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The Effect of ProvidingBreakfast in Class on StudentPerformance
Scott A. ImbermanAdriana D. Kugler
Abstract
Many schools have recently experimented with moving breakfast from the cafeteria tothe classroom. We examine whether such a program increases achievement, grades,and attendance rates. We exploit quasi-random timing of program implementationthat allows for a difference-in-differences identification strategy. We find that provid-ing breakfast in class relative to the cafeteria raises math and reading achievementby 0.09 and 0.06 standard deviations, respectively. These effects are most pronouncedfor low-performing, free lunch-eligible, Hispanic, and low body mass index students.A lack of differences by exposure time and effects on grades suggest that these im-pacts are on test-taking performance rather than learning. At the same time, the resultshighlight the possibility that measured achievement may be biased downwards, andaccountability penalties may be inappropriately applied, in schools where many stu-dents do not consume breakfast. C© 2014 by the Association for Public Policy Analysisand Management.
INTRODUCTION
Nutritionists have long been concerned that children in the United States do notconsume enough breakfast. Indeed, it is estimated that between 12 and 35 percentof children in the United States skip breakfast (Gardner, 2008) despite the provisionof free or low-cost breakfasts in school for low-income students through the SchoolBreakfast Program (SBP) since 1975. Unfortunately, participation in the SBP is low.At most 60 percent of students eligible for free breakfast participate in the program(Dahl & Scholz, 2011). This could be due to “time/scheduling conflicts, [lack of]cafeteria space or the embarrassment associated with eating a free or reduced-price breakfast” (Cullen, 2010). Further, while the intent is for the SBP to increasebreakfast consumption, it is possible that children with access to an SBP may eatless at home. Waehrer (2008) looks at time-diary data and finds that children inthe SBP actually consume less on weekdays than weekends suggesting that theprogram may reduce consumption, although it is not clear what the students wouldhave eaten on weekdays in the absence of the program. Finally, even if studentscome from families that can afford to purchase breakfast and are ineligible for SBP,many children skip due to time constraints.
Due to the low take-up of free and reduced-price meals, and clinical evidencethat breakfast improves cognitive performance (Alaimo, Olson, & Frongillo, 1999;Middleman, Emans, & Cox, 1996; Wesnes et al., 2003), a number of school districts
Journal of Policy Analysis and Management, Vol. 33, No. 3, 669–699 (2014)C© 2014 by the Association for Public Policy Analysis and ManagementPublished by Wiley Periodicals, Inc. View this article online at wileyonlinelibrary.com/journal/pamSupporting Information is available in the online issue at wileyonlinelibrary.com.DOI:10.1002/pam.21759
670 / The Effect of Providing Breakfast in Class
around the country have tried to reduce the time and effort costs of eating breakfastby moving the meals from cafeterias to the classrooms. School districts in Chicago,Dallas, Florida’s Orange County, Houston, Little Rock, Maryland’s Prince George’sCounty, Memphis, New Mexico, New York City, and San Diego have all movedbreakfast to classrooms. Providing breakfast in class avoids space and schedulingproblems, reduces time constraints on students in the morning, and eliminates theneed for students who want to eat breakfast in school to arrive early. In addition,given that breakfast is offered to all students, and not only those who come tothe cafeteria in the morning, the stigmatization from getting a subsidized meal isplausibly eliminated. School districts hope that this leads to increased consumptionof breakfast that in turn increases students’ alertness and their capacity to learn.However, there are some potential disadvantages. First, it is possible that studentswill double up and eat breakfast both at home and at school, which could contributeto obesity. Second, there could be increased financial costs in providing breakfastin the classroom due to higher consumption and clean-up costs. Third, there areconcerns that the time it takes to serve and eat the breakfast may reduce instructiontime, though surveys of principals conducted by the district we studied suggest theimpact on instructional time is minimal.
In this paper, we assess the impact from the implementation of a free in-classbreakfast (ICB) program in a large urban school district in the Southwest UnitedStates (LUSD-SW) on students’ academic performance and attendance relative toproviding free breakfast in the cafeteria.1 LUSD first piloted the project in 33 schoolsand later expanded it to all elementary and middle schools starting on February 2,2010, with the rollout finishing in fall 2010. A nice feature of the rollout for thepurposes of our empirical analysis is that the timing of implementation had littleto do with school characteristics. While the rollout was initially slated to start inschools with a higher share of disadvantaged students, in practice the implemen-tation did not work out this way. First, some schools had rollout dates changedto accommodate logistical necessities (i.e., having schools in the same areas startaround the same time) or principals’ requests. Second, and perhaps more impor-tantly, 65 percent of elementary schools in LUSD have economic disadvantage ratesof 90 percent or higher.2 As a consequence, during the first 11 weeks of the programrollout, there is remarkably little variation in terms of economic disadvantage ratesand other characteristics of the schools across implementation dates. For example,the mean economic disadvantage rate in 2008 to 2009 for schools adopting in week 1is 93.5 percent while the mean in week 11 is 91.5 percent. This indicates that schoolsthat implemented the program early differed little from later adopters as measuredby observable characteristics.3 More importantly given our difference-in-differencesidentification strategy, early and late adopters have virtually no differences in trends.
We test the validity of the difference-in-differences assumptions in three ways.First, we show that the timing of adoption is mostly uncorrelated with school char-acteristics and changes in those characteristics conditional on the school adopting inthe first 11 weeks of implementation. Second, we estimate placebo tests in the spiritof Angrist and Krueger (1999) that estimate the difference-in-difference impact of
1 Researchers seeking access to the data for replication should contact the authors, at which point wewill identify the district for the requestors and provide instructions on how to submit a research proposalto the district’s research department.2 A student is considered economically disadvantaged if she qualifies for free lunch, reduced-price lunch,or another federal or state antipoverty program.3 A small portion of schools in LUSD have relatively low disadvantage rates. These schools mostly begantheir programs after the 11th week of the rollout. As a consequence, schools that adopted in the 12th weekand later differ substantially from those that adopted earlier. Hence, we only consider schools adoptingduring the first 11 weeks of the program in our analysis.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 671
adoption using only pre-ICB data. This checks for whether underlying trends maybe influencing our results. These tests provide little evidence of any such trending.Third, we test whether there are difference-in-differences impacts on contempora-neous exogenous covariates and find very few significant effects.
Using schools that adopt during weeks 1 through 11, a time period that coversthe testing days for the fifth-grade state exam in week 9, we assess the impactof providing breakfast in class on achievement, grades, and attendance. We findthat achievement increased in schools that adopted ICB before testing comparedto schools where ICB was adopted after testing. In particular, the introduction ofbreakfast in the classroom increased test scores by 0.09 standard deviations (SDs) inmath and 0.06 in reading. Moreover, these effects were larger for students with lowpreprogram achievement, those who qualified for free lunch, Hispanics, childrenwith limited English proficiency (LEP), and students with a low body mass index(BMI). However, we find little evidence that impacts vary with exposure time, andwe also find little evidence that ICB affected attendance or grades. Together, thesefindings suggest that the program likely helps students perform better on exams,but does not appear to affect learning. This is indicative of a phenomenon similar tothat found in Figlio and Winicki (2005) whereby schools increased calories in mealsto improve test performance. Nonetheless, our evidence only provides suggestivesupport for this theory since, due to the short implementation window, we cannotrule out longer-term effects of exposure time, and the lack of grade impacts couldreflect teachers adjusting their grading curves as students improve. Even short-term impacts, however, have important policy implications. The results imply thatachievement scores in schools with low breakfast consumption may suffer from adownward bias relative to high consumption schools. As a result, providing univer-sal ICB may avoid situations where schools with large disadvantaged populationssuffer accountability penalties and, as a result, receive fewer resources because theirstudents have not received appropriate nutrition on testing days.
PREVIOUS LITERATURE
There is an extensive literature on the link between nutrition and education inthe developing country context. In general, the studies that are able to establishcausal effects tend to find that good nutrition increases educational performance(Alderman et al., 2001; Alderman, Hoddinott, & Kinsey, 2006; Glewwe, Jacoby, &King, 2001; Maluccio et al., 2009; Vermeersch & Kremer, 2004).
In contrast to studies in the developing world, most research on the effects ofnutrition on learning in developed countries has been conducted by physiciansand public health experts.4 The most credible studies for developed countries haveinvolved experimental trials that randomly assigned kids to receiving breakfast orno breakfast in a given week and the following week switched the assignment. Thebest of these include Pollitt, Leibel, and Greenfield (1981) and Pollitt et al. (1982)who find little impact of the change in diet on cognitive performance.
Most research on the impact of food programs in school focuses on how theseaffect nonacademic outcomes such as nutrient intakes (Devaney & Fraker, 1989;Gleason & Suitor, 2003; Grainger, Senauer, & Runge, 2007), food expenditures(Long, 1991), food insecurity (Bartfeld & Ahn, 2011; Gundersen, Kreider, & Pep-per, 2012), obesity (Anderson & Butcher, 2006; Gundersen, Kreider, & Pepper,2012; Hofferth & Curtin, 2005; Millimet, Tchernis, & Hussein, 2010; Schanzenbach,2009), and other health outcomes (Bernstein et al., 2004; Bhattacharya, Currie, &Haider, 2006). Other studies that consider the effects of these programs on academic
4 See Rampersaud et al. (2005) for a review.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
672 / The Effect of Providing Breakfast in Class
outcomes in the United States use nonexperimental data and for the most part dosimple comparisons of schools participating in the School Breakfast and LunchPrograms to nonparticipating schools. While some of these studies do not controlfor other school and student characteristics in the participating and nonparticipat-ing schools, others attempt to control for observable differences. However, evencontrolling for observable differences may not be enough, since schools may self-select into participating in the program on the basis of unobservable characteristics(e.g., local wealth levels).5 Likewise, other studies compare children eligible forfree/reduced-price meals to those not eligible, but these students differ on the basisof unobservable characteristics both at the school and student levels. One studyby Meyers et al. (1989) finds that the Massachusetts SBP is associated with highertest scores and lower levels of tardiness and absences, but does not control for theselection described above. Dunifon and Kowaleski-Jones (2003), on the other hand,address potential selection into free/reduced-price meal eligibility by comparingchildren within the same family—one of whom attends a school with a school mealprogram, while the other attends a school without a program. There may still be theproblem that parents send the children with greater nutritional needs to a schoolwith a meal program, while sending the other child to a school without a program,but this study reports that the likelihood of split participation is not associated withimproved child-specific factors (including health status or BMI).
