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Family Income, School Attendance, and Academic Achievement in Elementary School

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Abstract

Low family income is associated with poor academic achievement among children. Higher rates of school absence and tardiness may be one mechanism through which low family income impacts children's academic success. This study examines relations between family income, as measured by receipt of free or reduced-price lunch, school attendance, and academic achievement among a diverse sample of children from kindergarten to 4th grade (N = 35,419) using both random and within-child fixed-effects models. Generally, results suggest that the receipt of free or reduced-price lunch and duration of receipt have small but positive associations with school absences and tardies. Poor attendance patterns predict poorer grades, with absences more associated with grades than tardies. Given the small associations between receipt of free or reduced-price lunch and school attendance, and between the duration of receipt of free or reduced-price lunch and children's grades, results do not provide strong evidence that absences and tardies meaningfully attenuate relations between the duration of low family income and student achievement; poorer attendance and persistent low income independently predict poorer grades. Implications for policy and future research are discussed. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
Developmental Psychology
Family Income, School Attendance, and Academic
Achievement in Elementary School
Taryn W. Morrissey, Lindsey Hutchison, and Adam Winsler
Online First Publication, August 5, 2013. doi: 10.1037/a0033848
CITATION
Morrissey, T. W., Hutchison, L., & Winsler, A. (2013, August 5). Family Income, School
Attendance, and Academic Achievement in Elementary School. Developmental Psychology.
Advance online publication. doi: 10.1037/a0033848
Family Income, School Attendance, and Academic Achievement in
Elementary School
Taryn W. Morrissey
American University
Lindsey Hutchison and Adam Winsler
George Mason University
Low family income is associated with poor academic achievement among children. Higher rates of
school absence and tardiness may be one mechanism through which low family income impacts
children’s academic success. This study examines relations between family income, as measured by
receipt of free or reduced-price lunch, school attendance, and academic achievement among a diverse
sample of children from kindergarten to 4th grade (N 35,419) using both random and within-child
fixed-effects models. Generally, results suggest that the receipt of free or reduced-price lunch and
duration of receipt have small but positive associations with school absences and tardies. Poor attendance
patterns predict poorer grades, with absences more associated with grades than tardies. Given the small
associations between receipt of free or reduced-price lunch and school attendance, and between the
duration of receipt of free or reduced-price lunch and children’s grades, results do not provide strong
evidence that absences and tardies meaningfully attenuate relations between the duration of low family
income and student achievement; poorer attendance and persistent low income independently predict
poorer grades. Implications for policy and future research are discussed.
Keywords: academic achievement, school attendance, family income
Family income during childhood has substantial impacts on
academic achievement (Duncan, Ziol-Guest, & Kalil, 2010).
The achievement gap between children living in low-income
families and those in more well-off families begins before
kindergarten, and widens with age (Duncan & Magnuson, 2005;
Heckman, 2006; Magnuson & Duncan, 2006). One possible
mechanism underlying relations between family income and stu-
dent achievement is school attendance. Children who miss class
fail to benefit from teacher-led lessons, peer interactions, and other
activities designed to foster learning, which is harmful for school
success. Absences from school during the elementary school years
are an important indicator of later academic success (Gottfried,
2011; Steward, Steward, Blair, Jo, & Hill, 2008). However, little is
known about how living in a low-income household impacts
children’s school attendance, and, in turn, about how school at-
tendance impacts academic achievement, particularly among di-
verse and inner-city populations of young children most at risk for
poor achievement. Furthermore, less is known about how income
stability (i.e., fluctuations in family income over time) relates to
children’s attendance and achievement. In this study, we address
these gaps in the literature by examining relations between family
income, school attendance, and academic achievement among an
ethnically and racially diverse urban sample of children from
kindergarten to fourth grade, using robust longitudinal methods to
limit potential selection bias.
There is a vast literature on the impacts of income on family
processes and children’s development (Duncan & Brooks-Gunn,
1997; Duncan et al., 2010; Evans, 2004). Greater income enhances
the material and social resources available to children (McLoyd,
1990); however, material resources, such as the number of books
in the home, and children’s access to learning opportunities ex-
plain only about one third of the poor–nonpoor achievement gap
(Brooks-Gunn & Markman, 2005). Family income tends to be
volatile (Ziliak, Hardy, & Bollinger, 2011), and the intensity and
directionality of changes, even small income changes, experienced
during childhood has been associated with long-term outcomes
(Galambos & Silbereisen, 1987; Salkind & Haskins, 1982). Using
data from the National Longitudinal Survey of Youth, Dahl and
Lochner (2005) found that an increase in income of $1,000 was
associated with a 2.1% and 3.6% of a standard deviation increase
in children’s math and reading test scores, respectively. Consistent
with theory and research on the developmental importance of early
childhood (Bronfenbrenner & Morris, 1998; Shonkoff & Phillips,
2000), experiencing low family income at younger ages versus
later periods has a greater impact on achievement (Duncan &
Brooks-Gunn, 1997; Duncan et al., 2010; National Institute of
Child Health and Human Development Early Child Care Research
Taryn W. Morrissey, Department of Public Administration and Policy,
American University; Lindsey Hutchison and Adam Winsler, Department
of Psychology, George Mason University.
This research was supported by the Early Learning Coalition of Miami-
Dade/Monroe and by the Children’s Trust. The Trust is a dedicated source
of revenue established by voter referendum to improve the lives of children
and families in Miami-Dade County. We thank participants at colloquia at
the Society for Research in Child Development and the University of
Virginia. We also thank the Miami-Dade County Public Schools for their
support and assistance with this research.
Correspondence concerning this article should be addressed to Taryn W.
Morrissey, Department of Public Administration and Policy, American
University, 4400 Massachusetts Avenue, NW, Washington, DC 20016.
E-mail: taryn.morrissey@american.edu
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Developmental Psychology © 2013 American Psychological Association
2013, Vol. 49, No. 9, 000 0012-1649/13/$12.00 DOI: 10.1037/a0033848
1
Network [NICHD ECCRN], 2005). However, children whose fam-
ilies face persistent poverty fare the worst in terms of academic
achievement (Dearing, McCartney, & Taylor, 2001; Duncan &
Brooks-Gunn, 1997; NICHD ECCRN, 2005). Thus, the volatility,
timing, and persistence of low family income appear important to
child development.
When economic resources are scarce, children face challenges at
multiple levels that may impact their likelihood of attending school
on time or at all, which, in turn, may impact academic success.
Children living in low-income families are more likely than their
higher income peers to experience physical, behavioral, and men-
tal health problems (Currie, 2005; Evans & Kim, 2007; Wentzel,
1991); poorer nutrition (Currie, 2005); and environmental hazards
(Evans, 2004), which can lead to more missed days of school or
tardiness. Children in low-income families tend to experience
greater residential mobility, which is linked to poorer academic
outcomes (Burkam, Lee, & Dwyer, 2009). Children in poverty are
generally exposed to higher levels of family conflict than their
higher income counterparts (Evans, 2004) and greater instability in
family structure (Burkam et al., 2009). Residential and family
instability may make establishing and maintaining routines diffi-
cult, which in turn may lead to more school absences and tardies.
In addition, children living in low-income neighborhoods are more
likely to experience child maltreatment (Coulton, Korbin, Su, &
Chow, 1995), and exposure to this and other significant life events
(e.g., parental divorce) is associated with increased problem be-
havior and school absence (Reynolds, Weissberg, & Kasprow,
1992). Parents struggling to make ends meet are more often
employed during nonstandard hours (nights/weekends) or have
variable work schedules (Han, 2004), which are linked with poorer
child cognitive outcomes (Han, 2005). Children with parents who
work rotating or nonstandard shifts may need to be more self-
reliant in getting ready for and getting to school, which may result
in increased school tardiness/absence.
