The Effects of Increased Maternal Education on Children’s Academic Outcomes:
Evidence from the ECLS-K
Katherine Magnuson & Hilary Shager
University of Wisconsin-Madison
This draft was prepared for the ITP Spring Training Seminar, April 11, 2008
Please do not cite or circulate without the authors’ permission
Introduction
By pursuing more education, do mothers set their children on better academic courses?
Or do such pursuits do little to alter children’s academic trajectories? Research shows that
children of more highly educated parents enter school with higher levels of academic skills and
continue to perform better than other children (Entwisle & Alexander, 1993; Lee & Burkham,
2002). Social scientists often attribute this achievement gap to the mix of economic and social
advantages afforded by higher levels of parental education, but debates continue on the extent to
which parental education is causally associated with children’s achievement (Sobel, 1998).
Researchers have studied parental education largely as if it were static; yet, it has become
increasingly common for adults to accrue education in a discontinuous fashion, and to extend their
schooling well into adulthood (Astone, Schoen, Ensminger & Rothert, 2000; Jacobs & Stoner-Eby,
1998). Aware of the increasing importance of a skilled labor force, policymakers have created
incentives for adults to return to school; for example, by offering tuition tax credits and publicly
funding adult basic education and job training programs. National estimates suggest that now over
20 percent of adult women pursue some type of education (Rich & Kim, 1999), and about 27 percent
of all female college students are over age 25 (Shin, 2005). Economically disadvantaged women,
and mothers in particular, are especially likely to return to school later in life. Recent studies find
that close to 50 percent of low-income mothers attend school after the birth of their children (Love et
al., 2002; McGroder, Zaslow, Moore & LeMenestrel, 2000; Rich & Kim, 1999).
Nearly 20 years ago Furstenberg, Brooks-Gunn and Morgan (1987) found that some
children born to poor, urban, adolescent mothers seemed to flourish despite experiencing the
same adverse early environments as peers who did not fare so well. Over 50 percent of mothers
in their study had attended school after the birth of their children, and Furstenberg and colleagues
1
argued that this additional education contributed to children’s subsequent scholastic success.
This was a compelling hypothesis, although with a small sample of young mothers, it was largely
speculative. The authors could not conclude that it was the mothers’ education per se that
mattered, rather than other characteristics that were correlated with mothers’ schooling. For
example, perhaps a mother’s academic aptitude both brought her back to school and set her child
on a better academic trajectory from the very beginning.
For those who consider maternal educational attainment as simply a proxy for a set of
individual or family advantages or socioeconomic standing, it may seem as if an incremental
change in a mother’s education would have little bearing on her child’s development. From this
perspective, increases in maternal education would matter only if they bring about large shifts in
family resources, perhaps as a result of improvements in mothers’ job prospects and earnings.
Yet for those who consider maternal education as an important determinant of the quality of the
child’s learning environment, incremental improvements in maternal education may well
influence children’s developmental outcomes, because they create positive changes in children’s
developmental niches (Bronfenbrenner, 1986). Ecological and life course theories emphasize the
interconnection between the experiences of parents and their children, and argue that proximal
environments and interactions that facilitate the intergenerational transmission of achievement
and behavior patterns are dynamic and changeable (Bronfenbrenner, 1986; Elder, 1994;
Macmillan, McMorris, & Kruttschnitt, 2004). To date, studies have primarily focused on links
between changes in parents’ economic resources and children’s outcomes (e.g., Macmillan et al.,
2004). Changes in their parents’ education, however, are also likely to be important (Furstenberg
et al., 1987), especially because maternal education is often the component of socioeconomic
status most strongly associated with children’s achievement (Bornstein, Hahn, Suwalsky, &
2
Haynes, 2003).
How might additional education improve the contexts in which young children are
reared? Research on how educational attainment affects children’s achievement provides some
likely explanations. To the extent that a mother’s additional schooling provides her with positive
learning experiences, as well as increases in basic skills, knowledge, and higher-order thinking, it
may shape her expectations for her children’s education and enable her to create better home
learning environments for her children (Alexander, Entwisle, & Bedinger, 1994; Corwyn &
Bradley, 2003; Davis-Kean, 2005; Halle, Kurtz-Costes, & Mahoney, 1997). Parents with higher
levels of education engage their children in more learning-related activities both in the home
(e.g., reading books) and out of the home (e.g., music or art lessons) (Davis-Kean, 2005). More
highly educated mothers are more verbally responsive to their young children and tend to use
teaching strategies with their children that mimic formal instructional techniques, such as asking
questions and offering feedback rather than issuing directives (Laosa, 1980; Richman, Miller, &
LeVine, 1992; Tracey & Young, 2002). Opportunities for children to engage in learning
activities and the quality of mother-child interactions, particularly verbal interactions, have been
linked to children’s cognitive development and their academic skills (Taylor, Clayton, &
Rowley, 2004). Finally, among school-age children, higher levels of parental education have
also been linked to higher levels of parental involvement in school, which in turn have been
linked to better school outcomes (Brody & Flor, 1998; Hill et al., 2004; Stevenson & Baker,
1987).
Understanding whether changes in mothers’ educational attainment influence children’s
developmental trajectories is important not only because such changes are common, but also
because it may shed light on the extent to which parental education, more generally, influences
3
children. One of the key difficulties in studying the effects of maternal education on children’s
development is that when it is completed before the birth of children, maternal education is likely
to be confounded with other relevant mother and family characteristics that are difficult to
untangle. For example, we might hope to isolate the effects of a mother’s schooling from her
sense of academic motivation, efficacy, or aptitude. However, if a mother’s education is already
completed, it is likely that her educational attainment reflects all of these factors, and yet it is
also likely that all of these factors affect her children’s achievement independent of her
attainment. Thus, researchers end up in the unenviable position of having to over-control or
under-control for these and other family characteristics.
When a mother’s education improves after the birth of her children, this provides a
unique opportunity to estimate the effects of maternal education on children’s development, with
fewer concerns about how to account for confounding factors. By focusing on the effects of
changes in education, many characteristics of mothers and children measured before mothers
attend school can be taken into account. Nevertheless, unobserved characteristics may
differentiate mothers who return to school and those who do not. If the effects of these
unobserved characteristics are constant over time, then change analyses provide a way to reduce
omitted variable bias (NICHD & Duncan, 2003). Thus, the unbiased effects of maternal
education can be identified more clearly than when a mother’s education is completed prior to
the birth of her children, although as with all non-experimental research, concerns about over- or
under-controlling for related characteristics and factors may still remain. Despite the important
advantage such an approach provides, few studies have analyzed the effects of changes in
parents’ education, perhaps because it requires either an experimental manipulation of parental
education or longitudinal observations of both parents’ education and children’s outcomes.