Studies on the impact of school meal programs on student outcomes using naturalexperiments tend to find positive effects. Hinrichs (2010) exploits differences ineligibility rules across cohorts and states for free/reduced-price lunch. He findsthat those who participated in the program as children experienced sizable andsignificant increases in educational attainment. In a recent paper, Frisvold (2013)exploits threshold variation across states and schools in the percent of free andreduced-price eligible students that mandate schools to adopt breakfast programs,and finds positive effects on student achievement. Additionally, Leos-Urbel et al.(2012) look at the implementation of universal free breakfast in New York Cityand find increased uptake and small positive effects on attendance for blacks andAsians. The exception to this pattern of positive results is Bernstein et al. (2004)who conducted randomized experiments in six school districts across the countrythat provide breakfast in some schools, but not others. They find little evidence ofimprovements in terms of achievement, attendance, or discipline.6
Our analysis is closest to Hinrichs (2010), Frisvold (2013), and Leos-Urbel et al.(2012) in that we conduct a nonexperimental analysis exploiting the differentialtiming in a change to a breakfast program. We study schools in a LUSD-SW inorder to identify the effect of providing breakfast to all students in the classroomrelative to providing breakfast in the cafeteria. We theorize that this reduces the timeand effort costs for students to consume breakfast and potentially impacts studentperformance including grades, test scores, and attendance. To our knowledge, theonly quasi-experimental study of ICB implementation is unpublished work by Dotter(2012). He examines a universal ICB program in San Diego and finds effects on testscores of 0.10 to 0.15 SD, but does not find impacts on student behavior. However,
5 For example, Anderson and Butcher (2006) find that schools under financial pressure tend to adoptpotentially unhealthy food policies.6 About 20 percent of the schools in Bernstein et al. (2004) provided breakfast in the classroom, butultimately whether to provide breakfast in the classroom or the cafeteria was decided by the principaland, hence, that portion of the study was not randomized. As a result, their estimates on this treatmentare potentially biased. Nonetheless, while they find greater take-up of breakfast when it is served in theclassroom than when it is served to all students in the cafeteria, they find no significant impact of thein-classroom breakfast on achievement.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 673
in schools that already offered free breakfast in the cafeteria—which is the case forall schools in LUSD—he finds no significant effect, in contrast to our findings ofpositive effects, despite finding increased participation in both these schools andthose that did not previously provide universal breakfast. Given that many schooldistricts are adopting these programs it is important to assess what impact, if any,they might have.
THE ICB PROGRAM
The LUSD ICB program provides free breakfast to all students in their classroomsat the beginning of the school day. Prior to the introduction of the ICB program, allstudents were able to get a free breakfast in the cafeteria before the start of class.7
Thus, there are a few simultaneous changes as a result of the introduction ofthe ICB program. First, the venue of the breakfast was changed to the classroom,making it easier to get breakfast. Second, the timing of the breakfast was changedto the beginning of the school day, rather than before the start of class, potentiallyreducing instructional time. However, in interviews conducted by the school district,93 percent of principals report that breakfast is administered in less than 15 minutes.Further, it is unlikely that the ICB program reduces instructional time as the periodof time during which breakfast is served is typically used by teachers and schoolofficials to take attendance, provide announcements, collect homework, and plan therest of the day, activities that can all be done concurrent with breakfast consumption.At the same time, it is possible that under the cafeteria-based system some studentswho wanted to eat breakfast could not do so without being late to class.
We interpret this movement of breakfast from the cafeteria to the classroom asa reduction in the cost to the student of consuming breakfast. First, the change invenue implies a reduction in time cost and an increase in convenience since thestudent does not need to arrive at school early or walk to the cafeteria. Second,although all students had access to cafeteria breakfast prior to ICB, some stigmaeffects from eating breakfast in the cafeteria may have remained. These reductionsin costs likely led to an increase in calorie consumption on average. Unfortunately,we do not have the data to test directly for whether students consume more calories.8
However, while it is feasible that some students may not change their behavior, it islikely that serving breakfast in the classroom will cause other students to consumemore food overall. First of all, Bernstein et al. (2004) report that student take-up ofbreakfast at schools that provide free universal breakfast in the classroom is higherthan in schools that provide free universal breakfast, but serve it in the cafeteria.Second, a comparison of 33 pilot schools in the LUSD, which began providing ICBin 2008 to 2009,9 to nonpilot schools in the district found that while 80 percent ofstudents in the former ate breakfast in school during 2008 to 2009, only 41 percentof the latter did so.10 Third, we know from six case studies of elementary schools
7 LUSD started providing free breakfast to all students, not just those eligible under the National SchoolBreakfast Program, in 2006 to 2007.8 Food consumption data are collected by the food service department of the school district and isnot linked to student data. Moreover, the food consumption information collected by the district’s fooddepartment only includes breakfast and lunch consumed at the school and would not provide informationon whether the students ate breakfast at home or the total calories consumed by the student during aday.9 We exclude these 33 pilot schools from all estimates of the impact of the in-class breakfast programas they were chosen on the basis of where food administrators thought the program could be bestimplemented.10 It is not obvious whether this is due to more students eating breakfast who were not before, sincesome of the new in-school eaters may have been eating breakfast at home, or the difference may reflect
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
674 / The Effect of Providing Breakfast in Class
conducted by the district before and after the 2009 to 2010 implementation of theICB program that take-up of breakfast increased from 33 to 83 percent in theseschools. While these schools are more disadvantaged than the average school inthe district, they are similar to schools in our sample as our identification strategyrequires us to focus on very low-income schools. Further, one of the schools is similarto the average elementary school on observable characteristics. In this school, uptakeincreased from 29 to 73 percent. All together this evidence suggests that uptakeincreased by between 40 and 50 percentage points, on average.
The caloric content of the breakfast served in LUSD hardly changed after theICB program was introduced, so any change in calorie intake would come fromincreased breakfast consumption. Students are given an entree that could be hot orcold (i.e., yogurt, chicken biscuits, pop-tarts, mini pancakes) usually with a snackitem (i.e., fruit, blueberry muffins, graham crackers), juice, and milk. On average, astudent could consume up to 534 calories from the breakfast. This is comparable tothe meals offered in the cafeteria before ICB implementation where a student couldconsume up to 520 calories on average.11 In addition, a comparison of availablecaloric information of breakfast served in the cafeteria and breakfast served in theclassroom on exam days shows little difference in the menus.12 Rather, it seems thatit is increased consumption that changed after the introduction of the ICB program.
In 2009 to 2010, LUSD started implementing the program in the nonpilot schools.All but a handful of elementary schools started in that year, while the rest of the ele-mentary schools and secondary schools began ICB early in the 2010 to 2011 schoolyear. Given this timing, we only assess elementary schools in this study. The initialintention was to implement the program in new schools each week starting with theschools with the highest rates of economically disadvantaged students and endingwith the lowest. However, in practice the implementation did not occur this way.Adoption dates were modified for a number of logistical reasons such as principalrequests for delays or to facilitate initial food deliveries. This combined with the factthat 65 percent of LUSD elementary schools had economic disadvantage rates above90 percent made schools that adopted during the beginning of the 11-week periodfrom February 2, 2010 to April 20, 2010 remarkably similar on observable charac-teristics to those that adopted toward the end of the period. Hence, we argue thatthe implementation was quasi-random, and we identify treatment effects using adifference-in-differences framework. That is, we assume that the timing of adoptionduring the first 11 weeks of the rollout is uncorrelated with trends in unobservedschool characteristics conditional on observable student characteristics, observableschool characteristics, and prior achievement.
Table 1 provides support for this assumption. In this table, we provide some char-acteristics of elementary schools that adopted ICB at different times. New schoolsimplemented the program every week from February 2, 2010, through Septem-ber 21, 2010, with gaps during testing periods, spring break, and summer break.This table shows that among schools that implemented the program from February2, 2010, through April 20, 2010, the week of adoption is uncorrelated with many
selection of schools into the pilot program. Nonetheless, given that principals entered the pilot voluntarily,it is likely they did so in response to low in-school consumption, which would imply that this is anunderestimate of the actual effect on take-up.11 Calorie counts based on authors’ calculations from school menus and nutrition information.12 On April 6, 2010, the day of math testing, schools serving breakfast in the cafeteria served fouritems out of a choice of French toast sticks, cereal, Colby omelet, fruit, and juice, while schools servingbreakfast in class served waffle sticks, animal crackers, orange juice, and milk. On April 7, 2010, the dayof reading testing, schools serving breakfast in the cafeteria served four items out of a choice of breakfasttacos, cereal, skillet brown potatoes, fruit, and juice, while schools serving breakfast in class served minipancakes, animal crackers, juice, and milk.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 675
Tab
le1
.Mea
ns
ofel
emen
tary
sch
oolc
har
acte
rist
ics
by
wee
kof
imp
lem
enta
tion
ofin
-cla
ssb
reak
fast
(tes
tsa
mp
le).
Wee
kS
tart
dat
eIm
por
tan
tev
ents
No.
ofsc
hoo
ls
Eco
nom
ical
lyd
isad
van
tage
d(%
)B
lack
(%)
His
pan
ic(%
)L
EP
(%)
Ave
rage
teac
her
exp
erie
nce
(yea
rs)
Stu
den
t–te
ach
erra
tio
Att
end
ance
rate
Per
-pu
pil
exp
end
itu
reM
ean
mat
hac
hie
vem
ent
Mea
nre
adin
gac
hie
ve-
men
t
(Est
imat
ion
sam
ple
incl
ud
eson
lyw
eeks
1to
11)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(A)
Lev
els
in20
08to
2009
1F
ebru
ary
2,20
107
93.5
23.7
72.8
50.4
11.6
16.8
96.6
6,91
3−0
.13
−0.1
92
Feb
ruar
y9,
2010
895
.634
.364
.437
.910
.716
.896
.87,
367
−0.1
2−0
.09
3F
ebru
ary
16,2
010
En
dof
fou
rth
atte
nd
ance
per
iod
596
.731
.867
.644
.711
.216
.697
.37,
003
0.17
0.13
5M
arch
2,20
1013
95.3
28.2
69.3
45.1
11.1
16.1
96.5
6,61
5−0
.24**
*−0
.18**
*
6M
arch
9,20
10E
nd
ofth
ird
grad
ing
cycl
e13
94.9
33.4
65.1
43.5
10.0
15.9
97.2
7,63
5.9*
−0.0
9−0
.09
7M
arch
23,2
010
1094
.734
.363
.940
.312
.416
.096
.87,
091
−0.0
7−0
.09
8M
arch
30,2
010
994
.032
.965
.743
.312
.314
.897
.27,
285
0.01
0.01
9A
pri
l6,2
010
Fif
th-g
rad
ete
stin
g,en
dof
fift
hat
ten
dan
cep
erio
d
1090
.329
.968
.744
.49.