Finally, family income may affect school attendance through
neighborhood and societal characteristics. Low-income families
are more likely to live in dangerous neighborhoods (Leventhal &
Brooks-Gunn, 2004) and experience greater exposure to neighbor-
hood violence (Krenichyn, Saegert, & Evans, 2001) than higher
income families, and thus getting to and from school safely could
be problematic. Indeed, neighborhood quality has been linked to
academic achievement (Sanbonmatsu, Duncan, Kling, & Brooks-
Gunn, 2006). Low-quality neighborhoods may negatively impact
children’s school attendance and achievement through increased
stress from community violence, gangs, or drug activity; a lack of
positive role models and the presence of negative peer influences,
leading to problem behavior and truancy; or a lack of institutional
resources including police protection (Sampson, Morenoff, &
Ganon-Rowley, 2002).
Children who frequently miss or are late to school fail to benefit
from teacher instruction and modeling, peer interactions, and other
activities designed to scaffold learning. Indeed, there is a growing
body of research linking attendance and academic achievement,
such that as absenteeism increases, school performance declines
(Gottfried, 2009, 2011; Reynolds et al., 1992; Steward et al.,
2008). In one study, elementary students who missed school fre-
quently (present less than 80% of school days) scored 20 points
lower on a test of reading achievement compared with students
who had close to perfect attendance (Family Housing Fund, 1998).
After controlling for various demographic variables, Gottfried
(2009) found that increased absenteeism predicted lower reading
and math achievement among a sample of second- to fourth- grade
ethnically diverse students. Other studies have exploited the ex-
ogenous variation in snow days to demonstrate that more instruc-
tional time, in terms of more school days, increases student per-
formance on standardized tests (Marcotte & Hemelt, 2008).
Although the small body of research on absenteeism has focused
on the number of days that children missed school (Gottfried,
2009, 2011), no prior studies could be found examining how
tardiness, or missing part of the day, affects achievement. Tardi-
ness is an issue with which the schools appear to be concerned, as
indicated by the careful record keeping and disciplinary actions
associated with being tardy. Research on tardiness has focused on
demographic and individual differences linked with rates of tardi-
ness rather than exploring links between being late for school and
children’s performance. For example, overweight children show
more tardiness compared with nonoverweight peers (Shore et al.,
2008). It is important to examine whether tardiness is associated
with poor school performance among elementary students, partic-
ularly those from disadvantaged backgrounds. Absence and tardi-
ness may be differentially linked to student achievement, with
absence for the entire day likely having a more negative effect on
children’s school performance than missing only part of the day.
Because children from low-income families may be likely to
miss school or be late more often than higher income children, the
consistency of children’s school attendance may account for part
of the achievement gap between poor and nonpoor students. It may
be particularly detrimental for children from low-income families
to miss or be late to school because such families are less likely to
have the time or resources necessary to help children “catch up”
with missed school material, compared with peers from more
advantaged backgrounds (Chang & Romero, 2008). Furthermore,
compared with their higher income peers, low-income children are
more likely to attend low-quality schools that often lack resources
for educators to intervene with children who have poor attendance
or who are often late to class (Hanushek, 1997; Leventhal &
Brooks-Gunn, 2004; Sanbonmatsu et al., 2006). Using kindergar-
ten and first-grade data from the Early Childhood Longitudinal
Study Kindergarten cohort, Ready (2010) found not only that
school absences were negatively related to literacy development in
kindergarten but also that school absences had more of a negative
effect on achievement for children from low-socioeconomic (SES)
backgrounds, compared with children of higher SES, and non-
Asian minorities and English Language Learners (ELL) were more
likely to be absent than other groups. Others have found that
although chronic absence in kindergarten is related to lower first-
grade performance for all children, missing school in kindergarten
may be particularly detrimental for Latino children and children
living in poverty (Chang & Romero, 2008).
Despite the many possible ways in which family income may be
related to children’s school attendance, very few studies have
examined the links between family income, children’s school
absences and tardies, and their achievement, especially for chil-
dren in elementary school. This is an important age at which to
examine such links, as parents are more accountable for regular
school attendance for their young children. Marcotte and Hemelt
(2008) found that school closures had greater effects on younger
elementary school children, compared with fifth and eighth grad-
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2
MORRISSEY, HUTCHISON, AND WINSLER
ers. However, other studies suggest that school absences and
tardies may have a greater negative effect on achievement as
children grow older. Most previous research examining associa-
tions between children’s absences and academic achievement
among older school-age children, adolescents, or college students
has found modest relationships between absenteeism and academic
performance (e.g., Steward et al., 2008; Wentzel, 1991), whereas
smaller but substantial associations between attendance and read-
ing/math achievement are typically found with elementary school
children, with standardized regression coefficients ranging be-
tween 0.05 and 0.14 (Gottfried, 2009, 2011).
The Present Study
Despite evidence that the achievement gap starts before kinder-
garten and widens throughout elementary school (Duncan & Mag-
nuson, 2005), little is known about relations between school ab-
sences, tardies, and student achievement over time, particularly
among ethnically diverse, low-income, young children who are
most at risk for poor achievement. To date, no research has
examined how instability in family income and the persistence of
low family income relate to children’s attendance in elementary
school. In addition, with few exceptions (Gottfried, 2011; Marcotte
& Hemelt, 2008), most previous research has relied on models that
are susceptible to omitted variable bias resulting from the many
differences between children with high and low rates of school
attendance, or between high- and low-income families. For exam-
ple, underlying family health problems (Currie, 2005), or the
transportation problems or lengthy commute times common
among low-income parents (Dunifon, Kalil, & Bajracharya, 2005),
may affect both attendance and achievement. Most research uses
ordinary least squares (OLS) regression, controlling for back-
ground characteristics; however, many of these differences are
unobserved. Without being able to control for all of the potential
ways in which children differ from each other, OLS regressions
predicting school absences from family income, or student
achievement from school attendance, may be biased.
The goal of this study was to estimate relations between family
income, school attendance, and children’s academic achievement
over time among a diverse sample, using robust longitudinal
methods including random-effects and within-child fixed-effects
models. Data for this study come from the Miami School Readi-
ness Project (MSRP; Winsler et al., 2012, 2008), a large-scale
university– community partnership in which five countywide co-
horts of children attending various types of community-based
childcare and public school pre-kindergarten (pre-K) programs,
most with the assistance of subsidies, were followed longitudinally
through fourth grade. Consistent with prior work (Gottfried, 2009,
2011; Reynolds et al., 1992), we use in the present study children’s
receipt of free or reduced-price lunch in the National School Lunch
Program (NSLP) as a proxy for family income status. Because
most children are enrolled using direct certification by local edu-
cation agencies, requiring little action from children’s parents or
guardians, participation in the NSLP is high among eligible chil-
dren. To qualify for free or reduced-price lunch, a child’s family
must be at 130% or 185%, respectively, of the Federal Poverty
Level (FPL). In 2010, 32 million children received free or reduced-
price lunch at school (U.S. Department of Agriculture [USDA],
2010). Given that the FPL has been criticized as being too low to
adequately represent economic hardship (Boushey et al., 2001), it
is important to use both free and reduced-price lunch categories to
represent different experiences of economic disadvantage. The
2012 FPL was $23,050 for a family of four (U.S. Department of
Health and Human Services, 2012); thus, the annual income
thresholds for a family of four were $42,643 for reduced-price
lunch and $29,965 for free lunch, a difference in annual income of
$12,500. Research suggests that small changes in income can lead
to significant changes in children’s development (Dahl & Lochner,
2005; Dearing et al., 2001); thus, it is possible that the increase in
family income needed for a family to qualify for free lunch in 1
year and for reduced-price lunch the next year could correspond
with meaningful increases in attendance or achievement.
Specifically, this study addresses five research questions: (1) Is
family income status associated with the number of days children
are absent from or late to school? We hypothesized that children
receiving free or reduced-price lunch experience greater numbers
of absences and tardies than their counterparts who pay full price.
