrees.
bReflects percentages of mothers who earned each degree by their baseline education among mothers remaining in
the sample at Wave 6.

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11
presents the bivariate associations between baseline
maternal education and the four maternal health
outcomes. Pairwise estimates of statistical signifi-
cance between different levels of education were
performed using one-way analysis of variance.
Maternal health reflects an average of the pooled
estimates across the five study waves. Results dem-
onstrate the educational gradient in maternal health.
As mothers’ baseline level of educational attain-
ment increased, their overall self-rated health
increased, and their reports of parenting stress, hav-
ing poor mental health, and having recently smoked
decreased. The rate of smoking was four times
greater for mothers with less than a high school
degree than for mothers with a bachelor’s degree
(36% vs. 8%). Differences in mental health were
also noteworthy, albeit smaller (26% vs. 14%).
Patterns for overall health represented a 55% of a
standard deviation (overall SD = 1.05) difference
between mothers with the highest and lowest levels
of education. Patterns for parenting stress amount
to a 15% of a standard deviation (overall SD = .69)
difference. Notably, differences among those with a
high school degree and some college were not as
pronounced.
Multivariate Analysis of Maternal
Education and Health
For the multivariate analyses, I first estimated an
unconditional (i.e., without predictors) random
intercept model to assess the functional form of
maternal health (Model 1). This step revealed a time
trend in three of four health outcomes that was lin-
ear in form for global mental health (i.e., increased
over time) and quadratic in form for overall health
and parenting stress (both increased over time, but
the rate of increase decelerated at later waves). This
quadratic term was thus retained in subsequent
models. These coefficients, along with other param-
eters of interest (e.g., variance in within person
residuals, intraclass correlation), appear in Appendix
B in the online version of the article.
Next, I added baseline maternal education to the
model, with the high school degree/GED as the ref-
erence category, along with the Set 1 background
covariates (see Table 5, Model 2). These results
were similar to the patterns presented in the bivari-
ate analyses. Compared to mothers with high school
degrees or GEDs, mothers with bachelor’s degrees
had higher overall health and were less likely to
report a mental health issue or smoking. Conversely,
Table 4.  Pooled Means/Frequencies and Contrasts of Maternal Health Outcomes by Maternal
Education at Baseline based on One-Way ANOVA with Data from Fragile Families and Child Wellbeing
Study, Waves 2 through 6.
Means (SEs)/Frequencies of Maternal Health Outcomes

Self-Rated Overall
Health
Parenting Stress
Global Mental
Health
Smoking
Baseline education level
  Less than high school
3.49c
(.02)
2.22a
(.01)
27%a
36%a
  High school degree
3.63b
(.01)
2.11bc
(.01)
21%b
27%b
  Some college
3.68b
(.01)
2.07c
(.01)
21%b
22%c
  College degree
4.07a
(.02)
2.12b
(.01)
14%c
8%d
Full sample (SDs)
3.64
(1.05)
2.14
(.69)
22%
27%
n observations
19,374
18,868
19,329
18,275
Note: Estimates based on pooled values across Waves 2 through 6. Different superscripts indicate statistically
significant differences in health (with a p value of at least .05) between education levels. For example, for self-rated
overall health, mothers with college degrees (superscript “a”) have significantly different levels of overall health
compared to mothers with some college education (superscript “b”), but mothers with some college education do
not have significantly different levels compared to the high school group (also superscript “b”). The “a” superscript
indicates the largest value. The “d” superscript indicates the smallest value.

12
Journal of Health and Social Behavior 62(1)
mothers without a high school diploma had worse
overall health and higher rates of poor mental
health and smoking. They also had more parenting
stress. Differences among the high school and some
college groups were nonsignificant, with the excep-
tion of smoking, with lower rates of smoking
among the some college group. Education also did
not significantly differentiate parenting stress levels
among mothers with a high school degree/GED or
higher, although patterns across all outcomes are
consistent with the gradient.
Examining the Health Impact
of Mothers’ Higher Educational
Attainment
Having established these patterns, I examined
whether, net of these associations, postsecondary
education acquired after a birth can improve moth-
ers’ average levels of health. In doing so, I added
the Set 2 covariates (e.g., Wave 1 family income),
which were excluded from prior analysis because
they were likely to be endogenous to mothers’ base-
line education. The results of this modeling step
(see Table 6, Models 3–5) revealed few significant
associations between the time-varying measures
for postsecondary education and maternal health
(10 of 12 models). There were two exceptions.
Among mothers with a technical/trade degree or
less at baseline, those who earned an associate’s
degree reported significantly higher levels of over-
all health (B = .11, SE = .05; these patterns were the
same when restricting the sample to mothers in the
some college category). Second, mothers with
some college or less at the baseline wave who sub-
sequently completed a bachelor’s degree reported
significantly lower likelihoods of smoking (B =
–.62, SE = .26; odds ratio = .54). These estimates,
however, remain vulnerable to omitted variable
bias.
Thus, as a final step, I used fixed effects to tease
out time-invariant unobserved sources of heteroge-
neity. For this analysis, I followed an iterative pro-
cess that incorporated the time-varying covariates
Table 5.  Random Intercept Models Predicting Maternal Health by Maternal Education at Baseline with
Data from Fragile Families and Child Wellbeing Study, Waves 2 through 6.
B (SE) of Maternal Health Outcomes