4
In an effort to shed light on the relationship between a mother’s further education and her
child’s school success, three experimental evaluations of large education and job training
interventions for disadvantaged mothers have included a child assessment, two of which
specifically targeted teen mothers (McGroder et al., 2000; Maynard, Nicholson & Rangarajan,
1993; Quint, Bos, & Polit, 1997). In all three studies, mothers assigned to the control group
attended educational programs almost as frequently as those in the experimental group; thus, the
interventions did not boost educational levels by much. In the absence of substantial effects on
mothers’ education, it is not surprising that these programs did not appear to affect children’s
outcomes.
Quasi-experimental studies have proven more informative, suggesting that increases in
maternal education may improve young children’s achievement. However, these studies have
found that effects are concentrated among particularly disadvantaged populations. An analysis of
education mandated as part of a large welfare-to-work intervention fielded during the 1990s
suggests that mothers’ participation in adult basic education improved children’s school
readiness even when mothers’ earnings did not increase (Magnuson, 2003). Estimates suggest
that about 8 months of participation in educational activities when children were between ages 4
and 6 boosted school readiness by about a quarter of a standard deviation by age 6. Using
econometric methods with data from the Maternal and Child Supplement to the National
Longitudinal Survey of Youth (NLSY), Rosenzweig and Wolpin (1994) found that additional
maternal education completed during the first three years of a child’s life improved his or her
later vocabulary and academic skills. A more recent study by Moore and Schmidt (2007), also
using the NLSY data, found that a mother’s enrollment in education during the first three years
of her child’s life was associated with higher math and reading achievement by age 6.
5
Just one previous study has considered whether improvements in maternal education
improved achievement during middle childhood. Also using the NLSY data, Magnuson (2007)
examined whether additional maternal education that was obtained when children were between
the ages of 6 and 12 influenced children’s academic skills. Again, results suggest that positive
effects were only evident among children with young and educationally disadvantaged parents,
and were more pronounced for reading skills than for math skills. In addition, associations were
concentrated among younger children. For example, an additional year of completed education
was associated with a .23 increase in children’s reading skills between ages 6 and 8, whereas no
discernable benefits were found for mothers’ education completed after age 10.
Prior studies have considered the most basic question, whether additional education
improves the achievement of disadvantaged children, with little attention to whether the effects
are heterogeneous. Studies have also suggested that the most likely mechanism for explaining
these effects is an improvement in the quality of children’s home environments, which includes
more enriching learning opportunities and responsive and supportive parenting. It is likely,
however, that not all educational experiences are likely to bring about such positive changes in a
child’s environment. Attending school may be more costly and stressful for some mothers than
for other mothers, and the additional resource costs may reduce the impact of such education on
children’s home environments. In particular, in the current policy context, mothers with very low
incomes or single parents may find meeting demands of being a student, caregiver, and provider
difficult (Polakow, Butler, Deprez, & Kahn, 2004). For these families, it may be that in the short
term additional education does not improve the home learning environment or the achievement
of their children.
6
Although there may be heterogeneity in the effects of additional maternal education in
general, the difficulty of combining parenting and student roles may have gotten more difficult in
recent years by the passage of welfare reform legislation in 1996. Such legislation has greatly
reduced access to cash support for mothers pursuing their education, although some support is
still available for basic and vocational education (Polakow et al., 2004). This may mean that
disadvantaged mothers who are pursuing their education in the post-welfare reform era are
facing greater economic hardship while in school, are more likely to combine their schooling
with working, attend school part-time, or are seeking out a different type of education. To date,
all of the studies have used data collected from the pre-welfare reform era; thus, it is not clear if
the association between maternal education and children’s achievement will be as strong in the
current policy context as prior studies suggest.
Although evidence is accumulating that improvements in maternal education may benefit
children’s academic and language skills, the research literature is still quite sparse. The present
study uses a large longitudinal study, the Early Childhood Longitudinal Study-Kindergarten
Cohort, to consider whether changes in maternal education are associated with concurrent
improvements in children’s math and reading skills. We hypothesize that maternal education will
be linked to higher levels of academic achievement and that this association will be most evident
among mothers with lower levels of initial schooling. In addition, we examine whether increases
in maternal education are associated with reductions in grade retention and receipt of special
education services. The study explores whether there is heterogeneity in the effects of education,
considering subgroups defined by income (low income vs. higher income families) and family
structure (single vs. two-parent families).
7
Study Methods
Data
Data for this study are from the Early Childhood Longitudinal Study-Kindergarten
Cohort (ECLS-K), kindergarten through fifth grade public use files. The ECLS-K, which was
designed and conducted by the National Center for Educational Statistics (NCES), provides a
nationally representative sample of children who attended kindergarten beginning in the fall of
1998. Six waves of the survey are currently available, beginning in the fall of kindergarten and
continuing through the spring of fifth grade. For this study, we use information collected during
the fall of kindergarten (1998) through the spring of first grade (2000).1
Sample
The sample consists of 10,680 children with complete data for fall kindergarten and
spring first grade assessments in reading and math. In addition to non-response, missing
assessment data in the fall of kindergarten occurred because children who spoke a language other
than English or Spanish, or were determined to have significant developmental disabilities, were
not tested (N=378). Children who spoke Spanish were given a reduced version of assessments;
therefore, they are also excluded from our sample (N=866). Missing data in spring of first grade
in part reflects planned attrition; only half of the students who changed schools after the fall of
kindergarten were followed in subsequent waves of the study. Also excluded from the sample
are children whose parents did not complete surveys in the fall of kindergarten (N=775) or the
spring of first grade (N=950), because these are the sources of information about mothers’
education. Finally, an additional 1,507 cases are excluded because we do not have information
1
Current analyses are limited to the kindergarten and first grade waves due to a problem with the collection of data
in round five (spring of third grade), which led to significant measurement error in the variables used to determine
changes in maternal education (Elvira Hausken, NCES, personal communication, July 3, 2007). We are currently
attempting to find alternative ways to identify such changes, in order to incorporate more waves of data in future
iterations of this study.
8
on our key explanatory variables, mothers’ initial level of education or change in education
between the fall of kindergarten and spring of first grade. The primary reason for this type of
missing data was that the child’s mother was not identified as a primary caregiver.2
Characteristics of the sample of children and their families are presented in Table 1.
Although the above exclusions should be noted when interpreting our findings, they
create a consistent sample with which to estimate associations between changes in maternal
education and children’s academic skills. Given that it is likely that we are analyzing a slightly
more advantaged sample, however, we consider the possibility of differential effects for children
of mothers with lower initial levels of education.3
Measures
Academic Outcomes. The primary outcomes of interest in this study are reading and
math skills, assessed in one-on-one testing sessions in the spring of first grade. Scores from
comparable assessments conducted in the fall of kindergarten are used as control variables in
most regressions. These assessments were created specifically for the ECLS-K by a team of
experts, with some items adapted from existing instruments. The reading test included items
measuring knowledge of letters, beginning and ending sounds, word recognition, vocabulary,
and passage comprehension. The math test assessed understanding of numbers, operations,
measurement, patterns, and geometry and spatial relations. NCES reports high reliabilities for all
assessments (Tourangeau, Nord, Lê, Pollack, & Atkins-Burnett, 2006).