In addition, we expected receipt of free lunch to be associated with
greater absences and tardies than receipt of reduced-price lunch.
(2) Is family income status associated with children’s academic
achievement? We hypothesized that children receiving free or
reduced-price lunch would have lower academic achievement than
those paying full price and that receipt of free lunch would be more
strongly linked to poorer achievement compared with receipt of
reduced-price lunch. (3) Are school absences and tardies associ-
ated with children’s academic achievement? We hypothesized that
school absences and tardies would be associated with poor
achievement and that absences would be more strongly associated
with achievement than tardiness. (4) Assuming that the expected
associations between family income and school attendance, and
school attendance and student achievement are found, do the
number of days absent or times tardy attenuate associations be-
tween family income and academic achievement? We expected the
gap in achievement between low- and higher income children to be
partially explained by their attendance patterns. (5) Does child age
moderate associations between family income and school atten-
dance, or between school attendance and student achievement? We
hypothesized that poor attendance at the older grades (third and
fourth) would be more strongly associated with poorer academic
achievement compared with poor attendance at younger ages.
Method
Data
This study used data from the MSRP, a longitudinal cohort-
sequential study of 42,287 children in a large, urban public school
district in Florida who had participated in a large-scale project
during their pre-kindergarten year (Winsler et al., 2012, 2008). The
present study included a subsample of 35,419 children attending
259 public schools who remained in the study through kindergar-
ten. All data were obtained from elementary school records. Be-
cause our study followed children who moved to other neighbor-
hoods within the large county, only those children who moved
counties or stopped attending public schools altogether were ex-
cluded from the sample. Kindergarten consisted of a full-day
program in all schools. Children with special needs were excluded
from the present study because we were interested in how family
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3
INCOME, SCHOOL ATTENDANCE, AND ACHIEVEMENT
income influences typically developing children’s school atten-
dance and achievement; special needs and poor health have been
shown to negatively affect school attendance and achievement
(Klebanov, Brooks-Gunn, & McCormick, 1994). Children who
were retained at some point during early elementary school were
also excluded. Because of the way the data set was compiled
yearly from the district, it was not possible to track the children
who repeated a grade and arrived late to the next grade late across
5 years (their data were in the “wrong” grade in time). Although it
is not clear whether retained students were included in prior
studies of attendance (Gottfried, 2009, 2011; Reynolds et al.,
1992), it is unlikely that they were for the same structural reasons
discussed above. Thus, we believe the exclusion of retained stu-
dents in the present study increases comparability with prior re-
search in this area.
Five cohorts of children entering kindergarten participated, with
the oldest cohort (Cohort A) entering kindergarten in 2003 and the
youngest (Cohort E) entering kindergarten in 2007. Due to the
cohort-sequential longitudinal nature of the study, information in
the later school years is only available for older cohorts. For
example, although kindergarten information is available for all
cohorts of children, fourth-grade data are only available for the
oldest cohort (A), third-grade data only for the oldest two cohorts
(A and B), and so on. Table 1 describes the cohort structure and the
number of children in the sample each grade. The number of
children lost each grade (due to the child leaving the school
system, being retained, or skipping a grade) were from K to first
grade n 928; from first to second grade n 1,261; from second
to third grade n 981; from third to fourth grade n 741.
Children with background and school absence/tardiness data for at
least two grades (thus, excluding Cohort E) were included in the
within-child fixed-effects models (explained below). To be in-
cluded in the random-effects models, children had to have infor-
mation on gender, race, ethnicity, ELL status, pre-K history, and
absences/tardies for at least one grade. A total of 34,910 children
were included in at least one model in the present study (83% of
the total sample). Excluded children were more likely to be Black,
receive free lunch (but not reduced-price lunch), and were less
likely to be Hispanic, White, or ELL, than those remaining in the
sample. Children with complete information and those with miss-
ing data did not differ on pre-K participation.
Measures
Income status. Children from low-income families are eligi-
ble for free or reduced-price lunch (130% of the FPL and 185% of
the FPL, respectively) in the public school system. Eligibility is
recertified at the beginning of each academic year (USDA, 2010).
In Miami, families who receive the Supplemental Nutrition Assis-
tance Program (formerly known as Food Stamps) or Temporary
Assistance to Needy Families funds, and have a Social Security
number on file at the school can qualify for direct certification and
recertification each year. However, most families actively apply or
reapply each year by completing and returning the eligibility form
that all students receive through the mail at the beginning of each
school year to qualify for the school lunch program. This measure
served as a proxy of low-income status and was assessed at each
grade in school, and was coded with two dummy variables of
reduced-price lunch receipt (1 received reduced-price lunch)
and free lunch receipt (1 received free lunch). In addition to
status at each grade, two variables representing the duration of
receipt of reduced-price lunch and duration of receipt of free lunch
were generated by summing the years children were coded as
receiving either reduced-price or free lunch, respectively (range
0 –5).
School absence and tardiness. Participants’ attendance infor-
mation was collected from school records each year. Teachers
submitted active attendance reports daily, and administrative re-
cords listed both the total number of days tardy and the total
number of days absent during each school year for each child.
These totals represent the combination of both excused and unex-
cused absences and tardies. To examine potential nonlinear thresh-
old effects, we generated categorical variables representing differ-
ent ordinal levels of absences and tardies, with cutoffs based on
prior research (Chang & Romero, 2008): fewer than 2 days absent
or times tardy; 2– 4 days absent/tardy; 5–9 days absent/tardy;
10 –17 days absent/tardy; and 18 or more days absent/tardy.
Academic achievement in elementary school. At the end of
each academic year, children received grades from their teachers
for all subject areas. For example, in kindergarten subjects in-
cluded language development, prereading, handwriting, math, sci-
ence, Spanish, social studies, English as a second language, music,
art, and physical education. Grades were based on a 3-point scale
in kindergarten (3 Excellent,2 Satisfactory, and 1 Unsat-
isfactory). In first through fourth grade, grades were on a 5-point
scale, ranging, with the familiar letter assignments of A to F (5
A,4 B,3 C,2 D, and 1 F). Composite scores were
created by averaging all grades children received across all sub-
jects in a given year, resulting in one overall grade for each year.
Mean grades were standardized (M 0, SD 1) to allow for
comparisons across grade levels. Preliminary analyses showed that
grades for individual subjects were highly correlated, with no
pattern in the correlations between particular subject grades and
Table 1
Cohort Structure and Number of Participants in Each Grade
Cohort Year entered kindergarten Kindergarten 1st grade 2nd grade 3rd grade 4th grade
A 2003 6,102 4,921 4,918 4,426 3,685
B 2004 6,992 6,481 5,817 5,328
C 2005 7,879 7,094 6,500
D 2006 4,863 6,382
E 2007 5,916
Total number of observations at each grade 31,722 24,878 17,235 9,754 3,685
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4
MORRISSEY, HUTCHISON, AND WINSLER
attendance (i.e., it was not the case that math or PE grades were
more influenced by missing school) and supported our decision to
use composite grades for analyses.
The Florida Comprehensive Assessment Test (FCAT; Human
Resources Research Organization & Harcourt Assessment, 2007),
the standardized test used by the state to assess students’ progress
in math and reading, was administered in third and fourth grades.
The assessment is closely aligned with the curriculum experienced
by the students as both are required to cover state standards of
content for each year. The FCAT was administered over several
days in the spring of the academic year, with children spending
4 8 hr each day completing the test. Questions are in both
multiple-choice and short-answer formats. For the present study,
total scale scores were used from both the reading and math
portions of the test (range 100 –500; Cronbach’s ␣⫽.90 for
math and .98 for reading; Human Resources Research Organiza-
tion & Harcourt Assessment, 2007).