Self-Rated Overall
Health
Parenting Stress
Global Mental
Health
Smoking
Model 2
Main parameters
  Intercept
3.89***
(.10)
2.16***
(.07)
–2.17***
(.25)
–2.85***
(.58)
  Wave
.04
(.03)
.05**
(.02)
.04
(.02)
.97***
(.13)
  Wave2
−.02***
(.00)
−.01***
(.00)
—
−.12***
(.01)
Mother education baselinea
  Less than high school
−.15***
(.03)
.13***
(.02)
.56***
(.09)
1.85***
(.18)
  Some college
.03
(.03)
−.04
(.02)
−.04
(.10)
−.82***
(.19)
  Bachelor’s degree
.42***
(.05)
.02
(.03)
−.69***
(.14)
−4.00***
(.35)
n observations
19,374
18,868
19,329
18,275
n groups
4,710
4,697
4,709
4,689
Note: Models include random intercept and control for maternal race, mother age, mother age at first birth, mother
immigrant status, whether the current birth was the mother’s first, whether the mother lived with both parents
through age 15, and the education of the mother’s mother. Model 1 results available in the online version of the
article. Intraclass correlation, variance of within person residuals, and variance of residuals are not reported.
aReference group = high school/GED.
***p < .001.

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13
in a step-wise fashion. Factors less likely to be
mechanisms of mothers’ postsecondary attainment
(e.g., child health, access to emergency childcare)
were entered first. Factors more likely to be mecha-
nisms (e.g., family income, maternal paid work)
were entered last. Following this approach and
specifying fixed effects, the inclusion of the first
covariate, child overall health, attenuated the asso-
ciation between mothers’ associate’s degree com-
pletion and overall health and between mothers’
bachelor degree completion and reduced smoking
to nonsignificance.
Additional Robustness Analyses
Given the lack of significant findings, I performed
several auxiliary analyses to determine whether a
significant association may emerge under alternate
conditions. These included (1) among certain sub-
populations, including mothers for whom the focal
child was their first birth and mothers who were not
racial-ethnic minorities; (2) for other health out-
comes, including the CIDI–SF measure of depres-
sion, reports of heavy drinking, and body mass
index; or (3) using measures of school enrollment.
None of the models produced significant findings.
I also examined (4) heterogeneity in the “effects”
of a postsecondary degree based on mothers’ base-
line levels of education by adding interactions
between baseline education and the time-varying
additional education measures in the fixed effects
models. These analyses did not reveal heterogeneity
based on baseline education, with one exception.
There was a marginally significant association
between bachelor’s completion and reduced smok-
ing among mothers with both a high school degree/
GED and less than a high school degree at baseline.
Given that these were small groups (ns = 27 and 53,
respectively) and low in statistical power, I reesti-
mated the model for just this group. This model sug-
gested that a bachelor’s degree for this group was
significantly associated (p < .01) with a 16% reduc-
tion in the odds of smoking. Due to limited cell
sizes, I could not examine the cumulative effect of
multiple postnatal degrees.
Additionally, I considered (5) the potential for a
“lagged” effect by regressing health at Wave 6 on
measures on additional education measures from
Wave 5 net of the Set 1 and 2 covariates. These
models did not yield significant results. Finally, I
explored (6) the potential for indirect effects, focus-
ing on income, which is likely to be a main driver in
mothers’ decisions to return to school. These analy-
ses used mediation tests in structural equation mod-
eling that extended the lagged models. In short,
while income at Wave 6 was significantly associ-
ated with all four concurrent health outcomes, it did
not significantly mediate the link between voca-
tional degree or associate’s degree attainment and
maternal health. It did significantly mediate the
Table 6.  Random Intercept Models, Predicting Maternal Health by Postsecondary Attainment, Net
of Baseline Education and Restricted by Education at Birth with Data from Fragile Families and Child
Wellbeing Study, Waves 2 through 6.
B (SE) of Maternal Health Outcomes

Self-Rated
Overall Health Parenting Stress
Global Mental
Health
Smoking
Degree completion
Model 4: Vocational degree
−.01
(.03)
−.03
(.02)
.01
(.09)
.28
(.14)
  n groups =
3,050
3,039
3,049
3,035
Model 5: Associate’s degree
.10*
(.05)
−.04
(.03)
−.19
(.16)
−.23
(.25)
  n groups =
3,197
3,186
3,196
3,183
Model 6: Bachelor’s degree
.08
(.05)
.02
(.03)
−.16
(.17)
−.62*
(.27)
  n groups =
4,202
4,189
4,201
4,182
Note: Models 4 through 6 control for all variables in Model 3 as well as baseline/Wave 2 measures of family structure,
family income-to-needs, work status, child birth weight, insurance coverage, emergency child care, access to financial
support, owned home, and number of children in the home.
*p < .05.