2
We impose this restriction because NCES only asked about educational changes for household members identified
as primary caretakers. Thus, if mothers resided in the home, but were not listed as the first or second household
member on the household roster, data was not collected on their education at later waves.
3
The weights constructed by NCES for longitudinal analyses are not appropriate given the criteria we use for
sample selection. However, applying weights does not substantially change the pattern of results reported in the
tables.
9
Math and reading outcomes are transformations of latent Item Response Theory (IRT)
ability scores into standardized t-scores with a mean of 50 and standard deviation of 10 (based on
the full sample distribution).4 These scores are interpreted as children’s performance relative to
their peers, and can be converted to effect sizes by dividing the regression coefficients by 10.
Children in the sample analyzed scored slightly higher than the full ECLS-K sample, suggesting
disproportionate attrition of less-skilled children (see Table 1). Reading and math skills were
highly correlated at both kindergarten (0.77, p<.01) and first grade (0.70, p<.01).
In the spring of 2000, data were collected on the child’s grade placement. If this
information suggested that the child still in kindergarten, rather than progressing directly into
first grade, the child was coded as having been retained. About 3.3 percent of students repeated
kindergarten in the 1999-2000 school year. We do not categorize students who were repeating
kindergarten in the fall of 1998 as having been retained, as they were retained before the time of
this study (and their mothers’ educational increase). We do, however, use such an indicator as a
control in our analyses.
Data from the teacher survey in the spring of first grade also provided information about
whether students received any special educational services. We use this information to create an
indicator of special education receipt. About 4.5 percent of the sample received special education
services in spring of first grade.
Maternal Education. The key independent measure in this study is whether mothers’
educational attainment increased between the fall of their child’s kindergarten year and the
spring of first grade. We identify such changes from responses to a question in the first grade
spring survey that asked whether the child’s mother had completed any additional grades of
4
We use the T-scores because Reardon (2007) suggests that these scores have better properties than the
unstandardized latent IRT scores.
10
school or received any diplomas or degrees. A dummy variable was created indicating the
presence of additional schooling (1=yes, completed additional schooling; 0=no). This measure
captures not only mothers who completed additional degrees, but also those who continued their
education at a particular level (e.g., mothers who were continuing with college but had not yet
finished a bachelor’s degree).
Given that mothers’ educational attainment was likely to be recorded with some error, we
checked the pattern of responses for inconsistencies. In particular, for mothers who indicated
that they had increased their education between rounds, we compared the type of new degree or
additional grade of schooling to the initial level of maternal education composite from round one
of the parent survey (a categorical variable with nine options ranging from eighth grade or below
to doctoral or professional degree). If the new degree information was missing, seemed
unobtainable given the time between survey rounds (e.g., an advanced degree for a mother with
less than a high school degree initially), or was less than the initial level of education, the
variable indicating change in maternal education was set to missing (N=58). Any remaining
error in the measures of mothers’ education will likely attenuate estimates of the effects of
increases in mothers’ educational attainment. Using this method, we identified 519 mothers (5
percent of the sample) whose education increased between their child’s kindergarten and first
grade year.
The type of education mothers pursued varied. Most mothers reported that they had
completed a vocational degree (30%) or some college (22%). Less common was completing an
associate’s degree (16%), a bachelor’s degree (10%), some graduate or professional schooling
11
(10%), or a high school diploma or General Equivalency Degree (10%). Least common was
completing a year of high school without obtaining a degree (less than 1%).5
Covariates. Mothers who pursue additional education are likely to be different from
those who do not. In order to isolate the effects of additional maternal education, we include an
extensive set of controls for child and family background characteristics that might be associated
with both mothers’ decisions to obtain more education and their children’s academic outcomes.
Measures were drawn primarily from surveys of parents in the fall of kindergarten, and include
demographic and family information such as child age; gender; race/ethnicity; birth weight;
height; weight; region of the country; urbanicity; family structure and size; parental education
and employment; income to needs ratio; mother’s immigrant status; mother’s age at birth of
child; language spoken in the home; whether the child was a first-time kindergartener; and
whether the child attended center-based childcare, non-center-based childcare, or Head Start in
the year before kindergarten. 6 Descriptive statistics for the demographic covariates are
presented in Table 1.
Most regressions include an additional set of covariates meant to proxy for the quality of
home learning environment experienced by each child. These include measures of how often the
family participates in activities such as singing songs, building things, and doing arts and crafts.
In addition, several covariates designed to indicate early literacy practices (e.g., the number of
children’s books in the home, how often the child is read to or looks at picture books) are also
included. Appendix Table 1 provides details describing all of the covariates and how they were
constructed.
5
Additional information about their school attendance; for example, whether they attended school full or part-time,
was not collected.
6
Information regarding mother’s immigrant status was available only from the spring of kindergarten parent
questionnaire.
12
Some of the covariates were missing information at the item level, although rates of
missing data are quite low, less than 3 percent for most cases. To retain these cases, relevant
regressors were set to zero, and dummy variables were created to indicate the presence of
missing values (Allison, 2002). For example, for a case missing data on father’s initial level of
education (the most common type of missing data), paternal education was coded as having a
value of zero, and a dummy variable indicating missing father’s education data was created.
This approach yields unbiased estimates of the key independent variables when there are low
rates of missing data (Allison, 2002).
Analytic Models
Given the clustered structure of the data in the ECLS-K (students clustered within
schools), we used hierarchical linear modeling (HLM) to assess the extent to which mothers’
increases in education between fall of kindergarten and the spring of first grade are associated
with children’s reading and math skills at the spring of first grade. HLM is a generalization of
linear regression that accounts for the fact that observations of students’ outcomes within a
school may not be statistically independent (Raudenbush & Bryk, 2002).7 In our HLM models,
fixed effects, which are analogous to standard regression coefficients, are directly estimated at
the individual child level, while allowing for a random intercept term at the school level.8 For
our dichotomous outcomes, grade retention and receipt of special education services, we used
probit regression models, and for ease interpretation, report marginal effects. To account for the
non-independence among students within a school in the probit models, we used clustered
standard errors.
7
In our sample, the intraclass correlation (i.e., the proportion of variation existing between schools) for first grade
spring reading scores was 0.21, and for math scores was 0.19, suggesting that HLM is an appropriate analytic
strategy. The 10,680 students in the sample attended 910 different schools.