3***
16.7
*97
.26,
588
0.15
0.14
10A
pri
l13,
2010
1092
.818
.178
.352
.311
.9**
*15
.8*
97.2
6,93
3−0
.01
−0.1
6***
11A
pri
l20,
2010
991
.527
.166
.150
.311
.816
.897
.06,
637
−0.0
3−0
.05
Ap
ril2
7,20
10T
hir
d-
and
fou
rth
-gra
de
test
ing
Join
tF
-tes
tfo
rw
eeks
2to
11(F
-sta
tist
ics;
wee
k1
omit
ted
)0.
70.
40.
30.
51.
21.
70.
82.
1**1.
62.
0**
Est
imat
eof
dif
fere
nce
bet
wee
nw
eeks
10–1
1an
d1–
8−3
.1**
−1.6
−3.0
5.1
0.5
0.8
0.0
−549
*0.
050.
05
(Sta
nd
ard
erro
r)(1
.3)
(11.
7)(1
1.5)
(8.2
)(1
.1)
(0.6
)(0
.3)
(288
)(0
.11)
(0.0
9)13
May
4,20
109
88.2
**15
.678
.148
.412
.616
.697
.46,
974.
1**0.
110.
0614
May
11,2
010
769
.4**
*41
.3**
*40
.2**
*23
.7**
*14
.215
.6**
*96
.5**
*7,
096.
6***
0.02
0.20
15M
ay17
,201
07
58.4
*17
.0**
*51
.630
.811
.1**
16.7
***
97.1
6,41
60.
100.
2116
Sep
tem
ber
14,
2010
1124
.5**
*9.
7*26
.9**
*13
.2**
*13
.8*
17.1
97.2
6,31
00.
44**
*0.
57**
*
17S
epte
mb
er21
,20
101
(B)
Ch
ange
sfr
om20
06–2
007
thro
ugh
2008
–200
91
Feb
ruar
y2,
2010
70.
0−1
.40.
94.
20.
7−0
.60.
087
80.
180.
132
Feb
ruar
y9,
2010
82.
5−1
.51.
02.
5−0
.5−0
.5−0
.11,
409**
−0.0
20.
013
Feb
ruar
y16
,201
0E
nd
offo
urt
hat
ten
dan
cep
erio
d
50.
1***
−1.0
1.0
4.0
0.8
−0.4
0.5**
1,09
50.
130.
10
5M
arch
2,20
1013
0.9
−0.7
0.8
2.8
0.8
0.0
0.3
1,11
6−0
.05
0.00
6M
arch
9,20
10E
nd
ofth
ird
grad
ing
cycl
e13
0.4
0.0
0.2
1.7
0.7
0.0
0.4
1,15
5−0
.06
−0.0
6
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
676 / The Effect of Providing Breakfast in Class
Tab
le1
.Con
tin
ued
.
Wee
kS
tart
dat
eIm
por
tan
tev
ents
No.
ofsc
hoo
ls
Eco
nom
ical
lyd
isad
van
tage
d(%
)B
lack
(%)
His
pan
ic(%
)L
EP
(%)
Ave
rage
teac
her
exp
erie
nce
(yea
rs)
Stu
den
t–te
ach
erra
tio
Att
end
ance
rate
Per
-pu
pil
exp
end
itu
reM
ean
mat
hac
hie
vem
ent
Mea
nre
adin
gac
hie
ve-
men
t
7M
arch
23,2
010
101.
8−1
.51.
93.
6−0
.2−1
.0*
0.3
930
0.12
0.10
8M
arch
30,2
010
91.
9−4
.05.
06.
10.
0−1
.10.
41,
081
0.12
0.12
9A
pri
l6,2
010
Fif
th-g
rad
ete
stin
g,en
dof
fift
hat
ten
dan
cep
erio
d
10−0
.4−1
.01.
62.
4−0
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Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 677
observable characteristics, including percent of students economically disadvan-taged, black, Hispanic, with LEP, average teacher experience and tenure, student–teacher ratio, and attendance in the 2008 to 2009 school year.13 Joint significancetests only show significant differences across weeks in per-pupil expenditure andreading scores. Tests comparing weeks 1 through 8 (schools that adopt prior totesting) to weeks 10 and 11 (posttesting adopters) also show few differences in char-acteristics between schools. A notable exception is economic disadvantage rates,which are 3.1 percentage points lower in posttesting adopters. However, while sta-tistically significant, this difference is economically small, and we control for thisand other time-varying school characteristics in our empirical analysis below.
More importantly for our difference-in-differences identification strategy, panelB of Table 1 shows that the schools in our main sample where the program wasintroduced between February 2 and April 20, 2010, do not differ in terms of changesbetween 2006–2007 and 2008–2009 in any of the above-mentioned characteristics.t-Tests of differences between pre- and posttesting adopters show no significantdifferences, nor do joint F-tests show significant differences across the first 11 weeks.This suggests that initially the program was introduced in a close to random manner,at least conditional on fixed school characteristics. Later, we test this assumptionfurther through estimates of impacts on exogenous covariates and placebo tests thatlook for impact estimates using only preimplementation data.
ESTIMATION STRATEGY
To implement our difference-in-differences strategy, we estimate the following re-gression to look at the effects of the ICB program on student achievement:
Yijt = α + β Postt × IC Bj + γ1Yijt−1 + γ2Yijt−2 + ψ j + τt + �Xi j t + �Z jt + εi j t, (1)
where Yijt is student test scores, grades, or absenteeism for student i, in school j, attime t. Xijt includes race, gender, and indicators for whether the student qualifiesfor free lunch, reduced-price lunch, or is otherwise economically disadvantaged,and grade fixed effects. The regression also controls for school characteristics Zjt,such as the percent of students of each race/ethnicity in the school, economicallydisadvantaged, of LEP, in special education, in bilingual education, in each gradelevel, or referred to an alternative disciplinary program. Moreover, we include schoolfixed effects ψ j, to control for time-invariant unobservable characteristics of theschools, such as the quality of the teachers and principal, and we also include timefixed effects τt, to control for time-varying factors that affect all schools. Finally, weinclude two lags of the dependent variable to capture persistence in achievement,grades, and attendance, and to help account for any preexisting trends.14
This specification makes our analysis a difference-in-differences model wherechanges in outcomes before and after program implementation for earlier adoptersare compared to changes in outcomes for schools that adopt late in the process.The difference-in-differences impact of the program is captured by the estimate
13 One concern may be that the district implemented the program first in schools at risk of sanctionsfrom accountability regimes. However, none of the schools in our sample received the state’s lowestaccountability rating in the year prior to implementation, and none were subject to sanctions.14 Andrabi et al. (2011) show that it is important to add lagged achievement to education productionfunctions in order to account for persistence in achievement. When we estimate models with a singlelag, our estimates are slightly larger, but we also find a slight increase in achievement in pretest adoptersrelative to posttest in the year prior to implementation. We add a second lagged achievement score tothe regressions to account for this trend.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
678 / The Effect of Providing Breakfast in Class
for Postt × ICBj, which is an interaction of a dummy for being in a period afterthe introduction of the program, Postt, with an indicator of whether the schoolimplemented the program prior to fifth-grade testing in 2009 to 2010, ICBj (i.e.,an indicator for whether the ICB was implemented in weeks 1 through 8). Fortest scores, fifth-grade students took the state accountability exams in reading andmath on April 6 and 7. Hence, for these students, we will estimate equation (1)by comparing schools where ICB started prior to April 6 to those where it startedafterwards but before April 27.15 Since it is unclear whether schools that implementduring the week of April 6 provide the program to fifth-grade students due to thetesting, we drop all schools that adopt during this week (i.e., week 9) from ouranalyses.
The difference-in-differences framework described above only requires that trendsfor early adopters do not differ from trends for late adopters conditional on observ-able controls. We argue that the implementation is quasi-random in the sense that itis unrelated to underlying trends once we control for student characteristics, schoolcharacteristics, and two achievement lags. Below, we provide evidence that indicatesthe program implementation satisfies this assumption.
The difference-in-differences analysis can be extended to include the duration ofexposure to the ICB program, ICB_Durationjt, or intensity of treatment as follows:
Yijt = α + β1 Postt × IC Bj + β2 IC B Durationjt + γ1Yijt−1 + γ2Yijt−2 + ψ j + τt
+�Xit + �Z jt + εi j t. (2)
After controlling for student characteristics, school characteristics, and schoolfixed effects, we may expect students in schools that have participated longer in theICB program to have improved nutrition and to have better achievement. If this istrue then the estimate for β2 should be positive and significant. On the other hand,it is possible that any benefits accrue merely from a day of testing effect wherebythe extra calories boost concentration on the exam, but do little to improve generallearning. In this case, we should see statistically insignificant estimates of β2 thatare close to zero. We will also provide estimates from a more flexible version ofmodel (2) as follows:
Yijt = α +∑
w
βw Postt × IC B Weekw j + γ Yijt−1 + δYijt−2 + ψ j + τt + �Xit
+�Z jt + εi j t. (3)
where w is the week of implementation and IC B Weekw j is an indicator for schoolj adopting during week w. This version of the model allows us to track the impactestimates from week to week as the program is implemented. Finally, since theavailability of breakfast is unlikely to affect all students equally, and in particularis likely to have a bigger impact on low socioeconomic status students, we provideanalyses that split the sample by economic status, ethnicity, gender, LEP status, andprior achievement, which will allow us to test whether the impact of the ICB programvaries for different types of students. Further, we are able to test for differences inimpacts by students’ BMI for a subset of schools in 2008–2009 and 2009–2010.16
15 Since third and fourth graders took the exam on the week of April 27, we cannot estimate this modelfor them.16 Although it would be informative to examine the impact of the in-class breakfast program on nutrition,we are not able to do this with the data we have. This is because we have BMI data for 2009 to 2010, but
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 679
Since grades and attendance accrue continually, we use modified versions ofequations (1) and (2) for these outcomes. Since there are four grading periodsand six attendance periods during the school year, in these cases we include gradelevel by period fixed effects instead of year fixed effects as we have both within-year and across-grade variation. This accounts for differences across grades in eachtime period as well as differences across time periods due to, for example, studentsbecoming restless as the December holidays approach or becoming more likely toskip school as the school year ends. We consider a school to be treated if it adoptsICB at any point during the grading/attendance period. Nonetheless, this may be apoor measure of exposure as a student who is exposed to ICB for the full periodmay be affected more than one who is exposed for only part of the period. Thus,our focus is on the duration model in equation (2). We also estimate the followingmodel:
Yig jt = α + β1 FullyTreatedjt + β2 PartiallyTreatedjt + γ1Yig jt−1 + γ2Yig jt−2 + ψ j + τgt
+�Xit + �Z jt + εig jt, (4)
where FullyTreatedjt is an indicator set equal to 1 if school j is treated for all weeksof period t, while PartiallyTreatedjt equals 1 if the school was treated for some, butnot all, weeks of period t. Both of these values are set to 0 in any period prior toimplementation.