14
Journal of Health and Social Behavior 62(1)
association with Wave 5 bachelor’s degree attain-
ment, but the size of the indirect effects were small
(see Appendix C in the online version of the arti-
cle). Taken in combination with the small coeffi-
cient size and insignificant direct effect for
bachelor’s degree completion, these results suggest
that this small positive mediating effect of greater
income is likely offset by other negative mediating
factors (for a methodological discussion, see Zhao,
Lynch, and Chen 2010).
Discussion
This study aimed to understand the health impact of
a particular nonnormative process (in terms of how
education has conventionally been, and still consid-
ered to be, completed and reflected in how educa-
tional institutions are structured and traditional
students are defined): women’s pursuit of higher
education after the birth of a child. A rich body of
scholarship linking education to health suggests that
mothers who return to school to complete a higher
education degree should experience improved
health outcomes, yet this question has not been
directly investigated. Moreover, life course theories
that point to the “life course penalties” associated
with off-time schooling suggest that mothers may
not experience the same health returns to higher
education as women who completed higher educa-
tion prior to having children. As such, I tested the
expectation that mothers’ postnatal schooling will
do little to alter their health. Doing so has theoreti-
cal relevance to understanding a key social deter-
minant of health, education, as well as tremendous
policy importance given the increasing health pol-
icy emphasis on investments in higher education
and two-generation policies that target lower edu-
cated mothers (Gardner, Brooks-Gunn, and Chase-
Lansdale 2017; Kaushal 2014).
The results widely supported my expectation.
Overall, mothers’ pursuit of higher education did
not improve their health. This pattern held true
across a variety of outcomes that reflected physical,
mental, and behavioral aspects of health; popula-
tion subgroups; and measures of higher education
that reflected increased years and degrees and insti-
tution types. Although these findings stand in con-
trast with the general consensus of much of the
research and theory linking education to health,
they are consistent with several recent studies that
have explored the connections between nonnorma-
tive educational trajectories and health outcomes
(e.g., Barban 2013; Miech et al 2015). This bur-
geoning literature highlights the need for more
nuanced treatments of the linkage between educa-
tion and health and raises critical questions for the
understanding of theories informing this body of
research. Specifically, why does sequencing the
completion of schooling after the transition to
motherhood diminish the power of educational
attainment to affect one’s health?
I offer some tentative explanations. First, the
social aspects of mothers’ lives—where they live,
who they interact with, whether they are married—
are “sticky” features of people’s lives that may
remain largely unchanged after having children
(Laumann 1973). Second, the circumstances
around mothers’ births may have enduring nega-
tive health effects; for example, women with unin-
tended births experience higher risks for depression
across the life course (Herd et al. 2016). Third, the
nonpecuniary returns to higher education—the
skills and capacities that help one manage their
health (see Mirowsky and Ross 2003)—may be
less for mothers if they have less time to devote to
school work; have less time for and interest in
extracurricular activities (Bloom 2007); suffer
lower levels of emotional well-being due to the
challenges of balancing work, family, and school-
ing (Augustine et al. 2018); and receive less sup-
port from family and friends (Carney-Crompton
and Tan 2002). Fourth, the labor market returns for
mothers may be small because they are penalized
for their age or lack of work experience, lack
access to the types of networks that connect them
to good jobs, or had enrolled in institutions like
for-profit schools that yield fewer labor market
opportunities and result in greater debt than non-
profit schools (Deming, Goldin, and Katz 2013;
Elman and O’Rand 2004; Felmlee 1988). Lastly,
mothers who earn a bachelor’s degree may experi-
ence increased income, as suggested by the media-
tion models, but these benefits may be offset by
other factors, such as the loss of social support as a
result of mothers’ changed socioeconomic position
(Wilson 2008), heightened time crunches due to
work demands, and many of the factors described
previously, such as student debt. Adjudicating
among these explanations is beyond the scope of
the current study but are important topics for future
research. A distinct explanation that should also be
considered reflects the etiology of health and the
question of whether and to what extent health is set
early in the life course (Conti et al. 2010; Harris
et al. 2013).
As to social policy, these findings suggest that
efforts aimed at improving maternal health may be
more efficacious if they target women prior to