8
As a robustness check, we also ran OLS models, clustering standard errors at the school level. These models
produced estimates that were very similar to the HLM results.
13
The primary challenge of our regression approaches is to ensure that we account for the
variety of ways in which mothers who go back to school after the birth of their children may
differ from mothers who do not. First, descriptive statistics were used to explore the extent to
which mothers whose education increased differed from mothers whose education did not
change. If our statistical models did not account for these possible differences, we may risk
attributing to mothers’ improved education benefits that reflect differing child or family
background characteristics.
Next, we regressed children’s academic skills on an indicator of whether mothers’
education had increased. We adjusted for initial level of skill achievement in reading and math
as well as background differences using the rich data collected in the ECLS-K study. These data
enabled us to statistically control for many preexisting differences that may explain the
association between maternal education and children’s achievement. Unobserved characteristics
may still result in omitted variable biases. Four separate estimation models with differing levels
of controls were conducted for each outcome. The first regression model included only the
indicator for whether a mother had increased her education. In the second model, children’s
achievement in reading and math during the fall of kindergarten was entered. In the third model,
a set of child, mother, and family controls were included. In the fourth model, measures of the
child’s home learning environment were added. By parsing out any association between
mothers’ education and initial levels of achievement and family characteristics, this fourth
regression model is particularly powerful in controlling for earlier differences in family contexts
and children’s development. We do not wish to control for co-occurring changes that result from
an improvement in mothers’ education, as these may reflect the mechanism by which maternal
14
education influences children’s development. If, however, there are spurious and simultaneous
changes that are not due to mothers’ education, these may bias our results.
First, we estimated regressions among the full sample of children. Second, because
previous research has suggested that the effects of incremental changes in maternal education are
larger for mothers who have initially low levels of education, we also estimated these models for
the subgroup of mothers who reported having a vocational degree or less education (N=4,640)
and for those who had completed at least some college (N=6,040). We expected that the
associations would be larger for mothers with lower levels of education. Finally, among the lesseducated families, we explored whether some subgroups of children were more or less likely to
benefit from their mothers’ increased schooling. We considered low-income families (those with
household incomes <=1.5 of the federal poverty threshold) and single parent families.
Even holding constant a large set of observed characteristics may fail to appropriately
estimate maternal education effects if the children whose mothers complete additional schooling
differ greatly from comparison children. For example, regression estimates may be biased if
there is insufficient overlap in distribution of observed characteristics, and the models are forced
to extrapolate beyond the data. Our HLM models also impose assumptions about the linearity
and additivity of regressors that are difficult to test with many covariates.
Selecting an appropriate comparison group through propensity score matching offers an
alternative way to obtain comparable samples and requires fewer assumptions about the
“correct” functional form than regression approaches. Our propensity score analysis proceeds in
two steps. First, we estimate a propensity score for each individual as the conditional probability
(from a probit model) of having a mother whose education increases given the full set of
covariates.
15
The propensity score is next used to create a matched control group of children whose
mothers did not complete additional schooling. 9 We use the nearest neighbor matching
technique and limit the sample to children for whom there is sufficient overlap in propensity
scores between the maternal education and comparison group (the area of common support).10 If
the matching process proceeds correctly, the treatment and control children will have similar
measured characteristics, and the effects of increased maternal education can be estimated by
regression methods, which again include controls for all covariates. 11 Because propensity score
techniques match cases on measured characteristics, unobserved differences between children
whose mothers completed additional schooling and other children remain a possible source of
bias in these analyses.
Results
We begin by considering how mothers who pursue additional education after the birth of
their children differ from mothers who do not. Prior research has indicated that mothers who
attend school have more to gain from it than mothers who do not, and this appears to be the case
in these data as well (Table 1). Mothers who completed additional education have lower
household incomes, are more likely to have been teen parents, and less likely to live with the
biological father of their children compared with other mothers. They also have partners with
lower levels of education. These differences suggest that mothers who pursued education after
the birth of their children are disadvantaged compared with mothers who do not attend school.
9
We include a continuous measure of family income-to-needs.
We use STATA8’s shareware psmatch2 program to conduct the propensity score analysis, specify a caliper width
of .01, and allow for “replacement” (or the ability for a comparison case to serve as a control more than once).
11
We use OLS regressions for achievement, because with the reduced sample size children are not sufficiently
clustered within schools to estimate a random effect at the school level. We use probit models for grade retention
and special education placement regressions. All regressions include clustered standard errors to account for nonindependence within the school.
10
16
Given these disparities, it is not surprising that their children also have lower levels of
achievement. Splitting the sample by prior levels of education reduces the disparities, but it is
notable that mothers who attend school are still significantly more disadvantaged than those who
do not.
Understanding how mothers who increase their education differ from those who do not
provides some insight into how we might expect their children to fare. Among the full sample, if
mothers who complete additional schooling are relatively disadvantaged, then it is likely that
their children may not fare as well as other children. Means presented in Table 1 indicate that
on, average, children who have mothers whose education increased between the fall of
kindergarten and the spring of first grade performed significantly less well on both the
kindergarten and the first grade achievement tests. Although children of mothers’ whose
education increased had lower levels of special education and grade retention, this difference did
not reach statistical significance.
Results from the HLM regression models, which include a school level random error,
however, suggest no difference in the reading and math skills of children whose mothers
completed additional schooling compared with other children (Table 2). Adding in controls for
children’s earlier achievement scores results in positive coefficients for maternal education,
although the coefficients are small and not significant. Additional controls for family
background differences further increase the maternal education coefficients, to the point of
marginal significance for reading only (columns 3 and 4 of Table 2).
Results of the propensity score analysis are displayed in column 5 of Table 2. The goal of
this approach is to construct a comparison sample that is matched to the treatment group on the
predicted likelihood of having a mother who returns to school, and also on all regressors.
17
Consequently, one key step is to ensure that the observable characteristics of the comparison
group (created in the first step) do not differ from those of the maternal education group. Our
check for such balance confirmed that there were no differences in the mean level of covariates
across the two groups.12 Satisfied that our matching resulted in an appropriate comparison
group, we continued to the second stage of the propensity score analysis. The resulting
regression based propensity score coefficient estimates are very similar to those from the HLM
analyses. This suggests that our HLM results were not biased by using an inappropriate
comparison group.
The results from our probit models, which estimate marginal changes in the probability of
grade retention and receipt of special education services, are presented in Table 3. Although the
negative coefficients suggest a potential association between maternal education and reductions
in grade retention and receipt of special education services, they do not reach levels of statistical
significance.13
We next consider whether the effects differ by mothers’ prior level of education, by
looking at the association between increased maternal education and children’s achievement
within more and less-educated mother subgroups. First, taking children of mothers with a
vocational degree or less education (top panel of Table 4), we find that without controls there
appears to be no benefit of increases in maternal education. However, after controls are included
for both prior achievement and family characteristics, there is a positive effect of increases in
mother’s education on children’s reading achievement (effect size of .10). Moreover, the
12
We used a Hotelling T2 test for the joint equality of covariate means, conducted for 5 groups stratified on the
propensity scores, which further verified balance on the covariates across the additional maternal education and
comparison group. Balance was achieved for the full sample, as well as each of the education subgroups we
consider.