We note however that a key limitation of our study is that we do not have accessto data on the actual uptake of the breakfasts. Thus, we are limited to a reduced-form analysis based on the intention to treat rather than the treatment effects.Nonetheless, as mentioned above, the district’s nonrandom pilot study found thatuptake in pilot schools was double that of nonpilot schools and the case studiesfound that take-up increased 250 percent in more disadvantaged schools. This willallow us to generate back-of-the envelope calculations as to the effect of ICB onstudents who are induced to eat breakfast in school (e.g., effect of treatment on thetreated).
DATA DESCRIPTION
Our data come from student records in a LUSD-SW. The district is one of the largestin the country with over 200,000 students. Given that the program implementationonly overlapped with the testing for elementary students, we focus on students ingrades 1 to 5. Testing data cover the 2002–2003 through 2009–2010 academic years;however, we start our analysis with 2004 to 2005 to allow for the inclusion of twoachievement lags in our test-score regressions. For our other outcomes—grades andattendance—the data we have are more limited, with only 2008–2009 and 2009–2010available for grades and 2009 to 2010 for attendance rates.
Testing data come from the state accountability exams in math and reading.These exams are high stakes in that the scores determine whether the students arepermitted to advance to the next grade as well as the school’s accountability rating,and whether the school meets “Adequate Yearly Progress” under the No Child LeftBehind Act of 2001. Students can take the exam multiple times until they pass.
most of the data were collected between January and April of 2010. At best, some students would havehad only a few weeks of exposure to ICB when the data were collected. Further, the BMI data come fromheight and weight information gathered as part of a physical examination conducted in some, but notall schools. Unfortunately, more detailed nutrition information on calories consumed by students is notavailable.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
680 / The Effect of Providing Breakfast in Class
Unfortunately, we do not know whether a given exam score is from the first or alater administration. Hence, we use the student’s minimum score in a subject in agiven year as their achievement score under the assumption that, since students whofail tend to get extensive coaching for retakes, the lowest score is most likely fromthe student’s first sitting. We then use these scores and standardize them withingrade and year across the district.17
In addition to achievement, the data provide some other student outcomes. Inparticular, we assess the impact of the breakfast program on attendance rates withineach six-week attendance period in 2009 to 2010 and the student’s mean grade acrossall subjects in each nine-week grading period in 2008–2009 and 2009–2010.18 Finally,we have information on student demographics including race, gender, economicstatus, LEP, at-risk status, gifted status, and special education, along with BMI datafor a subset of schools in 2008–2009 and 2009–2010.19
Table 2 provides summary statistics of students in 2009 to 2010. We limit the sam-ple to schools that started ICB between February 2 and April 27, 2010, excludingschools that adopt during the week of fifth-grade testing (week 9) as it is unclearwhether fifth-grade students in these schools become treated before or after testing.We then separate our data into three samples for each of the outcome measureswe assess: achievement, grades, and attendance. As described above, we are limitedto fifth-grade students for achievement, while our data cover grades 1 to 5 for at-tendance and grades. Nonetheless, the student characteristics are relatively similaracross the samples. LUSD is a heavily minority district comprising 87 percent His-panic or black students, but our subsample schools are more heavily minority asonly 3 percent of students are not black or Hispanic. This is not surprising, giventhat our subsample is limited to schools with high economic disadvantage rates, asis evidenced in the next row showing that 94 percent of students are disadvantaged.Further, a large majority of students are Hispanic rather than black. The schoolsalso have high rates of LEP. The students in this sample are much more likely to beminority and to qualify for free/reduced-price lunch, but they are also less likely tobe classified as special education kids and more likely to be classified as gifted andtalented.20
In total, we have 6,353 students and 84 schools in 2009 to 2010 in the achievementsample, 37,309 students in 87 schools in the grades sample, and 38,425 students in87 schools in the attendance sample.21 Our total estimation sample covers 2004–2005 through 2009–2010 for achievement, 2008–2009 through 2009–2010 for grades,and 2009 to 2010 only for attendance. They include approximately 30,700 math and
17 While it is more common to use scale scores in the standardization, the state changed the scalingprocedure in 2009 to 2010 from a horizontal to a vertical scaling regime, making the scale scores in thatyear incomparable to prior years. Hence, we rely on raw scores for our standardizations.18 While it would be interesting to see the impact of the breakfast program on behavior, unfortunatelythe only measure of disciplinary incidents available to us—the number of in-school suspensions or moresevere punishments a student receives—is too infrequent in elementary student populations to identifyeffects.19 A student is considered at-risk if he or she is low achieving, has previously been retained, is pregnant ora parent, is LEP, has been placed in alternative education or juvenile detention, is on parole or probation,is homeless, or has previously dropped out of school.20 In 2010, the Digest of Education Statistics reports that on average 54.9 percent of students in theUnited States are white, while only 17 percent are black and 21.5 percent are Hispanic. This sourcealso reports that 44.6 percent of students qualify for free/reduced lunch, 13.2 percent are designated asrequiring special education, and 6.7 percent are classified as gifted and talented at the national level.21 The difference in the number of schools in these samples reflects a handful of schools in LUSD thatserve only grades K–3.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 681
Table 2. Summary statistics in 2009 to 2010.
(A) Fifth-grade testedstudents only (test sample)
(B) All students in grades 1 to5 (grades sample)
(C) All students in grades 1 to5 (attendance sample)
Female 0.49 Female 0.48 Female 0.48(0.50) (0.50) (0.50)
Black 0.23 Black 0.23 Black 0.23(0.42) (0.42) (0.42)
White 0.02 White 0.01 White 0.02(0.12) (0.12) (0.12)
Hispanic 0.74 Hispanic 0.74 Hispanic 0.73(0.44) (0.44) (0.44)
Economically 0.94 Economically 0.94 Economically 0.94disadvantaged (0.24) disadvantaged (0.23) disadvantaged (0.23)
LEP 0.40 LEP 0.54 LEP 0.53(0.49) (0.50) (0.50)
At risk 0.67 At risk 0.74 At risk 0.74(0.47) (0.44) (0.44)
Gifted 0.15 Gifted 0.13 Gifted 0.13(0.35) (0.34) (0.34)
Special ed 0.09 Special ed 0.07 Special ed 0.07(0.29) (0.26) (0.26)
2008 to 2009math∗
−0.01 2008 to 2009math∗
−0.05 2008 to 2009math∗
−0.06(0.94) (0.97) (0.98)
2008 to 2009reading∗
−0.06 2008 to 2009reading∗
−0.08 2008 to 2009reading∗
−0.08(0.95) (0.97) (0.97)
2009 to 2010math
−0.04 Mean grade 83.1 Mean absencerate (%)
3.16(1.01) (7.2) (5.22)
2009 to 2010reading
−0.10(1.01)
Observations(student)
6,353 Observations(student-timeperiod; 4periods)
145,203 Observations(student-timeperiod; 6periods)
230,550
No. of students 6,353 No. of students 37,309 No. of students 38,425No. of schools 84 No. of schools 87 No. of schools 87
∗Prior year reading and math have 5,908 observations in panel A; 50,306 and 50,377 observations,respectively, for panel B; and 77,316 and 77,424 observations, respectively, for panel C. Scores onlyavailable for grades 4 to 5.Note: Standard deviations in parentheses.
25,400 reading student-year observations for achievement regressions,22 188,700student-grading period observations for grades regressions, and 149,000 student-attendance period observations for attendance regressions.
EFFECTS OF ICB ON ACHIEVEMENT, GRADES, AND ABSENTEEISM
Effects on Student Achievement
To see how the achievement in post-testing adopters compared to pretestingadopters evolves over time, in Figure 1 we present estimates from the following
22 For the math sample, 3,930 observations are in treated schools after implementation, while 18,651 arein treated schools prior to implementation. For reading those counts are 3,937 and 14,657, respectively.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
682 / The Effect of Providing Breakfast in Class
Note: Figures show estimates from equation (5) in the text. The solid line shows point estimates with thedotted lines showing 95-percent confidence intevervals.
Figure 1. Estimated Pre- and Posttreatment Impacts.
regression model:
Yijt = α +2009−2010∑
t=2005−2006
βtYeart × IC Bj + γ1Yijt−1 + γ2Yijt−2 + ψ j + τt + �Xi j t
+�Z jt + εi j t. (5)
Each estimate of is plotted along with its 95 percent confidence interval. The year2004 to 2005 is the left-out category. If the parallel trends assumption underlingour difference-in-differences strategy is correct, we should see no significant differ-ence in achievement up through 2008 to 2009. For math this is clearly the case. Forreading, some of the estimates are significantly greater than zero, indicating an in-crease in achievement relative to 2004 to 2005. However, from 2005–2006 through
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 683
2008–2009 the estimates do not significantly differ from each other and remainclose. Thus, we see little to indicate a persistent trend after 2004 to 2005, thoughthere is a slight uptick in achievement in 2008 to 2009. Later we will also show thatour results are robust to limiting the sample to later years indicating that neither theincrease from 2004–2005 to 2005–2006 in reading nor the slight increases in 2008to 2009 are substantial concerns. Given those results, we use all years in our anal-ysis to improve precision. Looking at the change from 2008–2009 to 2009–2010 inFigure 1, we see an increase in achievement consistent with the main regressionresults we provide below.
Table 3 shows the results of regressions using equations (1) and (2) for testscores.23 Panels A.I and B.I provide results from the basic difference-in-differencesregressions for math and reading, respectively. Column (1) shows that, on average,the impact of ICB is to increase test scores by 0.09 SD in math and 0.06 in read-ing (both significant at the 10-percent level). In panels A.II and B.II, we provideestimates that allow the impacts to vary by week of adoption. This specification isuseful in determining whether the impacts are likely due to actual learning gainsby students or if the breakfasts are simply increasing students’ test-taking perfor-mance. If the former is true, then we would expect to see larger achievement impactsfor students in early adopting schools than for late adopters. Nonetheless, the es-timates in panels A.II and B.II suggest little difference by exposure to treatment.The point estimates on the weeks of exposure interactions with being in the post-ICB period are insignificant and close to zero. In Figure 2, we provide graphs thatshow point estimates and 95 percent confidence intervals using equation (3) as theregression model. This figure shows whether any differences by exposure time canbe discerned using a less restrictive model. There is little indication of variation byweeks of exposure. Although somewhat noisy, the week-by-week estimates appearto be centered a bit below 0.1 SD in both subjects throughout the implementationperiod, and show little evidence of trending. Thus, the estimates shown here alongwith those in panels A.II and B.II suggest that the impacts are due to improvementsin exam performance, but not necessarily from learning itself.24 Later we provideevidence on course grades that corroborates this. Nonetheless, we caution that theimplementation period is only two months long. Further, a substantial portion ofinstruction during this period is focused on test preparation specifically. Hence, itis possible that there are learning effects, but they are only detectable over longertime periods.