Augustine
15
transitioning into adult roles. This does not mean
that additional education cannot help some mothers.
Rather, providing mothers with educational oppor-
tunities may not lead to reductions in population-
level disparities in maternal health—at least not
until more knowledge is acquired to help scholars
identify and intervene in the factors that explain
why the health benefits of additional education for
mothers are low. By the same token, there was one
exception to the pattern of results. Completion of a
bachelor’s degree among mothers who had no col-
lege schooling at baseline led to a decrease in smok-
ing. Although speculative, this pattern may be
explained by an increased appraisal of the “opportu-
nity costs” to smoking for expected work outcomes,
either in terms of physical health or coworker evalu-
ations (Cowell 2006), the prevalence of smoking
bans and education campaigns on college campuses
(Halperin and Rigotti 2003), and lower rates of reg-
ular smoking among traditional-aged college stu-
dents, who student mothers may view as a standard
of comparison (Centers for Disease Control and
Prevention 2019). Of course, future research must
also aim to assess the validity of this finding, partic-
ularly given the small size of this subsample.
This study also had several limitations. First, the
sample overrepresented minority women in urban
metros. Although I did not find evidence of a health
benefit to postnatal college completion among white
mothers—for whom there may be a larger health
benefit to higher education than minority mothers
(Kimbro et al. 2008)—this possibility still needs to
be investigated because the sample of white mothers
provided limited power to detect significant effects.
Given subsample size limitations, I also did not
investigate the moderating role of other demo-
graphic factors, such as marital status or work expe-
rience (see Rindfuss, Swicegood, and Rosenfeld
1987), which future research should explore, nor did
I explore circumstances under which some mothers
may benefit from completing a college degree (see
Furstenberg 2005), for example, in families with
access to high-quality child care (Harding 2015).
Furthermore, I could not differentiate among the
“quality” of mothers’ education, for example,
whether the coursework was online or in person,
the type of major, or whether mothers earned
degrees from a for-profit institution. This latter
issue is particularly noteworthy given that enroll-
ment in for-profit schools is substantially higher
among mothers than other groups (Cruse et  al.
2019) and leads to fewer labor market payoffs and
greater debt (Deming et al. 2013). At the same time,
enrollment in for-profit schools, as well as among
student mothers, has declined over the past several
years (Cruse et  al. 2019). Given these trends, as
well as recent increases in tuition costs and turbu-
lence in the economy (which recently included his-
torically low and high levels of unemployment), it
also remains unclear whether mothers who com-
pleted postsecondary education subsequent to the
FFCW sample would have experienced different
returns to postsecondary schooling.
In sum, this study focused on an emerging
yet understudied phenomenon among health
researchers—mothers’ return to school to increase
their education—to investigate the policy-relevant
issue of whether mothers’ reentry into school can
improve their health outcomes. Contrary to much
research and theory, the findings suggest that in
general, the answer is no. This finding points to an
important source of heterogeneity in the health
returns to education—the timing of degree attain-
ment vis-à-vis the timing of children’s births—that
further stratifies the education–health gradient in an
often unacknowledged way. It also speaks to the
limits of current public health and two-generation
policy approaches, points to alternative prescrip-
tions for social policy, and highlights a new research
frontier that explores the social and structural
constraints that limit the health returns to additional
education among women with children.
Author’s Note
Previous versions of this article were presented at the 2018
annual meeting for the Interdisciplinary Association for
Population Health Science and the 2019 annual meeting
for the Population Association of America.
ORCID Id
Jennifer March Augustine
 https://orcid.org/0000-
0003-1446-8723
Note
1.
I also included three additional measures—heavy
drinking, clinical depression, and body mass
index—in auxiliary analyses, as reported in the
Robustness Analyses section, but not as focal mea-
sures for the following reasons. Heavy drinking
was more common among higher-educated mothers
than lower-educated mothers and thus did not con-
form to the “educational gradient.” It was also low
in variability. Body mass index was only reported in
Waves 3 to 5 and only among a subset of mothers.
Clinical depression, as explained in the Methods
section, was a more restrictive measure than the
Global Mental Health measure that was also lower
in variability.