Child and family characteristics. Child gender and race/
ethnicity (Black, Hispanic, Hispanic Black, or White) were
obtained from school records. A child was considered an ELL
if the parent reported a home language other than English upon
entering kindergarten or the child received the English profi-
ciency test required for children designated by the school as
ELLs. Whether the child attended a free, half-day pre-K pro-
gram at a public school (62%; the vast majority of being free
Title I programs) or community-based childcare (38%; of these,
95% center-based, 5% family childcare) via state childcare
subsidies for low-income families, was known from the original
study (Winsler et al., 2008).
Analytic Plan
To address potential omitted variable bias resulting from the
unobserved ways that children living in low-income families
differ from those living in higher income families, we used both
random-effects (RE) models and within-child fixed-effects (FE)
regressions, pooling all available data from all five periods (K,
first, second, third, and fourth grade) and relying on repeated
observations of family income, school absences and tardies, and
achievement measures for each child. Because of the cohort-
sequential design, some children were observed for more data
waves than others; because the regression models used predict
cross-sectional associations between concurrent measures of
attendance, achievement, and school lunch status (or the dura-
tion of school lunch status to that point), this is not a problem
in the analyses.
RE models address potential omitted variable bias by including
a child-specific intercept in order to capture any unobserved char-
acteristics, assuming that all omitted variables are randomly dis-
tributed, and are independent of predictors and outcomes (Allison,
2005). All possible relevant fixed (time-invariant) and time-
varying background characteristics measured are controlled in the
model to limit potential omitted variable bias. In our RE models,
covariates included child age, gender, race/ethnicity, ELL status,
and whether the child attended a public school pre-K program (vs.
childcare in the community). Because of the cohort-sequential
nature of our data, standardized tests scores are available for
Cohorts A and B only; therefore, our RE models predicting test
scores include these cohorts only.
By contrast, FE models use within-child comparisons to predict
changes in the outcome (attendance or achievement) from changes
over time in the predictor (income or attendance) for the same
child. As a result, all measured and unmeasured fixed effects of a
given child or his or her family drop out of the FE model (e.g.,
child gender), and more conservative estimates are produced. As
such, FE models examine how a child’s attendance or achievement
at a specific time point deviates from that same child’s average
level of attendance or achievement measured across all data waves
(in this study, two to five data waves, depending on the availability
of data). This is predicted by family income status or child’s school
attendance level at a single time point, from which is subtracted
either the family’s average income status or the child’s average
attendance level across all waves. Only time-varying covariates are
included in FE models; our model controlled for child grade in
school. Because of the cohort-sequential design, the FE models
that predict math and reading scores include Cohort A only, as this
is the only cohort with 2 years of test score data; this model is
analogous to a change model.
FE models require variation in both the predictor and outcome.
We had substantial variation in receipt of free or reduced-price
lunch; 21% changed lunch status between K and first grade; 22%
for first and second grades; 23% for second and third grades; and
24% for third and fourth grades. Of children with data through
fourth grade, 43% had experienced at least one change in lunch
status from K through fourth grade: 16% had changed lunch status
once, 18% twice, and 9% three or four times. At each time point,
between 52% and 54% of the changes were to free lunch from
reduced-price lunch, or vice versa. The predictor variable repre-
senting the duration of time a child spent in a low-income house-
hold, by definition, is cumulative; these models include children
who consistently lived in low-income homes and those who ex-
perienced changes in family income.
Both FE and RE analyses control for numerous factors to
limit potential biases from observed and unobserved differences
between children in family and school experiences, but have
limitations. First, the assumption in the RE approach that all
omitted variables are randomly distributed and are independent
of predictors and outcomes is easily violated; for example, there
are likely unobserved differences between children living at
different income levels that also affect their attendance or
achievement, such as parental education or involvement in their
children’s education. Because of this, the FE models, which
control for all time-invariant characteristics and thus provide
more conservative estimates, are our preferred specification.
Second, neither the RE nor FE models remove the biasing
effects of unmeasured variables that change with time. For
example, changes in parental health or stress that co-occur with,
or even cause or are caused by, changes in children’s school
attendance or achievement will still bias our estimates. Third,
the FE model assumes that constant factors such as gender have
a consistent or time-invariant effect on the dependent variable,
and does not account for the fact that the influence of such
measures may change with age. Finally, neither analytic ap-
proach addresses reverse causality, the possibility that family
income is impacted by children’s attendance at school, or that
low-achieving students are more likely to miss school.
The measures of school absence and tardiness were skewed.
Over all five grades, 51% of children had five or fewer absences
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5
INCOME, SCHOOL ATTENDANCE, AND ACHIEVEMENT
per year, 11% had no absences per year, and less than 1% had 34
or more absences (range 0 –142). In the pooled data, more than
one quarter of children were not tardy during a year (27%), 51%
were tardy 2 or fewer days, and only 2% were tardy 47 or more
times per year (range 0–169). To account for the nonnormal
distributions, Poisson regressions were used to predict the contin-
uous measures of school absences and tardies from free or
reduced-price lunch receipt, and the categorical absence and tar-
diness variables were used as independent variables to predict
children’s achievement.
A series of regression models were run. First, Poisson RE and
FE models predicted school absences and tardies from receipt of
free and reduced-price lunch, and the duration of receipt of free or
reduced-price lunch. Second, RE and FE models predicted student
achievement from receipt of free and reduced-price lunch, and the
duration of receipt of free or reduced-price lunch. Third, RE and
FE models predicted student achievement from categorical mea-
sures of school absences and tardies. Fourth, the attenuation mod-
els, predicting student achievement from measures of school ab-
sences, tardies, and family income (both concurrent and duration),
were conducted. Finally, we tested whether child age moderates
(anticipated) associations between family income and school at-
tendance, and between school attendance and achievement (Aiken
& Clay, 1991). Results of Hausman chi-square tests, one method
used to compare regression coefficients from RE and FE models,
were significant for all models, indicating that there are systematic
differences between the coefficients in the RE and FE models
(Hausman, 1978). Thus, we prefer the FE estimates, although
we present results from both types of models. Because of the large
sample size, a conservative alpha of .01 was used to determine
statistical significance. Effect sizes for continuous variables are
calculated by dividing the standard deviation of the independent
variable by the standard deviation of the dependent variable, and
multiplying by the coefficient.