13
We did not estimate propensity score models for these outcomes, because the reduced sample size and low
frequency of grade retention and special education placement limited the ability of these models to converge.
18
propensity score model, which also includes a full set of covariates, yields a somewhat larger
estimate than the HLM models (effect size of .15). In the case of math achievement, the
estimated effects are again positive after controls are added, but do not reach statistical
significance in the HLM and propensity score models (effect sizes of .07 and .10, respectively).
Among children of mothers who have more than a vocational degree in the fall of
kindergarten, children whose mothers complete additional education have considerably lower
achievement than their peers whose mothers did not attend school (Table 4, panel 2). Although
including controls reduces these disparities, both the HLM and propensity score results suggest
there is no apparent association between mothers’ continued schooling and children’s reading or
math achievement.
The associations between additional maternal schooling and children’s grade retention
and special education placement also differ by mothers’ prior level of education. For both
outcomes, coefficients for children of mothers with lower initial levels of education are
consistently negative and larger, compared to the coefficients for children whose mothers had
higher initial levels of education (Table 5). Again, however, most coefficients fail to reach
statistical significance.
Next, we look at variation within the subgroup of mothers with low levels of education.14
The results presented in Table 6 come from HLM models with a full set of covariates (Model 4
in Tables 2 through 5). Results for all of the low-educated mothers are repeated in column 1 of
Table 6 for comparative purposes. First, we consider the association between increased maternal
education among families that differ in terms of their household incomes (low income vs. higher
income) as well as their family structure (single parent vs. two parent families). Ideally, we
14
We have also considered these subgroups among the more highly educated mothers (mothers with at least some
college) and find that there is no discernable association among any of these groups.
19
would distinguish between subgroups based on characteristics of families before mothers
attended school. Given that family structure is measured in the fall of kindergarten, it is likely
exogenous to mothers’ schooling. However, the income measure was not collected until the
spring of kindergarten, and thus it is possible that household incomes may have been affected by
mothers’ school attendance. For this reason, this subgroup split provides only a rough guideline
about how the effects of maternal education may differ across families with differing levels of
economic resources.
With respect to reading, the pattern of associations is remarkably consistent in suggesting
that the association between maternal education and achievement appear to be more pronounced
among more advantaged families with low levels of education. Among those families who have
low incomes (income-to-needs ratio less than or equal to 1.5 of the federal poverty threshold),
there is little detectable effect of increased maternal education; whereas among those with higher
income-to-needs ratios, the association appears to be more substantial (.04 versus .16 effect
sizes). Likewise, the association is negative (-.06 effect size), albeit imprecisely estimated,
among single parent families, but positive and larger among two parent families (.16 effect size).
In terms of math, we find that there is less consistent evidence of a differential pattern of
effects by family income. However, it seems that a positive association emerges among twoparent families (effect size of .13), whereas the association is negative (although again
imprecisely estimated) among single parent families.
With respect to grade retention and special education, coefficient patterns for groups
defined by income and family structure are less consistent (Table 7). Negative coefficients,
suggesting an association between increased maternal education and reductions in grade
retention and special education receipt, are present for both the low- and higher-income sub-
20
groups, although they do not reach statistical significance. The results for the single and not
single parent sub-groups are more consistent with the achievement findings. For non-single
parent families, increased maternal education is associated with a marginally significant 0.8
percentage point reduction in the probability of being retained.
Discussion
Consistent with prior research, these preliminary results suggest that children of mothers
with lower levels of education appear to have improved reading skills when their mothers’
education improves. The findings indicate no such association among more highly educated
mothers. In addition, there is less improvement in children’s math skills. These results appear to
be robust across HLM and propensity score methods. We also find some evidence of a possible
trend linking increases in mothers’ education to reductions in the probability of receipt of special
education services and grade retention among children of mothers with lower initial levels of
education.
The stronger association between mothers’ increased education and children’s reading
skills compared with math skills replicates findings from prior studies. The explanation for such
a pattern of effects, however, is elusive. There are several possible reasons for this difference.
Young children’s calculation skills may not be sensitive to variations in their social background
including parental education, once differences in verbal skills are taken into account (Entwisle &
Alexander, 1990; Ginsberg & Russell, 1981; Jordan et al., 1992). Some research has suggested
that social class differences in verbal and literacy learning opportunities may be greater than
differences in math learning opportunities (Jordan et al., 1992; Kellaghan, 1997; Saxe,
Guberman & Gearhart, 1987), and that math skills and concepts are primarily learned through
formal instruction in school (Entwisle & Alexander, 1992). Alternatively, if mothers are more
21
likely to undertake educational activities that bolster their language and literacy skills rather than
mathematical skills, then this might also explain this pattern of effects.
The magnitude of the association between improvements in maternal education and
children’s reading skills is quite modest, with estimated effect sizes ranging from .10 to .16.
These estimates correspond remarkably well to the effect of an additional year of parental
education on children’s achievement suggested by prior rigorous cross sectional studies. These
findings, however, are just slightly smaller than estimates derived from prior longitudinal studies
of changes in mothers’ educational attainment, which have typically estimated effect sizes of
about 0.20 for children of a similar age (Magnuson, 2007; Magnuson et al., 2007).
One explanation for the slightly lower estimates in this study compared with prior studies
is that the policy context may differ. The current study is conducted after welfare reform, which
may have limited poor women’s access to welfare benefits while they attend school (Covington
& Spriggs, 2004; Jacobs & Winslow, 2003). Prior studies draw their data from the pre-welfare
reform era in which economic supports were available to mothers pursuing additional education
and less emphasis was placed on quickly transitioning mothers into the labor market. Such
policies may have reduced the benefit of maternal education by making it more difficult and
costly for low-income mothers to attend school. This, in turn, may have changed the type of
education that mothers pursued, reducing attendance in higher educational institutions in
particular, and increasing vocational education (Polakow et al., 2004). Also, it may have
increased the likelihood that families faced financial hardship, or mothers were combining
school and work.
A unique contribution of this study is its investigation of the relationship between
increases in maternal education and other important school outcomes. Evidence suggests that for
22
children of non-single mothers with lower initial levels of education, increases in maternal
schooling are associated with a reduction in the probability of grade retention. The low
frequency of both grade retention and special education makes changes in these outcomes
difficult to detect, yet many of the results hovered near marginal significance. We will continue
our work with these outcomes, and if these analyses hold up in additional work, the findings are
promising, given the mounting evidence of long-term negative impacts of retention on children
(Hauser, 2000), as well as the societal financial benefits to be realized from reductions in
remediation and unnecessary special education service provision (Reynolds et al., 2001).