16
Journal of Health and Social Behavior 62(1)
References
Abidin, Richard. 1983. Parenting Stress Index. Charlottesville,
VA: Pediatric Psychology Press.
Adams, Scott. J. 2002. “Educational Attainment and
Health: Evidence from a Sample of Older Adults.”
Education Economics 10(1):97–109.
Adler, Nancy E., Thomas Boyce, Margaret A. Chesney,
Sheldon Cohen, Susan Folkman, Robert L. Kahn, and
Syme S. Leonard. 1994. “Socioeconomic Status and
Health: The Challenge of the Gradient.” American
Psychologist 49(1):15–24.
Allison, Paul D. 2009. Fixed Effects Regression Models.
Vol. 160. Newbury Park, CA: SAGE Publications.
Allison, Paul. 2012. “Handling Missing Data by Maximum
Likelihood.” Paper 312-2012. SAS Global Forum.
http://www.statisticalhorizons.com/wp-content/
uploads/MissingDataByML.pdf.
Arendt, Jacob N. 2005. “Does Education Cause Better
Health? A Panel Data Analysis Using School Reforms
for Identification.” Economics of Education Review
24(2):149–60.
Augustine, Jennifer M. 2016. “Exploring New Life
Course Patterns of Women’s Secondary and Post-
Secondary Schooling.” Population Research and
Policy Review 35(6):727–55.
Augustine, Jennifer M., and Daniela Negraia. 2018.
“Can Increased Educational Attainment among
Lower Educated Mothers Reduce Inequalities in
the Skill Development of Children?” Demography
55(1):59–82.
Augustine, Jennifer March, Kate C. Prickett, and Daniela
V. Negraia. 2018. “Doing It All? Mothers’ College
Enrollment, Time Use, and Affective Well-Being.”
Journal of Marriage and Family 80(4):963–74.
Bandeira, Frank C., Amanda Kalaydjian Richardson,
David J. Lee, Jian-Ping He, and Kathleen R.
Merikangas. 2011. “Secondhand Smoke Exposure
and Mental Health among Children and Adolescents.”
Archives of Pediatrics & Adolescent Medicine 165(4):
332–38.
Barban, Nicola. 2013. “Family Trajectories and Health:
A Life Course Perspective.” European Journal of
Population 29(4):357–85.
Beck, Audrey, Carey E. Cooper, Sara McLanahan, and
Jeanne Brooks-Gunn. 2010. “Partnership Transitions
and Maternal Parenting.” Journal of Marriage and
Family 72(2):219–33.
Bianchi, Suzanne M., John P. Robinson, and Melissa
A. Milkie. 2006. The Changing Rhythms of
American Family Life. New York, NY: Russell Sage
Foundation.
Bloom, Janice. 2007. “(Mis)reading Social Class in the
Journey towards College: Youth Development in
Urban America.” Teachers College Record 109(2):
343–68.
Braboy Jackson, Pamela. 2004. “Role Sequencing: Does
Order Matter for Mental Health?” Journal of Health
and Social Behavior 45(2):132–54.
Braboy Jackson, Pamela, and Alexandra Berkowitz.
2005. “The Structure of the Life Course: Gender
and Racioethnic Variation in the Occurrence and
Sequencing of Role Transitions.” Advances in Life
Course Research 9:55–90.
Brückner, Hannah, and Karl Ulrich Mayer. 2005.
“De-standardization of the Life Course: What It
Might Mean? And if It Means Anything, Whether
It Actually Took Place?” Advances in Life Course
Research 9:27–53.
Carney-Crompton, Shawn, and Josephine Tan. 2002.
“Support Systems, Psychological Functioning, and
Academic Performance of Nontraditional Female
Students.” Adult Education Quarterly 52(2):140–54.
Centers for Disease Control and Prevention. 2019. “Current
Smoking among U. S. Adults Aged 18 and Older.”
https://www.cdc.gov/tobacco/campaign/tips/resources/
data/cigarette-smoking-in-united-states.html.
Cockerham, William C. 2005. “Health Lifestyle Theory
and the Convergence of Agency and Structure.”
Journal of Health and Social Behavior 46(1):51–67.
Conti, Gabriella, James Heckman, and Sergio Urzua. 2010.
“The Education-Health Gradient.” The American
Economic Review 100(2):234–38.
Cooksey, Elizabeth C., Elizabeth G. Menaghan, and
Susan M. Jekielek. 1997. “Life-Course Effects of
Work and Family Circumstances on Children.”
Social Forces 76(2):637–65.
Cooper, Carey E., Sara S. McLanahan, Sarah O.
Meadows, and Jeanne Brooks-Gunn. 2009. “Family
Structure Transitions and Maternal Parenting Stress.”
Journal of Marriage and Family 71(3):558–74.
Cottom, Tressie McMillan. 2017. Lower Ed: The
Troubling Rise of For-Profit Colleges in the New
Economy. New York City, NY: New Press.
Cowell, Alexander J. 2006. “The Relationship between
Education and Health Behavior: Some Empirical
Evidence.” Health Economics 15(2):125–46.
Crnic, Keith A., Catherine Gaze, and Casey Hoffman.
2005. “Cumulative Parenting Stress across the
Preschool Period: Relations to Maternal Parenting
and Child Behavior at Age 5.” Infant and Child
Development: An International Journal of Research
and Practice 14(2):117–32.
Cruse, Lindsey Reichlin, Tessa Holtzman, Barbara Gault,
David Croom, and Portia Polk. 2019. Parents in
College by the Numbers. Washington, DC: Institute
for Women’s Policy Research.
Cutler, David M., and Adriana Lleras-Muney. 2010.
“Understanding Differences in Health Behaviors by
Education.” Journal of Health Economics 29(1):1–28.
Deming, David, Claudia Goldin, and Lawrence Katz.
2013. “For-Profit Colleges.” The Future of Children
21(1):137–63.
Duke, Naomi, and Ross Macmillan. 2016. “Schooling,
Skills, and Self-Rated Health: A Test of Conventional
Wisdom on the Relationship between Educational
Attainment and Health.” Sociology of Education
89(3):171–206.