Results
Descriptive Results
Table 2 provides descriptive information for the sample across
years. At each grade, between 61% and 67% of children received
free lunch, and an additional 11%–15% received reduced-price
lunch. By fourth grade, children had received free and reduced-
price lunch for an average of .67 and 3.19 years, respectively. The
Table 2
Sample Descriptive Statistics
Child grade
Variable K 1st grade 2nd grade 3rd grade 4th grade Total sample
Receive reduced-price lunch 11.24% 12.04% 13.22% 14.01% 14.73% 12.31%
Receive free lunch 66.64% 66.60% 64.63% 63.02% 61.49% 65.62%
Number of years received reduced-price lunch .11 (.32) .23 (.53) .36 (.72) .50 (.91) .67 (1.11) .26 (.61)
Number of years received free lunch .67 (.47) 1.32 (.87) 1.98 (1.24) 2.60 (1.61) 3.19 (1.98) 1.40 (1.25)
Child is male 52.22% 51.26% 50.46% 49.19% 47.95% 51.08%
Child is White 6.04% 5.63% 5.69% 5.89% 5.80% 5.83%
Child is Black 32.89% 32.63% 33.26% 33.08% 33.18% 32.88%
Child is Hispanic 55.91% 56.22% 55.26% 56.63% 56.26% 56.26%
Child is Hispanic Black 1.89% 2.09% 2.38% 0.82% 1.67% 1.89%
Child is an English Language Learner 56.93% 57.16% 56.68% 58.21% 59.10% 57.18%
Child attended public school pre-K program 68.50% 62.65% 61.65% 62.24% 60.54% 64.45%
Child attended childcare in the community 31.50% 37.35% 38.35% 37.76% 39.46% 35.55%
Attendance variables
Number of days absent from school 8.71 (8.39) 7.18 (7.14) 6.32 (6.45) 5.45 (5.70) 5.16 (5.87) 7.29 (7.41)
Number of times tardy to school 7.53 (12.44) 7.66 (12.72) 7.25 (12.07) 6.57 (11.01) 6.07 (11.15) 7.34 (12.25)
Absent 2 days 15.04% 19.44% 23.11% 26.71% 29.29% 19.78%
Absent 2–4 days 21.57% 24.43% 25.64% 27.94% 28.45% 24.18%
Absent 5–9 days 28.91% 28.83% 28.72% 27.08% 25.60% 28.51%
Absent 10–17 days 22.48% 19.59% 16.95% 14.46% 13.29% 19.29%
Absent 18 days 12.00% 7.71% 5.57% 3.80% 3.63% 8.23%
Tardy 2 days 41.49% 41.61% 42.68% 44.60% 47.22% 42.31%
Tardy 2–4 days 20.53% 20.01% 20.06% 20.84% 21.32% 20.35%
Tardy 5–9 days 13.96% 14.04% 13.76% 12.79% 12.86% 13.77%
Tardy 10–17 days 10.64% 10.51% 10.63% 10.08% 8.38% 10.44%
Tardy 18 days 13.49% 13.83% 12.88% 11.68% 10.23% 13.13%
Achievement variables
School grades (raw scores) 2.38 (0.46) 4.23 (0.63) 4.11 (0.62) 3.99 (0.61) 4.12 (0.52) 3.51 (0.64)
FCAT Math score 2.99 (1.11) 3.04 (0.97) 3.00 (1.07)
FCAT Reading score 3.18 (1.18) 3.19 (1.17) 3.19 (1.18)
Number of children with obs. at each grade
a
31,113 24,914 17,315 9,568 3,662 86,518
Note.K Kindergarten; FCAT Florida Comprehensive Assessment Test. Values in parentheses represent standard deviations.
a
Following several cohorts longitudinally, the number of observations (obs.) decreases with age. The figure in the final column represents the numberof
observations within children across all 5 years.
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6
MORRISSEY, HUTCHISON, AND WINSLER
sample is racially, ethnically, and linguistically diverse, with more
than half the children identified as Hispanic and as ELL. About
two thirds (64%) had attended public school pre-K, and the rest
attended center-based or family childcare at age 4. Across all
grades, on average, children were absent from school 7.29 days per
year, and tardy 7.34 days per year. Children received an average
grade of “satisfactory” in kindergarten and an overall “B” average
in first through fourth grade.
Predicting School Absences and Tardies From Receipt
of Free and Reduced-Price Lunch
Results from the Poisson RE and within-child FE regression
main-effects models that estimate relationships between the re-
ceipt, and duration of receipt, of free and reduced-price lunch and
school absences and tardies are in Table 3. Child and family
characteristics are controlled (not shown). Generally, results from
both the RE and FE models reveal very small but significant
associations between the receipt of free or reduced-price lunch and
greater absences and tardies. Receipt of free lunch (under 130%
FPL) is associated with more tardies and more absences (the latter
in the RE models only) than receipt of reduced-price lunch (130%–
185% FPL). Effect sizes were small for both days absent (.001%–
.006%) and times tardy (.001%–.002%), and tended to be smaller
in the FE models than the RE models. Associations between free
lunch and days absent, and reduced-price lunch and times tardy,
were not significant in the FE models. As expected, results from
both RE and FE models indicate that longer periods of receipt of
free school lunch are associated with more school absences and
tardies. A longer duration of reduced-price lunch receipt is asso-
ciated with more tardies in both types of models, and with more
absences in the FE models only. Effect sizes are again small, with
each additional grade spent receiving free or reduced-price lunch
associated with a 0.1%– 0.2% increase in absences or tardies.
Predicting Student Achievement From Receipt of Free
and Reduced-Price Lunch
Table 4 displays the results from the RE and FE models pre-
dicting student achievement from receipt, and duration of receipt,
of reduced-price and free lunch. Results from the RE models
indicate children receiving free lunch or reduced-price lunch ob-
tained considerably poorer grades than those paying full price
(18.3% and 6.2%, respectively). At third and fourth grades,
receipt of free and reduced-price lunch were also associated with
lower standardized test scores in the RE models. In contrast with
expectations, however, results from the FE models suggest that
changes in children’s receipt of free or reduced-price lunch were
not associated with changes in grades or test scores.
In models predicting student achievement from duration of
receipt of free and reduced-price lunch, both RE and FE models
indicate that longer duration of receipt predicted lower grades.
That is, the length of time spent in a household that qualified as
low income appears to have a cumulative, negative effect on
student grades. Effect sizes are relatively small (decreases ranged
from 0.04 and 0.18 of a standard deviation in grades). Children
who experienced longer periods of free or reduced-price lunch
averaged slightly lower test scores than those with fewer years of
receipt, but the lack of significance in the FE models indicates that
Table 3
Predicting Child Absence and Tardiness From Free and Reduced-Price Lunch Receipt and Duration of Free/Reduced-Price Lunch Receipt: Regression Results
Predicting days absent Predicting times tardy
Poisson RE Poisson FE Poisson RE Poisson FE Poisson RE Poisson FE Poisson RE Poisson FE
Variable B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE)
Full lunch (ref)
Reduced-price lunch .052
ⴱⴱ
(.008)
.032
ⴱⴱ
(.009)
.034
ⴱⴱ
(.008)
.018
(.008)
Free lunch .094
ⴱⴱ
(.007)
.012 (.008) .065
ⴱⴱ
(.007)
.042
ⴱⴱ
(.008)
Number of years reduced-price lunch .008 (.006) .025
ⴱⴱ
(.006)
.032
ⴱⴱ
(.006)
.029
ⴱⴱ
(.006)
Number of years free lunch .061
ⴱⴱ
(.003)
.036
ⴱⴱ
(.004)
.037
ⴱⴱ
(.004)
.031
ⴱⴱ
(.004)
Child grade .137
ⴱⴱ
(.001)
.137
ⴱⴱ
(.001)
.183
ⴱⴱ
(.003)
.167
ⴱⴱ
(.003)
.023
ⴱⴱ
(.001)
.023
ⴱⴱ
(.001)
.051
ⴱⴱ
(.003)
.047
ⴱⴱ
(.003)
Constant 2.084
ⴱⴱ
(.019)
2.123
ⴱⴱ
(.019)
2.196
ⴱⴱ
(.030)
2.201
ⴱⴱ
(.031)
Wald
2
11,284.47
ⴱⴱ
10,437.26
ⴱⴱ
10,812.56
ⴱⴱ
9,982.33
ⴱⴱ
1,201.95
ⴱⴱ
374.25
ⴱⴱ
1,042.35
ⴱⴱ
331.54
ⴱⴱ
Log likelihood 262,698.18 132,560.01 244,570.00 125,046.03 309,498.27 183,605.48 287,423.16 172,597.09
N 34,910 23,467 31,764 21,973 34,910 21,892 31,764 20,945
Note. RE random effects; FE within-child fixed effects; ref reference. Child gender, race, English Language Learner status, and pre-Kindergarten attendance are controlled for in RE models
(not shown). In FE models, 11,395 children were dropped because they had only 1 year’s data. For days absent analyses, 557 children were dropped because they were never absent; for tardy analyses,
2,132 children were dropped because they were never tardy.
p .05.
ⴱⴱ
p .001.
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7
INCOME, SCHOOL ATTENDANCE, AND ACHIEVEMENT
duration of receipt did not have a cumulative effect on scores;
however, this may be due to a lack of within-child variation in
standardized test scores, given they were administered at two
grades only.