Unlike prior studies, this study considered whether effects were homogenous across the
sample of less educated mothers, and found that the pattern of associations appeared to differ by
household income and family structure. However, with information on family income collected
in the spring of kindergarten, we caution that family income may in part be endogenous.
Nevertheless, in both cases, the estimated association between increased maternal education and
achievement appear to be slightly stronger among the more advantaged families (non-low
income and two parent families). Why? We speculate that the answer hinges on the processes
that are operating to improve children’s achievement. The main theoretical explanation for
improved achievement is that additional maternal education results in improved home learning
environments, including enhanced literacy and learning opportunities and activities. Certainly,
parents with lower levels of education are more likely to have more to gain from additional
education, and yet for parents with few supports or resources, additional education may be less
likely to translate into changes in their children’s home environments. Without support or
assistance, low-income and single mothers may find combining school and family
responsibilities financially difficult and psychologically stressful. Thus, we hypothesize that in
23
the current policy context, for low-income and single parents, the costs of schooling may eclipse
the benefits, at least in the short term, and may do little to promote their children’s achievement.
Another explanation may be the type of schooling that mothers in these two groups
experience. Low-income and single mothers were more likely to complete a high school degree
or GED than their more advantaged peers. Recent research has suggested that completing a GED
may do little to promote skills, and thus has little payoff in the labor market (Heckman &
LaFontaine, 2006). If this type of education is also less beneficial than other types of schooling,
then this might explain why there is less overall association between increases in maternal
education and children’s achievement among the more disadvantaged mothers. We intend to
explore these possible explanations in future work.
There are several important limitations to this preliminary work. First, it is important to
recognize that these analyses are non-experimental and may not identify causal associations. The
analytic approaches employed attempt to reduce omitted and third variable bias by controlling
not only for important demographic and family characteristics, but also for children’s prior
achievement. Nevertheless, it is possible that bias remains.
Second, although this study goes beyond the usual investigation of children’s academic
achievement and includes alternative outcome measures such as grade retention and receipt of
special education services, other indicators of school success should be considered. For
example, future research should explore whether increases in maternal education predict
disciplinary actions and engagement with school. Third, our analyses only consider children’s
achievement at the spring of first grade. We very much would like to know whether any positive
benefits persist beyond first grade. With ECLS-K data on children’s later achievement, we hope
to answer this question in future work. Fourth, we plan to explore whether particular types of
24
education are more or less strongly associated with children’s achievement outcomes. Finally,
states vary considerably in the extent to which they allow or encourage mothers to pursue
education through their welfare policies. We hope to exploit such state variation to estimate
instrumental variable models of the effects of additional education on children’s achievement.
Such an approach, if it can be well implemented, may improve our confidence in inferring
causality.
In summary, these findings replicate prior studies that have found that improvements in
mothers’ education appear to be linked to better reading achievement among their children. In
addition, increasing maternal education may result in reduced probabilities of grade retention and
receipt of special education services. These associations, however, are modest and limited to a
sample of less educated mothers.
25
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33
Table 1: Means and Standard Deviations for Demographic Control Variables and Child Academic Outcomes
Min
Child Gender
Male (1=Male, 0=Female)
Race/Ethnicity
White, Non-Hispanic (Referent)
African American
Hispanic
Asian
American Indian
Other race/ethnicity
Child Age, First Grade Spring (years)
First Time Kindergartener
Census Region
West (Referent)
Northeast
Midwest
South
Urbanicity
Rural (Referent)
Urban Fringe or Large Town
Central City
Low Birth Weight (<=2500 grams)
Number of Children in Household
Family Structure
2 Biological Parents (Referent)
1 Biological, 1 Step Parent
Single Parent
Adoptive or Foster Parents
Non-English Speaking Household
Mother Not Born in US
Mother <20 Years Old at Child's Birth
Received WIC Benefits
All (N=10,680)
Max Mean
SD
Change in
Maternal
Education
(N=519)
Mean
SD
No Change in
Maternal
Education
(N=10,161)
Mean
SD
0
1
.50
.50
.52
.50
.50
.50
0
0
0
0
0
0
6
0
1
1
1
1
1
1
8
1
.66
.13
.12
.04
.02
.04
7.25
.96
.47
.33
.32
.19
.13
.20
.35
.20
.54
.19
.14
.03
.04
.06
7.22
.96
.50
.40
.35
.17
.20
.23
.33
.19
.67
.12
.12
.04
.02
.04
7.25
.96
.47
.33
.32
.19
.13
.20
.35
.20
0
0
0
0
1
1
1
1
.20
.20
.28
.32
.40
.40
.45
.47
.18
.18
.29
.35
.39
.38
.46
.48
.20
.20
.28
.32
.40
.40
.45
.47
0
0
0
0
1
1
1
1
1
11
.24
.40
.37
.07
2.45
.42
.49
.48
.26
1.08
.25
.36
.40
.10
2.45
.43
.48
.49
.30
1.19
.23
.40
.37
.07
2.45
.42
.49
.48
.26
1.07
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
.71
.07
.19
.02
.06
.11
.10
.32
.45
.26
.39
.15
.24
.31
.31
.47
.60
.11
.29
.01
.08
.13
.18
.49
.49
.31
.45
.11
.28
.33
.39
.50
.72
.07
.19
.02
.06
.11
.10
.31
.45
.26
.39
.15
.23
.31
.30
.46
Sig. Diff.
a, b, c
a, b, c
a,c
c
a, b
a, b
a, b, c
a, c
a, b, c
a, c
c
a, b, c
a, b, c
Table 1 Continued: Means and Standard Deviations for Demographic Control Variables and Child Academic Outcomes
Min
Mother's Employment
Not Employed (Referent)
Employed Full-time
Employed Part-time
Father's Employment
Not Employed (Referent)
Employed Full-time
Employed Part-time
Mother's Education
Vocational/Technical Degree or Less
Some College or More
Father's Education
Vocational/Technical Degree or Less
Some College or More
Income to Needs Ratio
All (N=10,680)
Max Mean
SD
Change in
Maternal
Education
(N=519)
Mean
SD
No Change in
Maternal
Education
(N=10,161)
Mean
SD
0
0
0
1
1
1
.30
.46
.24
.46
.50
.43
.29
.46
.25
.45
.50
.44
.30
.46
.24
.46
.50
.43
0
0
0
1
1
1
.04
.93
.03
.20
.26
.17
.05
.92
.03
.23
.27
.16
.04
.93
.03
.20
.26
.17
0
0
1
1
.43
.57
.50
.50
.41
.59
.49
.49
.44
.56
.50
.50
0
0
0
1
1
59.88
.37
.45
3.30
.48
.50
3.21
.41
.33
2.45
.49
.47
2.13
.37
.46
3.35
.48
.50
3.25
Sig. Diff.