Augustine
17
Edin, Kathryn, and Kefalas Maria 2011. Promises I Can
Keep: Why Poor Women Put Motherhood before
Marriage. Berkeley, CA: University of California
Press.
Eide, Eric R., and Mark H. Showalter. 2011. “Estimating
the Relation between Health and Education: What
Do We Know and What Do We Need to Know?”
Economics of Education Review 30(5):778–91.
Eisenhower, Abbey Severance, Bruce L. Baker, and Jan
Blacher. 2009. “Children’s Delayed Development
and Behavior Problems: Impact on Mothers’
Perceived Physical Health across Early Childhood.”
Social Science & Medicine 68(1):89–99.
Elder, G. H. 1984 “Families, Kin, and the Life Course:
A Sociological Perspective.” Pp. 80–136 in Review
of Child Development Research: The Family, edited
by R. Parke. Chicago: University of Chicago Press.
Elman, Cheryl, and Angela M. O’Rand. 2004. “The
Race Is to the Swift: Socioeconomic Origins,
Adult Education, and Wage Attainment.” American
Journal of Sociology 110(1):123–60.
Felmlee, Diane H. 1988. “Returning to School and
Women’s Occupational Attainment.” Sociology of
Education 61(1):29–41.
Frieden, Thomas R. 2010. “A Framework for Public
Health Action: The Health Impact Pyramid.”
American Journal of Public Health 100(4):590–95.
Furstenberg, Frank F. 2005. “Non-normative Life Course
Transitions: Reflections on the Significance of
Demographic Events on Lives.” Advances in Life
Course Research 10:155–72.
Garbarski, Dana. 2014. “The Interplay between Child
and Maternal Health: Reciprocal Relationships
and Cumulative Disadvantage during Childhood
and Adolescence.” Journal of Health and Social
Behavior 55(1):91–106.
Gardner, Margo, Jeanne Brooks-Gunn, and P. Lindsay
Chase-Lansdale.
2017.
“The
Two-Generation
Approach to Building Human Capital: Past, Present,
and Future.” Pp. 330–61 in The Wiley Handbook of
Early Childhood Development Programs, Practices,
and Policies, edited by Edited by E. Votruba-Drzal
and E. Dearing. Hoboken, NJ: Wiley.
Gardner, Margo, Anne Martin, and Amélie Petitclerc.
2019. “Mothers’ Postsecondary Entry during
Early Childhood: Short- and Long-Term Effects
on Children.” Journal of Applied Developmental
Psychology 62:11–25.
Gershoff, Elizabeth T., J. Lawrence Aber, C. Cybele
Raver, and Mary Clare Lennon. 2007. “Income Is
Not Enough: Incorporating Material Hardship into
Models of Income Associations with Parenting and
Child Development.” Child Development 78(1):70–
95.
Ghisletta, Paolo, Olivier Renaud, Nadège Jacot, and
Delphine Courvoisier. 2015. “Linear Mixed-Effects
and Latent Curve Models for Longitudinal Life
Course Analyses.” Pp. 155–78 in A Life Course
Perspective on Health Trajectories and Transitions,
edited by C. Burton-Jeangros, S. Cullati, A. Sacker,
and D. Blane. Springer: Cham, the Netherlands.
Hahn, Robert A., and Benedict I. Truman. 2015.
“Education Improves Public Health and Promotes
Health Equity.” International Journal of Health
Services 45(4):657–78.
Halperin, Abigail C., and Nancy A. Rigotti. 2003. “U. S.
Public Universities’ Compliance with Recommended
Tobacco-Control Policies.” Journal of American
College Health 51(5):181–88.
Harding, Jessica F. 2015. “Increases in Maternal
Education and Low-Income Children’s Cognitive and
Behavioral Outcomes.” Developmental Psychology
51(5):583–99.
Harding, Jessica F., Pamela A. Morris, and Diane
Hughes. 2015. “The Relationship between Maternal
Education and Children’s Academic Outcomes: A
Theoretical Framework.” Journal of Marriage and
Family 77(1):60–76.
Harris, Kathleen Mullan, Carolyn Tucker Halpern, Jon
Hussey, Eric A. Whitsel, Ley Killeya-Jones, Joyce
Tabor, and Glen Elder. 2013. “Social, Behavioral,
and Genetic Linkages from Adolescence into
Adulthood.” American Journal of Public Health
103(1):S25–32.
Herd, Pamela, Jenny Higgins, Kamil Sicinski, and Irina
Merkurieva. 2016. “The Implications of Unintended
Pregnancies for Mental Health in Later Life.”
American Journal of Public Health 106(2):421–29.
Hofhuis, Ward, Johan C. de Jongste, and Peter Merkus.
2003. “Adverse Health Effects of Prenatal and
Postnatal Tobacco Smoke Exposure on Children.”
Archives of Disease in Childhood 88(12):1086–90.
Hogan, Dennis P. 1978. “The Variable Order of Events
in the Life Course.” American Sociological Review
43(4):573–86.
Hout, Michael. 2012. “Social and Economic Returns to
College Education in the United States.” Annual
Review of Sociology 38:379–400.
Kalleberg, Arne L. 2011. Good Jobs, Bad Jobs: The Rise
of Polarized and Precarious Employment Systems
in the United States, 1970s-2000s. New York, NY:
Russell Sage Foundation.
Katz, Sheila. 2013. “‘Give Us a Chance to Get an
Education’: Single Mothers’ Survival Narratives
and Strategies for Pursuing Higher Education on
Welfare.” Journal of Poverty 17(3):273–304.
Kaushal, Neeraj. 2014. “Intergenerational Payoffs of
Education.” The Future of Children 24(1):61–78.
Kimbro, Rachel Tolbert, Sharon Bzostek, Noreen
Goldman, and Germán Rodríguez. 2008. “Race,
Ethnicity, and the Education Gradient in Health.”
Health Affairs 27(2):361–72.
Laird, Nan M., and James H. Ware. 1982. “Random-
Effects Models for Longitudinal Data.” Biometrics
38(4): 963–74.
Laumann, Edward O. 1973. Bonds of Pluralism: The
Form and Substance of Urban Social Networks. New
York, NY: Wiley-Interscience.