In columns (2) through (8) of Table 3, we provide estimates that split the samplesby the once lagged achievement levels for each student, first by whether the student isabove or below the median achievement score and then by the student’s achievementquintile. The results indicate that the achievement effects for math found in column(1) are primarily coming from students who were low achievers prior to programimplementation. For those students who score below the median in the previousyear, the effects size is an increase of 0.12 SD. On the other hand, students whohave above median prior achievement have a smaller and insignificant effect sizeof 0.06 SD. Nonetheless, the below and above median estimates do not statisticallysignificantly differ from each other, so we take these results as suggestive ratherthan conclusive. For reading, the results are similar for low and high achievers.
23 Standard errors are clustered by school. In Appendix Table A1, we repeat the analysis in Table 3excluding school fixed effects and find similar results. All appendices are available at the end of thisarticle as it appears in JPAM online. Go to the publisher’s Web site and use the search engine to locatethe article at http://www3.interscience.wiley.com/cgi-bin/jhome/34787.24 Figlio and Winicki (2005) show that schools recognize the potential for extra consumption to improveachievement and increase calorie counts of in-school meals during testing weeks.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
684 / The Effect of Providing Breakfast in Class
Tab
le3
.Eff
ect
ofin
-cla
ssb
reak
fast
onfi
fth
-gra
de
ach
ieve
men
t—in
clu
din
gsc
hoo
lfix
edef
fect
san
dco
ntr
ols.
By
abov
e/b
elow
med
ian
lagg
edac
hie
vem
ent
By
lagg
edac
hie
vem
ent
quin
tile
s
Fu
llsa
mp
leB
elow
Ab
ove
Bot
tom
Sec
ond
Th
ird
Fou
rth
Top
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(A)
Mat
hI.
Red
uce
d-f
orm
trea
tmen
tef
fect
Pos
t×
Tre
ated
0.08
6*0.
115*
0.06
10.
086
0.17
3***
0.02
30.
119*
0.04
6(0
.046
)(0
.058
)(0
.045
)(0
.093
)(0
.065
)(0
.059
)(0
.060
)(0
.039
)O
bse
rvat
ion
s30
,771
15,5
5715
,214
5,05
57,
011
6,97
76,
455
5,27
3II
.Tre
atm
ent
effe
ctan
dex
pos
ure
tim
eP
ost×
Tre
ated
0.09
80.
145
0.05
60.
186
0.11
20.
094
0.07
40.
062
(0.0
71)
(0.0
98)
(0.0
59)
(0.1
39)
(0.1
23)
(0.0
83)
(0.0
71)
(0.0
58)
Pos
t×
Exp
osu
reT
ime
(wee
ks)
−0.0
02−0
.006
0.00
1−0
.019
0.01
2−0
.014
0.00
9−0
.003
(0.0
10)
(0.0
14)
(0.0
09)
(0.0
19)
(0.0
18)
(0.0
14)
(0.0
09)
(0.0
09)
Ob
serv
atio
ns
30,7
7115
,557
15,2
145,
055
7,01
16,
977
6,45
55,
273
(B)
Rea
din
gI.
Red
uce
d-f
orm
trea
tmen
tef
fect
Pos
t×
Tre
ated
0.06
2*0.
055
0.06
1**0.
055
0.06
60.
072*
0.03
60.
043
(0.0
34)
(0.0
44)
(0.0
30)
(0.0
79)
(0.0
58)
(0.0
43)
(0.0
42)
(0.0
36)
Ob
serv
atio
ns
25,4
4514
,065
11,3
805,
227
5,98
25,
617
4,88
63,
733
II.T
reat
men
tef
fect
and
exp
osu
reti
me
Pos
t×
Tre
ated
0.04
90.
037
0.05
10.
151
−0.0
000.
150**
0.00
4−0
.041
(0.0
53)
(0.0
74)
(0.0
47)
(0.1
23)
(0.0
90)
(0.0
66)
(0.0
69)
(0.0
61)
Pos
t×
Exp
osu
reT
ime
(wee
ks)
0.00
30.
004
0.00
2−0
.019
0.01
3−0
.015
0.00
70.
017
(0.0
08)
(0.0
11)
(0.0
08)
(0.0
21)
(0.0
12)
(0.0
11)
(0.0
11)
(0.0
12)
Ob
serv
atio
ns
25,4
4514
,065
11,3
805,
227
5,98
25,
617
4,88
63,
733
Not
e:D
ata
cove
rth
e20
04–2
005
thro
ugh
2009
–201
0ac
adem
icye
ars.
Ach
ieve
men
tsc
ores
are
stan
dar
diz
edw
ith
ingr
ade
and
year
.Du
eto
ach
ange
inth
esc
alin
gp
roce
du
rein
2009
to20
10,
we
stan
dar
diz
eu
sin
gra
wsc
ores
.T
reat
edis
anin
dic
ator
for
wh
eth
era
sch
ool
star
tsIC
Bp
rior
toth
ete
stin
gw
eek.
Stu
den
t-le
vel
cova
riat
esin
clu
de
lagg
edac
hie
vem
ent,
twic
ela
gged
ach
ieve
men
t,st
ud
ent’s
race
/eth
nic
ity,
gen
der
,an
dec
onom
icst
atu
sal
ong
wit
hye
aran
dgr
ade-
leve
ldu
mm
ies.
Sch
ool-
leve
lco
vari
ates
incl
ud
ep
erce
nta
geof
stu
den
tsw
ho
are
wh
ite,
bla
ck,H
isp
anic
,Nat
ive
Am
eric
an,A
sian
,eco
nom
ical
lyd
isad
van
tage
d,L
EP
,in
voca
tion
aled
uca
tion
,in
spec
iale
du
cati
on,g
ifte
d,i
nb
ilin
gual
edu
cati
on,i
nea
chgr
ade
leve
l,re
ferr
edto
anal
tern
ativ
ed
isci
pli
nar
yp
rogr
am,a
nd
sch
oolf
ixed
effe
cts.
*,**
,an
d**
*d
enot
esi
gnif
ican
ceat
the
10-p
erce
nt,
5-p
erce
nt,
and
1-p
erce
nt
leve
ls,r
esp
ecti
vely
.Sta
nd
ard
erro
rscl
ust
ered
by
sch
ooli
np
aren
thes
es.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 685
Note: Graphs show point estimates and confidence intervals from regressions of program impact onachievement where the impact estimates are allowed to vary by week of implementation.
Figure 2. Impact of In-Class Breakfast on Achievement by Week of Implementation.
Similarly, columns (4) through (8) provide estimates separated by prior achievementquintiles in which the point estimates for lower quintiles in math are generally higherthan those for the upper quintiles. Finally, we also provide estimates that interacttreatment status with exposure time for these models in panels A.II and B.II. Aswith the pooled estimates, there is little to indicate differences by week of adoption.Further, in Figure 3 we repeat the analysis shown in Figure 2, but split the samplesby whether the students are above or below the median achievement level. Thisfigure indicates that the impacts differ little by time of exposure, regardless of thestudents’ achievement levels.
One potential concern is that if the slight increase in reading in 2008 seen inFigure 1 is not natural variation, we might be overestimating the impact. Hence, wealso estimate the impacts limiting the sample to 2008 and 2009. Since we only have
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
686 / The Effect of Providing Breakfast in Class
Note: Graphs show point estimates and confidence intervals from regressions of program impact onachievement where the impact estimates are allowed to vary by week of implementation. Samples aresplit for each exam by whether the student is above or below the median score on the 2008–09 achievementexam.
Figure 3. Impact of In-Class Breakfast on Achievement by Prior Achievement andWeek of Implementation.
two years of data for this analysis, we leave out school fixed effects. The estimate formath falls to 0.041 (standard error of 0.059). While this estimate is not statisticallysignificant at conventional levels, it nonetheless remains positive. For reading, theestimate changes only slightly to 0.078 (0.046).
In Table 4, we provide results that examine whether there are heterogeneous ef-fects of ICB on different groups of students. Columns (1) and (2) show no differencesbetween boys and girls in the effects of the ICB on math and reading test scores.However, when we further split the sample by whether the students are high or lowachievers in columns (3) through (6) we find that the impacts on girls are heavilyconcentrated among low achievers. For boys, the results differ for math and read-ing exams. By contrast, the effects sizes for racial/ethnic groups clearly differ acrossgroups. Columns (7) to (9) show that the ICB increased test scores for Hispanics by0.12 and 0.09 of a SD in math and reading, but essentially had no impact on blacks.25
For white students, there are too few observations for reasonable precision in theestimates.
This finding for blacks and Hispanics is interesting as it indicates that Hispanicswere probably more likely to adjust their consumption patterns in response to thebreakfast program than black students. Unfortunately, we can only speculate as tothe reasons for this racial differential. One possibility is that black students in LUSDare less affected by stigma effects and were already eating in the cafeteria prior toprogram implementation. Another possibility is that LUSD black students are morelikely to eat breakfast at home than Hispanic students. A third possibility is that
25 This difference is statistically significant at the 10-percent level for math and at the 5-percent level forreading.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 687
Tab
le4
.Eff
ect
ofin
-cla
ssb
reak
fast
onfi
fth
-gra
de
ach
ieve
men
t—b
yst
ud
ent
char
acte
rist
ics.
Gen
der
and
pri
orac
hie
vem
ent
Gen
der
Eth
nic
ity
Mal
eF
emal
e
Mal
eF
emal
e
Bel
owm
edia
nla
gged
ach
ieve
men
t
Ab
ove
med
ian
lagg
edac
hie
vem
ent
Bel
owm
edia
nla
gged
ach
ieve
men
t
Ab
ove
med
ian
lagg
edac
hie
vem
ent
Bla
ckH
isp
anic
Wh
ite
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(A)
Mat
hP
ost×
Tre
ated
0.08
8*0.
087*
0.09
20.
082
0.13
6**0.
045
−0.0
290.
124**
*0.
150
(0.0
51)
(0.0
47)
(0.0
64)
(0.0
51)
(0.0
66)
(0.0
50)
(0.0
66)
(0.0
47)
(0.3
27)
Ob
serv
atio
ns
15,1
8815
,583
7,48
57,
703
8,07
27,
511
6,60
223
,259
420
(B)
Rea
din
gP
ost×
Tre
ated
0.06
90.
057*
0.04
00.
080*
0.08
5*0.
033
−0.0
020.
092**
0.12
0(0
.046
)(0
.030
)(0
.059
)(0
.041
)(0
.051
)(0
.036
)(0
.066
)(0
.037
)(0
.232
)O
bse
rvat
ion
s12
,794
12,6
517,
517
5,27
76,
548
6,10
35,
894
18,7
6736
6
Fre
elu
nch
LE
Pst
atu
sB
ody
mas
sin
dex
∗
Low
wei
ght
Med
ium
wei
ght
Ove
rwei
ght
Ob
ese
Not
elig
ible
Eli
gib
leN
otL
EP
LE
P<
25p
erce
nti
le25
to84
per
cen
tile
85to
94p
erce
nti
le�
95p
erce
nti
le(1
0)(1
1)(1
2)(1
3)(1
4)(1
5)(1
6)(1
7)
(A)
Mat
hP
ost×
Tre
ated
0.00
80.