c
a, b, c
a, b, c
Child Academic Outcomes
Reading, Kindergarten Fall
22.09 95.14 51.39 10.03
49.86 9.29
51.46 10.06
a, c
Math, Kindergarten Fall
18.62 96.25 52.14
9.71
50.57 9.09
52.22
9.74
a, b, c
Reading, First Grade Spring
6.63
80.29 51.72
9.16
50.84 8.87
51.76
9.17
a, c
Math, First Grade Spring
2.68
78.1
51.74
9.24
50.57 8.90
51.78
9.25
a, c
Child Retained btwn. K and 1st Grade
0
1
.03
.18
.03
.16
.03
.18
Child Received Special Ed Services
0
1
.05
.21
.04
.19
.05
.21
NOTE: Significant difference between mothers who change education and those who do not is indicated by the following letters: a=difference among
whole sample; b=difference among mothers with vocational/technical degree or less education; c=difference among mothers with some college or more
education. For continuous outcomes, mean differences between mothers with changes and education and those with no change were determined
through t-tests (p<0.05). For categorical (dichotomous) outcomes, differences between frequencies for the two groups were determined through chisquare tests (p<0.05).
Table 2: Summary of Results from Regressions of Changes in Maternal Education on Children's
Reading and Math Skills in Spring of First Grade, Full Sample
Reading
Change in Maternal Education
(K-Fall to 1st Grade-Spring)
Model 1
HLM
Model 2
HLM
Model 3
HLM
Model 4
HLM
Model 5
Propensity
0.07
(0.38)
0.37
(0.28)
0.43
(0.27)
0.46t
(0.27)
0.67 t
(0.41)
X
X
X
X
X
X
X
X
X
Model 4
HLM
Model 5
Propensity
K-Fall Test Scores
Demographic Variables
Home Learning Variables
Model 1
HLM
Change in Maternal Education
(K-Fall to 1st Grade-Spring)
-0.17
(0.39)
K-Fall Test Scores
Demographic Variables
Home Learning Variables
t
Math
Model 2
Model 3
HLM
HLM
0.18
(0.28)
0.32
(0.28)
0.32
(0.28)
.32
(0.41)
X
X
X
X
X
X
X
X
X
NOTE: ** p<0.01, * p<0.05; p<0.10; Standard errors in parentheses. Missing data dummies
included in models 3 and 4. The sample size for the HLM models is 10,680 and for the propensity
models is 1,023.
Table 3: Summary of Results from Regressions of Changes in Maternal Education on
Children's Grade Retention and Receipt of Special Education Services in Spring of
First Grade, Full Sample
Grade Retention
Change in Maternal Education
(K-Fall to 1st Grade-Spring)
Model 1
Probit
Model 2
Probit
Model 3
Probit
Model 4
Probit
-0.007
(0.007)
-0.008
(0.005)
-0.004
(0.003)
-0.005
(0.003)
X
X
X
X
X
X
10,634
10,626
10,570
K-Fall Test Scores
Demographic Variables
Home Learning Variables
N
Change in Maternal Education
(K-Fall to 1st Grade-Spring)
K-Fall Test Scores
Demographic Variables
Home Learning Variables
10,634
Model 1
Probit
Special Education
Model 2
Model 3
Probit
Probit
Model 4
Probit
-0.009
(0.008)
-0.011t
(0.006)
-0.006
(0.004)
-0.005
(0.005)
X
X
X
X
X
X
N
10,554
10,554
10,554
10,504
t
NOTE: ** p<0.01, * p<0.05; p<0.10; Robust standard errors in parentheses,
clustered by school. Missing data dummies included in models 3 and 4.
Table 4: Summary of Results from Regressions of Changes in Maternal Education on Children's Reading and Math Skills in Spring of First Grade,
by Initial Level of Maternal Education
Mothers with a Vocational/Technical Degree or Less Education
Model 1
HLM
Change in Maternal Education
(K-Fall to 1st Grade-Spring)
0.57
(0.64)
K-Fall Test Scores
Demographic Variables
Reading
Model 2 Model 4
HLM
HLM
Model 5
Propensity
Model 1
HLM
-0.04
(0.65)
0.87t
(0.47)
0.97*
(0.46)
1.45*
(0.68)
X
X
X
X
X
X
X
Home Learning Variables
Math
Model 2 Model 4
HLM
HLM
Model 5
Propensity
0.41
(0.47)
0.73
(0.47)
0.99
(.70)
X
X
X
X
X
X
X
Mothers with Some College or More Education
Model 1
HLM
Change in Maternal Education
(K-Fall to 1st Grade-Spring)
K-Fall Test Scores
Demographic Variables
Home Learning Variables
-1.07
(0.47)
Reading
Model 2 Model 4
HLM
HLM
Math
Model 4
HLM
Model 5
Propensity
Model 1
HLM
Model 2
HLM
-1.06
(0.47)
-0.16
(0.34)
-0.03
(0.34)
0.24
(.52)
X
X
X
X
X
X
X
-0.17
(0.34)
-0.05
(0.34)
0.24
(.52)
X
X
X
X
X
X
X
Model 5
Propensity
NOTE: ** p<0.01, * p<0.05, t p<0.10; Standard errors in parentheses. Missing data dummies included in model 4. The sample size for the first panel
(mothers with a vocational degree or less education) for the HLM models is 4,640 and for the propensity score is 412. The sample size in the second
panel (mothers with some college or more education) for the HLM models is 6,040 and for the propensity score models is 603.
Table 5: Summary of Results from Regressions of Changes in Maternal Education on Children's Grade Retention
and Receipt of Special Education Services in Spring of First Grade, by Initial Level of Maternal Education
Mothers with a Vocational/Technical Degree or Less Education
Change in Maternal Education
(K-Fall to 1st Grade-Spring)
Grade Retention
Model 1 Model 2 Model 4
Probit
Probit
Probit
Special Education
Model 1
Model 2 Model 4
Probit
Probit
Probit
-0.012
(0.011)
-0.028
(0.015)
K-Fall Test Scores
Demographic Variables
Home Learning Variables
N
4,617
-0.010
(0.007)
-0.007
(0.003)
X
X
X
X
4,617
4,493
4,595
-0.027t
(0.011)
-0.015
(0.008)
X
X
X
X
4,595
4,566
Mothers with Some College or More Education
Model 1
Probit
Change in Maternal Education
(K-Fall to 1st Grade-Spring)
-0.003
(0.009)
K-Fall Test Scores
Demographic Variables
Grade Retention
Model 2 Model 4
Probit
Probit
-0.007
(0.006)
-0.002
(0.003)
X
X
X
Home Learning Variables
N
Special Education
Model 1
Model 2 Model 4
Probit
Probit
Probit
0.006
(0.011)
-0.001
(0.007)
0.002
(0.005)
X
X
X
X
6,017
6,017
5,964
X
5,959
5,959
NOTE: ** p<0.01, * p<0.05, t p<0.10; Standard errors in parentheses, clustered by school. Missing data
dummies included in model 4.