18
Journal of Health and Social Behavior 62(1)
Link, Bruce G., and Jo Phelan. 1995. “Social Conditions
as Fundamental Causes of Disease.” Journal of
Health and Social Behavior 1995:80–94.
Lleras-Muney, Adriana. 2005. “The Relationship
between Education and Adult Mortality.” U.S.
Review of Economic Studies 72(1):189–221.
Lynch, Jamie L., and Paul T. von Hippel. 2016. “An
Education Gradient in Health, a Health Gradient
in Education, or a Confounded Gradient in Both?”
Social Science & Medicine 154:18–27.
Macmillan, Ross. 2005. “The Structure of the Life
Course: Classic Issues and Current Controversies.”
Advances in Life Course Research 9:3–24.
Marini, Margaret Mooney. 1984. “Age and Sequencing
Norms in the Transition to Adulthood.” Social Forces
63(1):229–44.
McDonough, Patricia M. 1997. Choosing Colleges: How
Social Class and Schools Structure Opportunity.
Albany, NY: SUNY Press.
McFarland, Michael, and Brandon G. Wagner. 2015.
“Does a College Education Reduce Depressive
Symptoms in American Young Adults?” Social
Science & Medicine 146:75–84.
Meara, Ellen R., Seth Richards, and David M. Cutler.
2008. “The Gap Gets Bigger: Changes in Mortality
and Life Expectancy, by Education, 1981–2000.”
Health Affairs 27(2):350–60.
Miech, Richard A., Michael Shanahan, Jason Boardman,
and Shawn Bauldry. 2015. “The Sequencing of a
College Degree during the Transition to Adulthood:
Implications for Obesity.” Journal of Health and
Social Behavior 56(2):281–95.
Mirowsky, John, and Catherine E. Ross. 2008. “Education
and Self-Rated Health: Cumulative advantage and
Its Rising Importance.” Research on Aging 30(1):
93–122.
Montez, Jennifer Karas, and Anna Zajacova. 2013.
“Trends in Mortality Risk by Education Level and
Cause of Death among White Women in the United
States from 1986 to 2006.” American Journal of
Public Health 103(3):473–79.
Mirowsky, John, and Catherine Ross. 2003. Education,
Social Status, and Health. New York, NY: Aldine
De Gruyter.
Parrish, R. Gibson. 2010. “Measuring Population Health
Outcomes.” Preventing Chronic Disease 7(4):1–11.
Pearlin, Leonard, Scott Schieman, Elena M. Fazio, and
Stephen Meersman. 2005. “Stress, Health, and
the Life Course: Some Conceptual Perspectives.”
Journal of Health and Social Behavior 46(2):205–19.
Reichman, Nancy E., Julien O. Teitler, Irwin Garfinkel,
and Sara S. McLanahan. 2001. “Fragile Families:
Sample and Design.” Children and Youth Services
Review 23(4/5):303–26.
Rindfuss, Ronald R., C. Gray Swicegood, and Rachel
A. Rosenfeld. 1987. “Disorder in the Life Course:
How Common and Does It Matter?” American
Sociological Review 512(6):785–801.
Rosenbaum, James E. 2001. Beyond College for All:
Career Paths for the Forgotten Half. New York, NY:
Russell Sage Foundation.
Seefeldt, Kristin S. 2016. Abandoned Families: Social
Isolation in the Twenty-First Century. New York,
NY: Russell Sage Foundation.
Singer, Judith D., and John B. Willett. 2003. Applied
Longitudinal Data Analysis: Modeling Change and
Event Occurrence. Oxford, UK: Oxford University
Press.
Turner, R. Jay, and Donald A. Lloyd. 1999. “The Stress
Process and the Social Distribution of Depression.”
Journal of Health and Social Behavior 40(4):374–404.
Turney, Kristin. 2011. “Maternal Depression and
Childhood Health Inequalities.” Journal of Health
and Social Behavior 52(3):314–32.
Von Hippel, Paul T. 2007. “Regression with Missing Ys:
An Improved Strategy for Analyzing Multiply Imputed
Data.” Sociological Methodology 37(1):83–117.
Williams, Kristi, Sharon Sassler, Adrianne Frech, Fenaba
Addo, and Elizabeth Cooksey. 2011. “Nonmarital
Childbearing, Union History, and Women’s Health
at Midlife.” American Sociological Review 76(3):
465–86.
Wilson, Kristin B. 2008. “Institutional Influences
Affecting the College-Going Decisions of Low-
Income Mothers Attending a Rural Midwestern
Community College.” PhD dissertation, Department
of Sociology, University of Missouri.
Yuan, Anastasia S. 2008. “Exploring the Changes in
Economic Hardship and Children’s Well-Being over
Time: The ‘Linked Lives’ of Parents and Children.”
Advances in Life Course Research 13:321–41.
Zajacova, Anna, and Elizabeth M. Lawrence. 2018. “The
Relationship between Education and Health: Reducing
Disparities through a Contextual Approach.” Annual
Review of Public Health 39:273–89.
Zhao, Xinshu, John G. Lynch, and Qimei Chen. 2010.
“Reconsidering Baron and Kenny: Myths and Truths
about Mediation Analysis.” Journal of Consumer
Research 37(2):197–206.
Author Biography
Jennifer March Augustine is an associate professor in
the Department of Sociology at the University of South
Carolina. Her research applies life course and ecological
approaches to studying the connections among parents’
socioeconomic circumstances, their health and well-being,
and the health and development of their children. She is
particularly interested in understanding the significance of
parents’ education, its implication for other related life
course outcomes, and the combined impact of these factors
on various dimensions of family life.