Predicting Student Achievement From School
Absences and Tardies
Results from the RE and FE models predicting achievement
from absences and tardies are in Table 5. As predicted, in both
models, children with more absences and tardies received lower
grades than peers with better attendance, and grades were lower
during the years children demonstrated poorer attendance. Al-
though children with poorer attendance received lower math and
reading scores compared with peers in the RE models, the lack of
significance in the FE models suggests that children do not have
worse test scores during years in which they have poorer atten-
dance. Again, this may be the result of the limited number of data
waves that include test scores.
Because grades are available at all ages, we restrict our attenu-
ation analyses to mean standardized grades, which are shown in
Tables 6 and 7. RE models provide some evidence that school
absences and tardies attenuate associations between family income
status and children’s grades, in that the coefficients for free and
reduced-price lunch receipt were smaller than in Table 4. In both
the RE and FE models, a longer period of receipt of free or
reduced-price lunch, more days absent, and more times tardy
independently predicted poorer grades, but the coefficients for the
duration of receipt of free and reduced-price lunch were somewhat
smaller than in the main models (see Table 4), suggesting that
school attendance partially attenuates relations between duration
of low income and children’s achievement. The lack of attenuation
is not surprising given the small associations between family
income and children’s attendance patterns, and between the dura-
tion of low family income and children’s grades.
Sensitivity Analyses
We tested the moderating effects of child age (grade in school)
in the FE models. Results (available upon request) provide evi-
dence that the association between income and absence grew
slightly with children’s age. The associations between free lunch
and days absent and times tardy increased by 0.03 and 0.02 days,
respectively, with each grade. Similarly, associations between
duration of free-lunch receipt and absences grew with age by 0.03.
By contrast, the association between duration of free lunch and
times tardy decreased by 0.02 with each grade. Child grade also
moderated associations between income and grades. As expected,
associations between free or reduced-price lunch and standardized
grades increased in magnitude (became more negative) as children
aged. Finally, child grade moderated associations between atten-
dance and children’s grades, with five or more absences appearing
to serve as a threshold. That is, as children aged, associations
between the categorical measures of school absence (five or more)
and children’s grades grew in magnitude (became more negative).
The pattern was similar with tardiness, although there did not
appear to be a threshold; with each grade, each categorical measure
of times tardy increased its negative effect on children’s grades.
Finally, separate FE models by child gender and race (Black,
Table 4
Predicting Student Achievement From Free and Reduced-Price Lunch Receipt and Duration of Free/Reduced-Price Lunch Receipt: Regression Results
Predicting mean standardized grades (K– 4th grades) Predicting math scores (3rd– 4th grades only) Predicting reading scores (3rd– 4th grades only)
RE FE RE FE RE FE RE FE RE FE RE FE
Variable B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE)
Full lunch (ref)
Reduced-price lunch .120
ⴱⴱ
(.011)
.013 (.014) .131
ⴱⴱ
(.030)
.042 (.05) .136
ⴱⴱ
(.033)
.020 (.060)
Free lunch .246
ⴱⴱ
(.010)
.027
(.013)
.380
ⴱⴱ
(.025)
.037 (.053) .376
ⴱⴱ
(.027)
.015 (.059)
Number of years
reduced-price
lunch .044
ⴱⴱ
(.007)
.039
ⴱⴱ
(.009)
.023 (.012) .004 (.054) .004 (.014) .021 (.060)
Number of years
free lunch .181
ⴱⴱ
(.005)
.141
ⴱⴱ
(.006)
.135
ⴱⴱ
(.008)
0.005 (.039) .143
ⴱⴱ
(.009)
.040 (.044)
Child grade .004
(.002)
.007
(.002)
.125
ⴱⴱ
(.004)
.106
ⴱⴱ
(.005)
.161
ⴱⴱ
(.015)
.321
ⴱⴱ
(.016)
.087
ⴱⴱ
(.017)
.334
ⴱⴱ
(.033)
.045
(.016)
.079
ⴱⴱ
(.017)
.133
ⴱⴱ
(.018)
.094
(.037)
Constant .53
ⴱⴱ
(.02)
.011 (.010) .445
ⴱⴱ
(.020)
.095
ⴱⴱ
(.006)
3.992
ⴱⴱ
(.063)
4.025
ⴱⴱ
(.066)
3.843
ⴱⴱ
(.065)
4.131
ⴱⴱ
(.064)
3.546
ⴱⴱ
(.069)
2.939
ⴱⴱ
(.072)
3.354
ⴱⴱ
(.071)
2.999
ⴱⴱ
(.070)
R
2
(overall)
.100 .038 .111 .066 .087 .002 .095 .0001 .089 .001 .099 .057
N 34,238 34,746 31,191 31,683 9,732 9,738 8,554 8,557 9,731 9,737 8,554 8,557
Note.RE random effects; FE within-child fixed effects; ref reference. Child gender, race, English Language Learner status, and pre-Kindergarten (K) attendance are controlled for in RE models
(not shown). Because the FE approach inherently controls for all stable, time-invariant variables including gender and race, these variables were not included in the FE regression models.
p .05.
p .01.
ⴱⴱ
p .001.
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8
MORRISSEY, HUTCHISON, AND WINSLER
Table 5
Predicting Student Achievement From Child Absence and Tardiness: Regression Results
Predicting mean standardized grades (K– 4th grades) Predicting math scores (3rd– 4th grades) Predicting reading Scores (3rd– 4th grades)
RE FE RE FE RE FE RE FE RE FE RE FE
Variable B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE) B (SE)
Absent 2
days (ref)
Absent 2–4 days .110
ⴱⴱ
(.008)
.080
ⴱⴱ
(.009)
.100
ⴱⴱ
(.022)
.019 (.034) .157
ⴱⴱ
(.024)
.044 (.037)
Absent 5–9 days .206
ⴱⴱ
(.008)
.144
ⴱⴱ
(.010)
.181
ⴱⴱ
(.024)
.052 (.041) .232
ⴱⴱ
(.026)
.023 (.045)
Absent 10–17
days .341
ⴱⴱ
(.010)
.215
ⴱⴱ
(.012)
.287
ⴱⴱ
(.029)
.053 (.053) .410
ⴱⴱ
(.032)
.039 (.058)
Absent 18
days .560
ⴱⴱ
(.013)
.296
ⴱⴱ
(.016)
.506
ⴱⴱ
(.050)
.093 (.089) .673
ⴱⴱ
(.054)
.079 (.098)
Tardy 2 days
(ref)
Tardy 2–4 days .075
ⴱⴱ
(.007)
.047
ⴱⴱ
(.008)
.123
ⴱⴱ
(.022)
.016 (.032) .091
ⴱⴱ
(.024)
.073
(.036)
Tardy 5–9 days .143
ⴱⴱ
(.039)
.088
ⴱⴱ
(.010)
.147
ⴱⴱ
(.027)
.035 (.043) .144
ⴱⴱ
(.029)
.050 (.047)
Tardy 10–17
days .194
ⴱⴱ
(.010)
.129
ⴱⴱ
(.012)
.210
ⴱⴱ
(.030)
.097 (.050) .207
ⴱⴱ
(.033)
.073 (.056)
Tardy 18
days .238
ⴱⴱ
(.010)
.140
ⴱⴱ
(.013)
.222
ⴱⴱ
(.030)
.095 (.056) .259
ⴱⴱ
(.033)
.001 (.062)
Child grade .022
ⴱⴱ
(.002)
.005
(.002)
.002 (.002) .006
(.002)
.167
ⴱⴱ
(.015)
.323
ⴱⴱ
(.060)
.168
ⴱⴱ
(.015)
.324
ⴱⴱ
(.016)
.038
(.016)
.078
ⴱⴱ
(.017)
.041
(.016)
.080
ⴱⴱ
(.017)
Constant .638
ⴱⴱ
(.020)
.131
ⴱⴱ
(.008)
.516
ⴱⴱ
(.020)
.046
ⴱⴱ
(.006)
3.987
ⴱⴱ
(.064)
4.090
ⴱⴱ
(.060)
3.943
ⴱⴱ
(.063)
4.093
ⴱⴱ
(.056)
3.601
ⴱⴱ
(.070)
2.957
ⴱⴱ
(.066)
3.484
ⴱⴱ
(.069)
2.895
ⴱⴱ
(.062)
R
2
(overall)
.114 .034 .090 .017 .076 .001 .069 .001 .091 .004 .076 .001
N 34,238 34,746 34,238 34,746 9,732 9,738 9,732 9,738 9,731 9,737 9,731 9,737
Note.K Kindergarten; RE random effects; FE within-child fixed effects; ref reference.
p .05.
p .01.