5,870
Table 6: Summary of Results from Regressions of Changes in Maternal Education on Children's Reading and Math Skills in Spring of
First Grade, for Mothers with a Vocational/Technical Degree or Less Education
Reading
All Mothers with
Vo/Tech Degree
or Less Educ.
(N=4,640)
Inc. to Needs
<=1.5
(N=1,945)
Inc. to Needs
>1.5
(N=2,695)
Single
Parent
(N=1,185)
Not Single
Parent
(N=3,455)
Model 4
HLM
Model 4
HLM
Model 4
HLM
Model 4
HLM
Model 4
HLM
.97*
(0.46)
0.37
(0.67)
1.64*
(0.67)
-0.59
(0.87)
1.64**
(0.56)
K-Fall Test Scores
Demographic Variables
X
X
X
X
X
X
X
X
X
X
Home Learning Variables
X
X
X
X
X
All Mothers with
Vo/Tech Degree
or Less Educ.
(N=4,640)
Model 4
HLM
Inc. to Needs
<=1.5
(N=1,945)
Model 4
HLM
Inc. to Needs
>1.5
(N=2,695)
Model 4
HLM
Single
Parent
(N=1,185)
Model 4
HLM
Not Single
Parent
(N=3,455)
Model 4
HLM
0.73
(0.47)
0.61
(0.67)
0.85
(0.68)
-0.37
(0.85)
1.25*
(0.57)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Change in Education
(K-Fall to 1st Grade-Spring)
Math
Change in Education
(K-Fall to 1st Grade-Spring)
K-Fall Test Scores
Demographic Variables
Home Learning Variables
NOTE: ** p<0.01, * p<0.05, t p<0.10; Standard errors in parentheses. Missing data dummies included in model 4.
Table 7 : Summary of Results from Regressions of Changes in Maternal Education on Children's Grade Retention and Receipt of
Special Education Services in Spring of First Grade, for Mothers with a Vocational/Technical Degree or Less Education
Grade Retention
Change in Education
(K-Fall to 1st Grade-Spring)
K-Fall Test Scores
Demographic Variables
Home Learning Variables
N
Change in Education
(K-Fall to 1st Grade-Spring)
All Mothers with
Vo/Tech Degree or
Less Educ.
(Low Ed.)
Model 4
Probit
Low Ed
Inc. to Needs
<=1.5
Model 4
Probit
Low Ed
Inc. to
Needs
>1.5
Model 4
Probit
Low Ed
Single
Parent
Model 4
Probit
Low Ed
Not Single
Parent
Model 4
Probit
-0.007
(0.003)
-0.009
(0.005)
-0.003
(0.002)
0.001
(0.005)
-0.008t
(0.003)
X
X
X
4,493
X
X
X
X
X
X
X
X
X
1,841
2,422
1,026
Receipt of Special Education Services
X
X
X
3,330
All Mothers with
Vo/Tech Degree or
Less Educ.
(Low Ed.)
Model 4
Probit
Low Ed.
Inc. to Needs
<=1.5
Model 4
Probit
Low Ed.
Inc. to
Needs
>1.5
Model 4
Probit
Low Ed.
Single
Parent
Model 4
Probit
Low Ed.
Not Single
Parent
Model 4
Probit
-0.015
(0.008)
-0.026
(0.012)
-0.006
(0.011)
-0.003
(0.008)
-0.020
(0.009)
K-Fall Test Scores
X
X
X
X
X
Demographic Variables
X
X
X
X
X
Home Learning Variables
X
X
X
X
X
N
4,566
1,820
2,630
1,049
3,354
NOTE: ** p<0.01, * p<0.05, t p<0.10; Robust standard errors in parentheses, clustered by school. Missing data dummies
included in Model 4.
Appendix Table 1: Definitions, Additional Details, and notes about Covariates Used in Analyses
Constructs and Variables
Definition, Details, and Notes
Child Characteristics
Continuous variable. Child age in years, 1st grade spring.
Dummy variable (boy=1).
Two dummy variables for: <1500 grams, 1500-2500 grams.
Average of two interviewer assessed measurements in lbs.
Average of two interviewer assessed measurements in inches.
Black, Hispanic, Native American, Asian, Other race. Omitted reference
group is White, non-Hispanic.
Dummy variable
Child age
Child gender
Birth weight
Child weight
Child height
Race and ethnicity
First time kindergartener
Parental Reports of Family Characteristics
Number of children in household
Family structure
Urbanicity
Region of country
Early maternal employment
Father’s and mother’s education
Non-English speaking household
Teen mother
Mother’s immigrant status
Father’s and mother’s employment
WIC participation
Childcare the year before kindergarten
Household income-to-needs ratio
Ordinal variable. Ranges from 1 to 11.
Single parent (one biological parent), blended family (one biological and
one non-biological parent), adopted or foster parents. Three dummy
variables. Omitted reference group is two biological parents.
Residence in central city or large town. Two dummy variables. Omitted
reference group is rural residence.
Northeast, Midwest, South. Three dummy variables. Omitted reference
group is West.
Mother ever employed between child’s birth and entry into kindergarten.
Dummy variable.
Less than high school degree through advanced post-graduate degree.
Five dummy variables for each parent. Omitted reference group is some
college.
Home language of child non-English. Dummy variable.
Mother’s age-child’s age <20. Dummy variable.
Mother not born in the United States. Dummy variable measured at
kindergarten spring.
Full-time (35 or more hours per week), part-time work (fewer than 35
hours per week). Two dummy variables for each parent. Omitted
reference group is no work.
Mother or child ever participated in Women, Infants, and Children
nutritional supplement program. Dummy variable measured at
kindergarten fall.
Child received center-based care, non-center based care (from relative or
non-relative), or participated in Head Start. Three dummy variables.
Household income compared to federal poverty threshold. Nine dummy
variables. Omitted reference group is household income-to-needs ratio
greater than 4.5.
Parental Reports of Home Learning Activities
Home learning activities
Number of children’s books in home
Reading outside of school
Frequency of parent engaging the child in 7 activities: building things,
teaching about nature, playing sports, doing art, doing chores, singing
songs, playing games. Responses range from 1 (not at all) to 4
(every day).
Ordinal variable. Ranges from 0 to 200.
Frequency of child looking at picture books and reading outside of school.
Two variables with responses ranging from 1 (never) to 4 (every day).