    Policy Brief:

Answer:
**Mothers’ Out-of-Sequence Postsecondary Education and Their Health and Health Behaviors**

**Policy Problem**A growing share of U.S. college students are mothers who return to school after having children, encouraged by federal workforce-development programs and “two-generation” antipoverty initiatives that frame post-secondary credentials as a proven lever for better family health. However, new evidence indicates that out-of-sequence college completion delivers few of the well-documented health dividends normally associated with higher education: in a national longitudinal study, additional schooling after childbirth left mothers’ self-rated health, mental health, and parenting stress essentially unchanged and reduced smoking only for the small minority who progressed all the way to a bachelor’s degree. These findings expose a policy blind spot: current investments that promote maternal college enrollment as a health-improvement strategy are unlikely to narrow health disparities unless they also tackle the structural constraints—time poverty, financial strain, limited social support, and lower labor-market returns—that blunt the payoff of late educational attainment.

**Scientific Research Findings**• Mothers’ postsecondary degree completion after having children does not improve their health, including their self-rated health, mental health, levels of parenting stress, or reports of smoking.
• These patterns were true irrespective of whether mothers completed a vocational, associate’s, or bachelor’s degree and were robust across population groups like White women, first-time mothers, and mothers without high school degrees as well as alternate analysis methods.
• The one exception to this overall pattern was that smoking rates were reduced among mothers who did not have any college schooling and subsequently earned a bachelor’s degree.

**Scientific Research Study Methods**We followed 4,898 mostly lower-educated mothers in the Fragile Families and Child Wellbeing Study from their child’s 1998-2000 birth through six follow-ups spanning 15 years, tracking every new vocational, associate’s, or bachelor’s credential they earned after childbirth. For each wave we measured four outcomes—self-rated overall health, global mental-health distress, parenting stress, and past-month smoking—and fitted random-intercept growth models, then individual fixed-effects models, to estimate within-mother changes in health after degree completion. All specifications controlled for race/ethnicity, age, immigrant status, baseline and time-varying family structure, income-to-needs ratio, employment, insurance coverage, social-support assets, additional births, and residential moves. We stratified analyses by baseline schooling, replicated models with lags, and tested income mediation to probe robustness and heterogeneity.

**Policy Implications**The results of this study point to three important policy considerations. First, policies that aim to reduce health disparities by promoting access to higher education may not benefit the health of all population groups, such as mothers caring for dependent children. Second, policies should reflect knowledge of the challenges faced by mothers pursuing postsecondary college in ways that could help mothers experience more health returns to their degrees. Third, education policies that aim to improve population health may be most efficacious if they target adolescents and young adults and help them to transition from high school to postsecondary schooling and avoid interruptions to their education.