ⴱⴱ
p .001.
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9
INCOME, SCHOOL ATTENDANCE, AND ACHIEVEMENT
Hispanic, White) showed no differences in associations between
school lunch status, school attendance, and achievement, suggest-
ing that patterns in our sample are similar for girls and boys and
across racial/ethnic categories.
Discussion
This study examined relations between family income, school
attendance, and achievement among a large sample of children
Table 6
Testing the Attenuation Effects of Child Absence and Tardiness in the Relationship Between Free/Reduced Lunch Receipt and Student
Achievement
Predicting mean standardized grades
RE FE RE FE
Variable B (SE) B (SE) B (SE) B (SE)
Full-price lunch (ref)
Received reduced-price lunch .117
ⴱⴱ
(.011)
.012 (.014) .120
ⴱⴱ
(.011)
.013 (.014)
Received free lunch .233
ⴱⴱ
(.010)
.027
(.013)
.245
ⴱⴱ
(.010)
.027
(.013)
Absent fewer than 2 days (ref)
Absent 2–4 days .109
ⴱⴱ
(.008)
.079
ⴱⴱ
(.009)
Absent 5–9 days .204
ⴱⴱ
(.008)
.144
ⴱⴱ
(.010)
Absent 10–17 days .335
ⴱⴱ
(.010)
.215
ⴱⴱ
(.012)
Absent 18 or more days .548
ⴱⴱ
(.013)
.296
ⴱⴱ
(.016)
Tardy fewer than 2 days (ref)
Tardy 2–4 days .075
ⴱⴱ
(.007)
.047
ⴱⴱ
(.008)
Tardy 5–9 days .141
ⴱⴱ
(.009)
.088
ⴱⴱ
(.010)
Tardy 10–17 days .192
ⴱⴱ
(.010)
.129
ⴱⴱ
(.012)
Tardy 18 or more days .235
ⴱⴱ
(.010)
.140
ⴱⴱ
(.013)
Child grade .025
ⴱⴱ
(.002)
.005
(.013)
.006
(.002)
.006
(.002)
Constant .747
ⴱⴱ
(.021)
.151
ⴱⴱ
(.013)
.632
ⴱⴱ
(.020)
.066
ⴱⴱ
(.011)
R
2
(overall)
.134 .045 .113 .030
N 34,238 34,746 34,238 34,746
Note. RE random effects; FE within-child fixed effects; ref reference. Child gender, race, English Language Learner status, and pre-Kindergarten
attendance are controlled for in RE models (not shown). Because the FE approach inherently controls for all stable, time-invariant variables including
gender and race, these variables were not included in the FE regression models.
p .05.
p .01.
ⴱⴱ
p .001.
Table 7
Testing the Attenuation Effects of Child Absence and Tardiness in the Relationship Between Duration of Free/Reduced Lunch Receipt
and Student Achievement
Predicting mean standardized grades
RE FE RE FE
Variable B (SE) B (SE) B (SE) B (SE)
Number of years child received reduced-price lunch .045
ⴱⴱ
(.007)
.036
ⴱⴱ
(.009)
.044
ⴱⴱ
(.007)
.038
ⴱⴱ
(.009)
Number of years child received free lunch .174
ⴱⴱ
(.005)
.139
ⴱⴱ
(.006)
.179
ⴱⴱ
(.005)
.140
ⴱⴱ
(.006)
Absent fewer than 2 days (ref)
Absent 2–4 days .100
ⴱⴱ
(.008)
.074
ⴱⴱ
(.009)
Absent 5–9 days .188
ⴱⴱ
(.009)
.134
ⴱⴱ
(.010)
Absent 10–17 days .314
ⴱⴱ
(.010)
.206
ⴱⴱ
(.012)
Absent 18 or more days .531
ⴱⴱ
(.014)
.296
ⴱⴱ
(.017)
Tardy fewer than 2 days (ref)
Tardy 2–4 days .069
ⴱⴱ
(.007)
.040
ⴱⴱ
(.008)
Tardy 5–9 days .138
ⴱⴱ
(.009)
.083
ⴱⴱ
(.010)
Tardy 10–17 days .183
ⴱⴱ
(.010)
.121
ⴱⴱ
(.012)
Tardy 18 or more days .227
ⴱⴱ
(.010)
.135
ⴱⴱ
(.013)
Child grade .099
ⴱⴱ
(.004)
.092
ⴱⴱ
(.005)
.121
ⴱⴱ
(.004)
.104
ⴱⴱ
(.005)
Constant .652
ⴱⴱ
(.021)
.227
ⴱⴱ
(.010)
.541 (.021) .145
ⴱⴱ
(.007)
R
2
(overall)
.142 .094 .123 .080
N 31,191 31,683 31,191 31,683
Note. RE random effects; FE within-child fixed effects; ref reference. Child gender, race, English Language Learner status, and pre-Kindergarten
attendance are controlled for in RE models (not shown). Because the FE approach inherently controls for all stable, time-invariant variables including
gender and race, these variables were not included in the FE regression models.
ⴱⴱ
p .001.
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10
MORRISSEY, HUTCHISON, AND WINSLER
from kindergarten to fourth grade, and represents a contribution to
the literature in several important ways. First, we are among the
first to examine children’s tardiness to school, in addition to school
absence. Second, we focus on an understudied group, specifically,
ethnically and linguistically diverse, low-income, young children,
allowing us to examine the extent to which variation in income and
attendance matters for the early achievement of educationally
at-risk elementary school children. Third, we examine questions
new to the school absence literature (i.e., attendance attenuating
income–achievement links, age moderating attendance effects on
outcomes) using multiple, robust analysis techniques designed to
minimize selection bias.
Results suggest that the receipt of free or reduced-price lunch
and duration of receipt, used as proxies for low family income, had
generally positive but quite small associations with the number of
days children were absent from or late to school. However, build-
ing on previous research with older and more advantaged samples
(Gottfried, 2009, 2011; Reynolds et al., 1992; Steward et al.,
2008), we found that poorer school attendance was associated with
nonnegligible decreases in children’s grades. The relationship be-
tween school attendance and achievement was concurrent; that is,
in the same year, more days absent or times tardy were associated
with lower grades and test scores, and the strength of associations
between absences and grades grew as children advanced through
elementary school. In general, the relationship between school
absence and achievement, although moderate in magnitude, was
stronger than that between times tardy and achievement. In other
words, missing an entire day of school appears to be worse for
children’s school performance than missing part of the day. When
children arrive late, they have the opportunity to catch up on the
day’s lesson, whereas when children are absent the entire day,
teachers may not be able to review material from a prior day for
one child. Therefore, policies and practices that help children get
to school, even if late, may help improve achievement.
It is interesting to note that the number of absences and tardies
declined over time from kindergarten to fourth grade. This may be
because, as children grow older, parents recognize the importance
of school attendance, which is key given that this study also
revealed that the number of days absent became more important
for achievement as children grew older during elementary school.