132**
*0.
052
0.11
8*0.
244**
0.01
2−0
.048
0.05
1(0
.056
)(0
.050
)(0
.051
)(0
.060
)(0
.108
)(0
.074
)(0
.110
)(0
.084
)O
bse
rvat
ion
s10
,563
20,2
0818
,926
11,8
4566
42,
846
1,48
32,
267
(B)
Rea
din
gP
ost×
Tre
ated
0.08
3**0.
046
0.05
6*0.
084
0.23
2*0.
050
0.11
4*0.
062
(0.0
38)
(0.0
43)
(0.0
32)
(0.0
57)
(0.1
30)
(0.0
92)
(0.0
68)
(0.0
65)
Ob
serv
atio
ns
9,29
016
,155
15,9
359,
510
683
2,90
51,
511
2,33
1
∗ BM
Id
ata
only
incl
ud
esa
sub
set
ofsc
hoo
lsin
2008
–200
9an
d20
09–2
010.
Not
e:D
ata
cove
rth
e20
03–2
004
thro
ugh
2009
–201
0ac
adem
icye
ars.
Ach
ieve
men
tsc
ores
are
stan
dar
diz
edw
ith
ingr
ade
and
year
.D
ue
toa
chan
gein
the
scal
ing
pro
ced
ure
in20
09to
2010
,w
est
and
ard
ize
usi
ng
raw
scor
es.
Tre
ated
isan
ind
icat
orfo
rw
het
her
asc
hoo
lst
arts
ICB
pri
orto
the
test
ing
wee
k.S
tud
ent-
leve
lco
vari
ates
incl
ud
ela
gged
ach
ieve
men
t,tw
ice
lagg
edac
hie
vem
ent,
stu
den
t’sra
ce/e
thn
icit
y,ge
nd
er,
and
econ
omic
stat
us
alon
gw
ith
year
and
grad
e-le
vel
du
mm
ies.
Sch
ool-
leve
lco
vari
ates
incl
ud
ep
erce
nta
geof
stu
den
tsw
ho
are
wh
ite,
bla
ck,
His
pan
ic,
Nat
ive
Am
eric
an,
Asi
an,
econ
omic
ally
dis
adva
nta
ged
,L
EP
,in
voca
tion
aled
uca
tion
,in
spec
ial
edu
cati
on,
gift
ed,
inb
ilin
gual
edu
cati
on,
inea
chgr
ade
leve
l,re
ferr
edto
anal
tern
ativ
ed
isci
pli
nar
yp
rogr
am,
and
sch
ool
fixe
def
fect
s.*,
**,
and
***
den
ote
sign
ific
ance
atth
e10
-per
cen
t,5-
per
cen
t,an
d1-
per
cen
tle
vels
,re
spec
tive
ly.
Sta
nd
ard
erro
rscl
ust
ered
by
sch
ooli
np
aren
thes
es.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
688 / The Effect of Providing Breakfast in Class
being Hispanic is correlated with other risk factors for malnutrition at a higher ratethan being black.
To explore this further, in columns (10) and (11) we examine differences in eco-nomic status by free lunch and nonfree lunch eligibility.26 This effectively separatesthe sample by those students from families with incomes below 130 percent of thefederal poverty line (eligible) and those above that income level (not eligible). Whileoverall economic disadvantage rates for blacks and Hispanics in our sample aresimilar, 91 and 95 percent, respectively, eligibility rates for free lunch are 61 and73 percent for blacks and Hispanics, respectively. Although they do not statisticallysignificantly differ, the results suggest the ICB program has a bigger effect on mathscores for those who are eligible. Nonetheless, in results not shown here, when wesplit the sample by both free lunch status and race, we still find the same racialgap between Hispanics and blacks regardless of free lunch status, so this does notappear to be an explanation for the gap.
Turning to other demographic characteristics, columns (12) and (13) show that,not surprisingly given the results for Hispanics, students with LEP also benefit morethan non-LEP students.27 Finally, in columns (14) through (17) we look at whetherimpacts differ by BMI. The BMI levels for each student come from height andweight taken during physical fitness tests at the end of the 2008 to 2009 school year.Unfortunately, the BMI data are only available for a subset of fifth-grade schools.28
Further, since we only have one pre- and one postadoption year for this analysis, wedo not include school fixed effects.
Since the relationship between BMI and obesity differ by age for children, weclassify the students into four categories based on the Centers for Disease Control’sBMI-for-age values and the student’s age in months. The four categories are low BMI(children are below the 25th percentile of weight for the CDC base year), mediumweight (25th to 84th percentile), overweight (85th to 94th percentile), and obese(�95th percentile). Note that the first two categories are not the same as thoseused by the CDC, which are underweight (<10th percentile) and healthy weight(10th to 84th percentile). We make this change to ensure we have sufficient powerto detect effects since we have very few observations that would be classified asunderweight.
The results indicate that ICB has a larger positive impact on children with lowBMI. In particular, we find that math scores are significantly higher for these stu-dents with a point estimate of 0.24 SD. Further, the low BMI estimate significantlydiffers from a pooled estimate for all other BMI categories. For all other weightcategories, the estimates are much smaller and statistically insignificant. For read-ing, the results for low BMI are similar, but the point estimates for other categoriesare larger than those for math. One might think that this could also be a poten-tial explanation for the racial gap if Hispanics are more likely to have low BMI.
26 Unfortunately, since we have so few students in the sample who are not economically disadvantagedwe cannot analyze differences along this dimension.27 We also investigate differences within subgroups by high and low achievers. Unlike the gender results,there are only a few differences by achievement for the other estimates provided in Table 4. For whites,while the sample size is very small, we see a significant effect on low achievers at the 10-percent level.Further, reading impacts are bigger for high-income low achievers and low-income high achievers. Theseresults are provided in Appendix Table A2. All appendices are available at the end of this article as itappears in JPAM online. Go to the publisher’s Web site and use the search engine to locate the article athttp://www3.interscience.wiley.com/cgi-bin/jhome/34787.28 There is a small relationship between the likelihood of a school having BMI data available and beingan early adopter. In particular, schools that adopt prior to week 10 are 8 percentage points more likelyto have BMI data available than those that adopt in weeks 10 or 11. This relationship is significant at the10-percent level.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 689
However, the opposite is, in fact, true. Low BMI rates in our sample are 8 percentfor Hispanics and 11 percent for blacks. We also note that low BMI does not nec-essarily correlate with low nutrition in our sample. Indeed, in many cases, childrenwho are malnourished tend to be obese as their malnourishment comes from low-quality food rather than low quantity. Thus, we urge caution in interpreting theselow BMI estimates as a proxy for malnutrition. Nonetheless, the larger impactsfor free lunch-eligible students in math do provide some evidence that students athigher risk of malnutrition are affected more by the program.
In Tables 5 and 6, we provide two tests of the validity of our difference-in-differences identification strategy. First, in Table 5 we examine the possibility thatschools that adopted prior to the ninth week had preexisting trends. To test for thesetrends we conduct a placebo test where we estimate equations (1) and (2), includingthe same controls and fixed effects, on the sample prior to 2009 to 2010 and label2008 to 2009 as the postperiod.29 If there are preexisting trends, then we shouldexpect to see a significant impact on achievement for schools that adopt prior totesting in the year 2008 to 2009 relative to the 2004–2005 through 2007–2008 period.The estimates in Table 5 show little to suggest the existence of pretrends. In almostall cases—full sample, split by above/below median, and split by quintile—the pointestimates on the Post × Treated and Post × Exposure Time variables are small andstatistically insignificant. For the overall impact on treated schools before the intro-duction of the ICB program, the estimate for math is 0.001 with an SE of 0.052, andthe estimate for reading is 0.031 with an SE of 0.042.30
Another concern is that if the timing of program implementation is relatedto changes in the characteristics of students in the adopting schools, or if theprogram itself induced changes in the composition of the students who tested, ourresults could be biased. Hence, in Table 6 we provide estimates of the difference-in-differences impacts on observable characteristics. Note that these models donot include any controls except grade and year dummies.31 Panel A of Table 6shows that earlier adoption of the program had no statistically significant effectson students’ gender, race, economic disadvantage, LEP status, at-risk status, giftedstatus, special education status, and most importantly mean lagged reading or mathscores for students in fifth grade.32 Only gifted status is significant, and only at the10-percent level. In panel B, we show that if we expand the sample to include allstudents in grades 1 to 5 there are no statistically significant effects.33 We also notethat aggregated school-level analyses show very similar results and are availableupon request.34
It is instructive to note here that the main effects for being a school treated priorto week 10 do show some small but significant differences in student characteristics.In particular, schools that adopt in weeks 1 through 8 have 3 percentage points moreeconomically disadvantaged students, achievement scores approximately one-tenth
29 Estimates excluding controls and fixed effects are similar and provided in AppendixTable A3. All appendices are available at the end of this article as it appears in JPAM online. Go to the pub-lisher’s Web site and use the search engine to locate the article at http://www3.interscience.wiley.com/cgi-bin/jhome/34787.30 We nonetheless note that the reading estimate is similar to that found for math in our main resultswhen the sample is limited to 2008 to 2009 and later.31 Estimates with school fixed effects are similar.32 Although student gender is fairly balanced across schools, the gender of those who test and are in thesample could change in response to access to breakfast, if, for example, some households give priority inaccess to food to boys over girls.33 For lagged achievement we are limited to grades 4 and 5 since testing begins in grade 3.34 We also find that there are no significant impacts on the likelihood of being in a given lagged achieve-ment quintile.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
690 / The Effect of Providing Breakfast in Class
Tab
le5
.Ach
ieve
men
tp
lace
bo
test
—sa
mp
leli
mit
edto
2008
to20
09an
dea
rlie
ran
dse
t20
08to
2009
asP
ost
per
iod
wit
hco
ntr
ols
and
sch
oolf
ixed
effe
cts.
By
abov
e/b
elow
med
ian
lagg
edac
hie
vem
ent
By
lagg
edac
hie
vem
ent
quin
tile
s
Fu
llsa
mp
leB
elow
Ab
ove
Bot
tom
Sec
ond
Th
ird
Fou
rth
Top
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(A)
Mat
hI.
Red
uce
d-f
orm
trea
tmen
tef
fect
Pos
t×
Tre
ated
0.00
10.
053
−0.0
020.
123
−0.0
130.
049
−0.0
38−0
.059
(0.0
52)
(0.0
50)
(0.0
32)
(0.1
29)
(0.0
82)
(0.0
66)
(0.0
49)
(0.0
36)
Ob
serv
atio
ns
25,3
6512
,732
12,6
334,
051
5,79
35,
794
5,35
54,
372
II.T
reat
men
tef
fect
and
exp
osu
reti
me
Pos
t×
Tre
ated
−0.0
42−0
.040
0.01
4−0
.008
−0.1
57−0
.023
−0.0
19−0
.006
(0.0
79)
(0.0
89)
(0.0
48)
(0.1
94)
(0.1
36)
(0.0
97)
(0.0
86)
(0.0
52)
Pos
t×
Exp
osu
reT
ime
(wee
ks)
0.00
80.
017
−0.0
030.
023
0.02
60.
013
−0.0
04−0
.010
(0.0
12)
(0.0
12)
(0.0
07)
(0.0
31)
(0.0
20)
(0.0
16)
(0.0
12)
(0.0
08)
Ob
serv
atio
ns
25,3
6512
,732
12,6
334,
051
5,79
35,
794
5,35
54,
372
(B)
Rea
din
gI.
Red
uce
d-f
orm
trea
tmen
tef
fect
Pos
t×
Tre
ated
0.03
10.
009
−0.0
14−0
.020
0.13
4**0.
042
−0.0
180.
007
(0.0
42)
(0.0
42)
(0.0
33)
(0.1
02)
(0.0
62)
(0.0
52)
(0.0
37)
(0.0
46)
Ob
serv
atio
ns
20,0
3211
,103
8,92
94,
116
4,74
64,
399
3,85
42,
917
II.T
reat
men
tef
fect
and
exp
osu
reti
me
Pos
t×
Tre
ated
0.04
30.
052
0.04
00.
053
0.04
20.
013
0.07
10.
091
(0.0
68)
(0.0
73)
(0.0
55)
(0.1
54)
(0.1
12)
(0.0
80)
(0.0
69)
(0.0
67)
Pos
t×
Exp
osu
reT
ime
(wee
ks)
−0.0
02−0
.008
−0.0
10−0
.013
0.01
70.
005
−0.0
17−0
.016
(0.0
12)
(0.0
10)
(0.0
08)
(0.0
21)
(0.0
20)
(0.0
13)
(0.0
11)
(0.0
13)
Ob
serv
atio
ns
20,0
3211
,103
8,92
94,
116
4,74
64,
399
3,85
42,
917
Not
e:D
ata
cove
rth
e20
04–2
005
thro
ugh
2007
–200
8ac
adem
icye
ars.
Ach
ieve
men
tsc
ores
are
stan
dar
diz
edw
ith
ingr
ade
and
year
.Du
eto
ach
ange
inth
esc
alin
gp
roce
du
rein
2009
to20
10,w
est
and
ard
ize
usi
ng
raw
scor
es.T
he
Pos
tin
dic
ator
isse
tequ
alto
1in
2007
to20
08.T
reat
edis
anin
dic
ator
for
wh
eth
era
sch
ools
tart
sIC
Bp
rior
toth
ete
stin
gw
eek.
Sch
ools
trea
ted
inw
eek
9ar
ed
rop
ped
asth
isis
the
fift
h-g
rad
ete
stin
gw
eek
and
som
esc
hoo
lsm
ayh
ave
pos
tpon
edth
est
art
ofIC
Bfo
rfi
fth
-gra
de
stu
den
ts.
Stu
den
t-le
vel
cova
riat
esin
clu
de
lagg
edac
hie
vem
ent,
twic
ela
gged
ach
ieve
men
t,st
ud
ent’s
race
/eth
nic
ity,
gen
der
,an
dec
onom
icst
atu
sal
ong
wit
hye
aran
dgr
ade-
leve
ld
um
mie
s.S
choo
l-le
vel
cova
riat
esin
clu
de
per
cen
tage
ofst
ud
ents
wh
oar
ew
hit
e,b
lack
,His
pan
ic,N
ativ
eA
mer
ican
,Asi
an,e
con
omic
ally
dis
adva
nta
ged
,LE
P,i
nvo
cati
onal
edu
cati
on,i
nsp
ecia
led
uca
tion
,gif
ted
,in
bil
ingu
aled
uca
tion
,in
each
grad
ele
vel,
refe
rred
toan
alte
rnat
ive
dis
cip
lin
ary
pro
gram
,an
dsc
hoo
lfi
xed
effe
cts.
*,**
,an
d**
*d
enot
esi
gnif
ican
ceat
the
10-p
erce
nt,
5-p
erce
nt,
and
1-p
erce
nt
leve
ls,
resp
ecti
vely
.S
tan
dar
der
rors
clu
ster
edb
ysc
hoo
lin
par
enth
eses
.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 691
Tab
le6
.Tes
tsof
imp
acts
onex
ogen
ous
cova
riat
es.
Fem
ale
Bla
ckW
hit
eH
isp
anic
Eco
nom
icd
isad
van
-ta
geF
ree
lun
ch
Red
uce
d-
pri
celu
nch
LE
PA
tri
skG
ifte
dS
pec
iale
dL
agge
dm
ath
Lag
ged
read
ing
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(6)
(7)
(8)
(9)
(10)
(11)
(A)
Mea
nfo
rfi
fth
-gra
de
only
Pos
t×
Tre
ated
−0.0
03−0
.017
0.00
20.
013
0.01
1−0
.017
0.00
20.
013
0.01
1−0
.037
*−0
.001
−0.0
21−0
.002
(0.0
16)
(0.0
15)
(0.0
04)
(0.0
16)
(0.0
14)
(0.0
15)
(0.0
04)
(0.0
16)
(0.0
14)
(0.0
19)
(0.0
09)
(0.0
48)
(0.0
45)
Tre
ated
−0.0
010.
060
−0.0
06−0
.017
0.03
3***
0.06
0−0
.006
−0.0
170.
033**
*−0
.039
**0.
023**
−0.1
13**
−0.1
15**
*
(0.0
07)
(0.0
69)
(0.0
05)
(0.0
68)
(0.0
09)
(0.0
69)
(0.0
05)
(0.0
68)
(0.0
09)
(0.0
16)
(0.0
10)
(0.0
48)
(0.0
40)
Ob
serv
atio
ns
38,9
1338
,913
38,9
1338
,913
38,9
1338
,913
38,9
1338
,913
38,9
1338
,913
38,9
1335
,062
35,0
98
(B)
Mea
nfo
ral
lgra
des
insc
hoo
lP
ost×
Tre
ated
0.00
5−0
.009
0.00
10.
005
0.00
4−0
.009
0.00
10.
005
0.00
4−0
.015
0.00
20.
017
−0.0
04(0
.009
)(0
.011
)(0
.002
)(0
.011
)(0
.007
)(0
.011
)(0
.002
)(0
.011
)(0
.007
)(0
.013
)(0
.005
)(0
.047
)(0
.039
)T
reat
ed−0
.003
0.06
6−0
.005
−0.0
210.
033**
*0.
066
−0.0
05−0
.021
0.03
3***
−0.0
37**
*0.
012**
−0.1
18**
−0.1
07**
(0.0
04)
(0.0
67)
(0.0
04)
(0.0
66)
(0.0
09)
(0.0
67)
(0.0
04)
(0.0
66)
(0.0
09)
(0.0
12)
(0.0
06)
(0.0
47)
(0.0
45)
Ob
serv
atio
ns
201,
206
201,
206
201,
206
201,
206
201,
206
201,
206
201,
206
201,
206
201,
206
201,
206
201,
206
75,8
5975
,899
Not
e:D
ata
cove
rth
e20
04–2
005
thro
ugh
2009
–201
0ac
adem
icye
ars.
For
pan
elB
,lag
ged
ach
ieve
men
treg
ress
ion
sar
eon
lyp
ossi
ble
for
grad
es4
and
5.A
chie
vem
ent
scor
esar
est
and
ard
ized
wit
hin
grad
ean
dye
ar.R
egre
ssio
ns
incl
ud
egr
ade
and
year
du
mm
ies
asco
ntr
ols.
Tre
ated
isan
ind
icat
orfo
rw
het
her
asc
hoo
lst
arts
ICB
pri
orto
the
test
ing
wee
k.S
choo
lstr
eate
din
wee
k9
are
dro
pp
edas
this
isth
efi
fth
-gra
de
test
ing
wee
kan
dso
me
sch
ools
may
hav
ep
ostp
oned
the
star
tof
ICB
for
fift
h-g
rad
est
ud
ents
.*,*
*,an
d**
*d
enot
esi
gnif
ican
ceat
the
10-p
erce
nt,
5-p
erce
nt,
and
1-p
erce
nt
leve
ls,r
esp
ecti
vely
.Sta
nd
ard
erro
rscl
ust
ered
by
sch
ool
inp
aren
thes
es.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
692 / The Effect of Providing Breakfast in Class
Table 7. Specification checks.
Math Reading
(1) No lagged achievementPost × Treated 0.078 0.077*
(0.060) (0.044)Observations 44,730 45,628
(2) Limit to 2007 to 2008 and laterPost × Treated 0.071 0.091**
(0.052) (0.039)Observations 15,433 15,822
(3) No school fixed effectsPost × Treated 0.073 0.081**
(0.047) (0.035)Observations 30,771 25,445
(4) Exposure time effect on fourth gradePost × Exposure Time (weeks) −0.004 0.008
(0.007) (0.006)Observations 36,634 35,327
Note: Data cover the 2004–2005 through 2009–2010 academic years. Achievement scores are standardizedwithin grade and year. Due to a change in the scaling procedure in 2009 to 2010, we standardize usingraw scores. Treated is an indicator for whether a school starts ICB prior to the testing week. Student-levelcovariates include student’s race/ethnicity, gender, and economic status along with year and grade-leveldummies. School-level covariates include lagged, achievement, twice-lagged achievement, percentage ofstudents who are white, black, Hispanic, Native American, Asian, economically disadvantaged, LEP, invocational education, in special education, gifted, in bilingual education, in each grade level, referredto an alternative disciplinary program, and school fixed effects. Fourth-grade results only include oneachievement lag. *, **, and *** denote significance at the 10-percent, 5-percent, and 1-percent levels,respectively. Standard errors clustered by school in parentheses.
of a SD lower, lower gifted rates, and higher at-risk and special education rates.This is the primary reason why we argue that the adoption timing is quasi-randomrather than entirely random, and we rely on a difference-in-differences strategyrather than a simple cross-sectional comparison. Nonetheless, the important take-away from this table is that there is little evidence that the changes in achieve-ment found in Table 3 are correlated with contemporaneous changes in studentcharacteristics.