Understanding age-related differences in relations between atten-
dance and child outcomes is important, but has, to date, been
hampered by the fact that most previous research on children’s
school attendance has typically involved older students (Steward
et al., 2008). Indeed, age differences in the influence of school
attendance on behavior were shown in a recent study in which,
among middle-school students, missing school was prospectively
associated with increases over time in conduct problems and
depression, but among high school students, directionality was
reversed—the mental health and behavioral concerns predicted
later absenteeism (Wood et al., 2012). One caveat worth noting,
however, is that another potential explanation for the decreasing
number of absences and tardies with age may be that children with
particularly poor attendance could have been those retained in-
grade and thus excluded from our sample.
One of the goals of this work was to discover the extent to which
school attendance can explain associations between income and
school achievement. The strong version of this hypothesis would
see school attendance as the main mechanism through which
family income affects children’s early achievement, such that
when attendance is included in the models, the effect of income on
achievement would disappear altogether. A softer version of this
hypothesis would expect only partial attenuation, with income–
achievement associations reducing somewhat with the inclusion of
attendance in the analyses. Given the relatively small associations
identified between low family income and school attendance, and
the very small or null findings between low family income and
children’s achievement, it was not surprising that we found that,
technically, school absences and tardies only partially attenuated
the effects of family income on academic achievement; however,
given the large sample size, the small change in the coefficients,
and the size of the standard errors, results do not provide evidence
that school attendance attenuates links between family income and
achievement in a meaningful way. Thus, there appear to be other
mechanisms through which family income influences school
achievement in the early years of school. It is important to recall
that our at-risk sample represents a restricted range of income, and
that fact, combined with our conservative analysis techniques,
limits our likelihood of identifying larger effects.
As with all research, this study has several limitations. First, our
RE models are susceptible to omitted variable bias from time-
invariant characteristics, and both the RE and FE models are
subject to omitted variable bias from time-varying characteristics,
or from time-invariant factors that change their relationships with
the outcomes over time. Furthermore, our FE models examined
how changes in family income related to children’s school atten-
dance, and how changes in children’s attendance patterns relate to
achievement; such changes in income or school attendance may
also co-occur with other potentially disruptive events in children’s
lives, such as changes in household structure. Unfortunately, many
family characteristics such as household composition or parental
employment were not measured, and thus we were unable to
control for these, potentially biasing our results.
A second limitation pertains to the sample. As noted above,
although ethnically, racially, and linguistically diverse, our sample
had a restricted income range. Due to the original nature of the
project (to evaluate the progress of children from economically
disadvantaged families), many of those children who did not
qualify for free or reduced-price lunch in a particular academic
year were likely not living very far above the eligibility line. This
restricted income range may explain our lack of large or significant
findings regarding family income and school attendance or
achievement. It is important to note that a number of studies using
samples with greater income variation find that relatively small
increases in income may be meaningful for children’s academic
performance (Dahl & Lochner, 2005), with implications for the
levels of support provided by antipoverty programs. Additionally,
because of the nature of the original study, our sample included
only children who attended prekindergarten or licensed or informal
childcare in the year prior to kindergarten, excluding those in
exclusive parent care during that year. Furthermore, and similar to
other work in this area, we restricted our sample to children who
progressed on time through elementary school and who did not
have special needs. Such children may have displayed better
attendance patterns than their retained peers or those with special
needs. Children from low-income families are more likely to be
retained in-grade (Alexander, Entwisle, & Dauber, 2003) and to
have health problems (Currie, 2005), and therefore, it is possible
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11
INCOME, SCHOOL ATTENDANCE, AND ACHIEVEMENT
that we missed a subsample of low-performing children with
higher rates of absences and tardies. In addition, some children
who moved away from the school district were lost from the
sample. However, in our study, unlike others, children had to move
out of the county and/or stop attending public school altogether in
order to be lost—we included children who moved to other neigh-
borhoods within the large county.
Finally, the study’s measures were limited, particularly our lack
of a specific assessment of family income. Although receipt of
free- or reduced-lunch status is an often-used indicator of family
income, it is possible that receipt of free or reduced-price lunch
does not map on to family economic resources in this sample and
that changes in receipt of free or reduced-price lunch do not
accurately reflect changes in family income (Harwell & LeBeau,
2010), which may underlie our largely null findings regarding
family income and children’s achievement and of attenuating
effects. Parents or educators may not consistently complete or
return the appropriate forms each year, and thus a failure to
reenroll children in the program could result from reasons other
than an increase in family income, which would threaten the
validity of our measure. Furthermore, our study did not take into
account measures of school or neighborhood quality. Prior re-
search indicates that higher quality schools may have more of an
impact on the academic achievement of children from less advan-
taged backgrounds compared with those from more advantaged
backgrounds (Raudenbush, 2009; Ready, 2010). It may be that
children from low-income families who attend higher quality
schools are more negatively impacted when they miss school (i.e.,
they have more to lose) than similarly poor children attending
lower quality schools. Other studies emphasize the importance of
neighborhood characteristics on children’s educational achieve-
ment (Leventhal & Brooks-Gunn, 2004).
Conclusions
This study builds on the existing literature by demonstrating that
children’s school attendance patterns are linked with achievement
in elementary school among a predominantly disadvantaged sam-
ple. Ensuring that children attend school, even if late, may be one
way to enhance achievement among low-income children. Provid-
ing transportation or tracking and following up with students who
are chronically absent or tardy may help encourage student atten-
dance, and in turn, increase achievement. However, we identified
very small associations between low-income status and school
attendance within this generally disadvantaged sample, and largely
null findings between low family income and children’s achieve-
ment. More research is needed to shed light on other mechanisms
through which family income affects children’s achievement that
may serve as policy levers to help close the achievement gap.
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Received June 2, 2011
Revision received November 27, 2012
Accepted May 6, 2013
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INCOME, SCHOOL ATTENDANCE, AND ACHIEVEMENT
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Plain language summary School feeding program effects Our study aimed to assess the impact of school feeding programs introduced to address hunger and enhance student performance. Specifically, we examined the effects of such programs on pupil enrollment, attendance, and academic achievement in northeastern Nigeria. Data for this research were collected from 180 class teachers at sixty participating primary schools, complemented by secondary data extracted from school records. The findings from our linear regression analysis highlighted a significant positive correlation between the duration of the feeding program and pupils’ academic performance. Propensity scores matching results, on the other hand, indicated that the school feeding program had a positive influence on pupil enrollment and classroom attendance. These findings align closely with the “Human Capital Theory,” emphasizing the pivotal role of adequate nutrition in facilitating cognitive development and academic success. The observed improvements in enrollment, attendance, and academic performance in schools with school feeding programs can be attributed to the provision of nutritious meals, addressing students’ fundamental physiological needs, and enhancing their ability to focus on learning. However, it’s important to acknowledge some limitations in our study. We lacked baseline and recall data at the pupil level, as well as information on the socioeconomic backgrounds of students’ households. Factors such as parental education, household income, and food security status could influence pupil enrollment, attendance, and academic performance. Future research efforts may shed more light on these aspects.
... Other programs employing similar partnership approaches have also found significant impacts on school attendance (Sheldon, 2007;Childs & Grooms, 2018). Given the clear connection between school attendance, school grades (Gottfried, 2010;Gershenson, Jacknowitz, & Brannegan, 2017;Morrissey, Hutchison, & Winsler, 2014), and achievement (Gottfried, 2010;Gottfried, 2019) it seems likely that the sustained impact on reading achievement and reduced take up rate of former Future Forward students for special education was partially due to students being in school more often. ...
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... achievement. Researchers consistently described chronic absenteeism as associated with negative impacts, such as reduced academic achievement and school dropout (Balfanz & Byrnes, 2012;Garcia & Weiss, 2018;Kearney & Graczyk, 2020;Morrissey et al., 2014). In their meta-analysis of COVID-19 related literature, Hammerstein et al. (2021) reported that the majority of the studies indicated negative impacts of pandemic-related school closures on academic achievement. ...
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