In Table 7, we provide a set of specification checks for our baseline treatmenteffect estimates. In row (1), we estimate models with lagged achievement omitted.In row (2), we limit the sample only to 2007 to 2008 and later years. In row (3),we provide estimates without school fixed effects. In all of these cases, the esti-mates are qualitatively similar to those provided in Table 3. Lastly in row (4), weprovide exposure time estimates for students in fourth grade. Since the exam forfourth-grade students occurs after week 11, we cannot estimate overall treatmenteffects. Nonetheless, we can use the variation in time of exposure to see if theseestimates are consistent with our estimates for fifth-grade students. Indeed, that iswhat we find, as there appears to be no relationship between time of exposure toICB and achievement.
Effects on Absenteeism and Grades
Since advocates of moving breakfast to the classroom often argue that this kindof program helps to reduce tardiness and absenteeism, we also look at attendance
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 693
rates.35 Unlike the testing regressions, in these analyses, along with the assessmentsof grades, we have access to data for grades 1 through 5 so we can see if anyimpacts arise for younger students. The attendance results are limited only to the2009 to 2010 school year, since we do not have attendance rates by attendanceperiod in prior years. Hence, we use differences in timing of implementation acrossattendance periods within 2009 to 2010—ICB was implemented during attendanceperiods 4, 5, and 6—to identify treatment effects. To keep the estimates consistentwith our achievement estimates, we include lagged attendance rates for the prior twoattendance periods in the models. Since the literature has not come to a consensuson the inclusion of lagged effects in nonachievement outcomes, we also estimatedmodels excluding the lags and find similar results.
The results for attendance are provided in Table 8. We estimate three types ofmodels. The first is a corollary to equation (1) where we include an indicator forwhether ICB is in place at any point during period t. In panel II, we modify the anal-ysis to allow for separate estimates for being fully or partially treated as describedin the empirical strategy section. Finally, in panel III we estimate models based onequation (2) where the treatment effect is allowed to vary by weeks of exposure. Ingeneral, we find little evidence of impacts on absenteeism. The baseline treatmenteffect estimate is −0.06 with a standard error of 0.08. All other estimates are sta-tistically insignificant whether we split the sample by prior achievement or gradelevel.36
Table 9 provides results for average course grades. Once again our data are morelimited in years of coverage as the grades data are only available from 2008–2009through 2009–2010. Nonetheless, these data provide us eight grading periods overthe two years with ICB being implemented during the third and fourth gradingperiods of 2009 to 2010. As a result, we include the lagged grades from the prior twograding periods as controls, though once again our estimates are not sensitive to theexclusion of these lags. Using models that mirror those in Table 8, the results suggestthere is little impact of the program on grades. In all three models, there are nostatistically significant estimates overall, split by achievement level, or split by gradelevel.37 One possible explanation for the lack of impact on grades despite the impacton achievement is that, since grades have a relative component, teachers may simplyadjust their grades to the new and higher performance of the students. On the otherhand, the lack of effects here are consistent with finding no exposure-time gradienton achievement, in that they likely reflect the program impacting test performancebut not overall learning, given the relatively short period for which it had been inplace by the end of the 2009 to 2010 academic year. To examine whether the lackof an effect of grades is simply because teachers adjust their curve, we examineimpacts on different quintiles in results not shown. If teachers were adjusting theircurve, this would most likely show up at the lower end of the distribution ratherthan at the higher end where students have already reached their maximum grade.However, we do not find any differences in grades across quintiles, indicating thatthe lack of an overall effect on grades is unlikely coming from teachers adjustingtheir curves, but rather is due to a lack of an impact on learning.
35 Unfortunately, we do not have tardiness data.36 Estimates using student fixed effects instead of lagged dependent variables are similar and pro-vided in Appendix Table A4. All appendices are available at the end of this article as it appearsin JPAM online. Go to the publisher’s Web site and use the search engine to locate the article athttp://www3.interscience.wiley.com/cgi-bin/jhome/34787.37 We provide similar estimates using student fixed effects instead of lags in Appendix Table A5. All appen-dices are available at the end of this article as it appears in JPAM online. Go to the publisher’s Web site anduse the search engine to locate the article at http://www3.interscience.wiley.com/cgi-bin/jhome/34787.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
694 / The Effect of Providing Breakfast in Class
Tab
le8
.Eff
ect
ofin
-cla
ssb
reak
fast
onat
ten
dan
cera
te—
grad
es1
to5
wit
hsc
hoo
lfix
edef
fect
s.
By
abov
e/b
elow
med
ian
pri
orye
arm
ath
ach
ieve
men
t(f
ourt
han
dfi
fth
grad
eson
ly)
By
grad
e
Fu
llsa
mp
leB
elow
Ab
ove
1st
2nd
3rd
4th
5th
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
I.A
ny
trea
tmen
tT
reat
ed−0
.060
−0.0
14−0
.039
−0.0
630.
007
−0.1
070.
011
−0.1
61(0
.075
)(0
.131
)(0
.115
)(0
.138
)(0
.116
)(0
.122
)(0
.136
)(0
.130
)II
.Fu
llor
par
tial
trea
tmen
tF
ull
yT
reat
ed−0
.079
−0.0
52−0
.125
0.01
20.
021
−0.2
390.
088
−0.3
30(0
.156
)(0
.257
)(0
.196
)(0
.289
)(0
.212
)(0
.180
)(0
.239
)(0
.242
)P
arti
ally
Tre
ated
−0.0
62−0
.018
−0.0
47−0
.056
0.00
8−0
.120
0.01
9−0
.180
(0.0
80)
(0.1
38)
(0.1
19)
(0.1
45)
(0.1
19)
(0.1
24)
(0.1
38)
(0.1
38)
III.
Tre
atm
ent
and
wee
ksof
exp
osu
rein
rep
orti
ng
per
iod
Tre
ated
−0.0
36−0
.145
0.11
6−0
.167
0.06
10.
026
−0.0
950.
002
(0.1
01)
(0.1
61)
(0.1
46)
(0.1
87)
(0.1
63)
(0.1
81)
(0.1
74)
(0.1
81)
Wee
ksof
Exp
osu
re−0
.008
0.04
3−0
.050
0.03
4−0
.017
−0.0
430.
034
−0.0
54(0
.027
)(0
.041
)(0
.038
)(0
.056
)(0
.047
)(0
.038
)(0
.042
)(0
.048
)O
bse
rvat
ion
s14
8,94
527
,132
23,3
2932
,345
30,4
9330
,195
29,7
7826
,134
Not
e:D
ata
cove
rsi
xre
por
tin
gp
erio
ds
in20
09to
2010
.Att
end
ance
rate
isca
lcu
late
das
the
nu
mb
erof
day
sp
rese
nt
div
ided
by
the
nu
mb
erof
day
sen
roll
edd
uri
ng
are
por
tin
gp
erio
d.
In-c
lass
bre
akfa
st(I
CB
)p
has
esin
du
rin
gp
erio
ds
4,5,
and
6.T
reat
edis
anin
dic
ator
set
to1
du
rin
gan
yp
erio
dw
hen
ast
ud
ent’s
sch
ool
ofre
cord
—d
efin
edb
ysc
hoo
latt
end
edin
Oct
ober
—h
asat
leas
ton
ew
eek
ofIC
B.F
ully
Tre
ated
equ
als
1if
the
sch
oolh
asal
lwee
ksin
ap
erio
dw
ith
ICB
wh
ile
Par
tial
lyT
reat
edeq
ual
s1
ifth
esc
hoo
lh
asat
leas
ton
eb
ut
not
all
wee
ksin
ap
erio
dw
ith
ICB
.W
eeks
ofE
xpos
ure
den
ote
the
nu
mb
erof
wee
ksd
uri
ng
are
por
tin
gp
erio
dfo
rw
hic
ha
stu
den
t’ssc
hoo
lhas
ICB
.Cov
aria
tes
incl
ud
etw
ola
gsof
atte
nd
ance
inp
rior
per
iod
s,st
ud
ent’s
race
/eth
nic
ity,
gen
der
,an
dec
onom
icst
atu
sal
ong
wit
hre
por
tin
gp
erio
dan
dgr
ade-
leve
ld
um
mie
s.S
choo
l-le
vel
cova
riat
esar
eom
itte
das
they
are
abso
rbed
by
sch
ool
fixe
def
fect
s.*,
**,a
nd
***
den
ote
sign
ific
ance
atth
e10
-per
cen
t,5-
per
cen
t,an
d1-
per
cen
tle
vel,
resp
ecti
vely
.Sta
nd
ard
erro
rscl
ust
ered
by
sch
ooli
np
aren
thes
es.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 695
Tab
le9
.Eff
ect
ofin
-cla
ssb
reak
fast
ongr
ades
—gr
ades
1to
5w
ith
sch
oolf
ixed
effe
cts.
By
abov
e/b
elow
med
ian
pri
orye
arm
ath
ach
ieve
men
t(f
ourt
han
dfi
fth
grad
eson
ly)
By
grad
e
Fu
llsa
mp
leB
elow
Ab
ove
1st
2nd
3rd
4th
5th
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
I.A
ny
trea
tmen
tT
reat
ed−0
.010
−0.0
650.
020
0.01
30.
053
−0.0
240.
054
−0.1
37(0
.035
)(0
.072
)(0
.059
)(0
.017
)(0
.081
)(0
.094
)(0
.086
)(0
.103
)II
.Fu
llor
par
tial
trea
tmen
tF
ull
yT
reat
ed−0
.026
−0.1
230.
034
0.02
50.
062
−0.0
510.
044
−0.1
89(0
.058
)(0
.114
)(0
.097
)(0
.028
)(0
.126
)(0
.141
)(0
.140
)(0
.169
)P
arti
ally
Tre
ated
−0.0
08−0
.057
0.01
80.
013
0.05
2−0
.020
0.05
5−0
.129
(0.0
32)
(0.0
67)
(0.0
54)
(0.0
17)
(0.0
75)
(0.0
88)
(0.0
80)
(0.0
96)
III.
Tre
atm
ent
and
wee
ksof
exp
osu
rein
grad
ing
per
iod
Tre
ated
−0.0
31−0
.104
−0.0
170.
012
0.03
0−0
.004
0.01
7−0
.191
(0.0
38)
(0.0
82)
(0.0
66)
(0.0
17)
(0.0
82)
(0.0
90)
(0.1
00)
(0.1
24)
Wee
ksof
Exp
osu
re0.
004
0.00
70.
007
0.00
00.
004
−0.0
040.
007
0.01
0(0
.003
)(0
.006
)(0
.005
)(0
.001
)(0
.007
)(0
.008
)(0
.008
)(0
.009
)O
bse
rvat
ion
s18
8,67
737
,206
33,0
5931
,284
41,1
7541
,060
39,7
6235
,396
Not
e:D
ata
cove
rei
ght
grad
ing
per
iod
sin
2008
–200
9an
d20
09–2
010.
Gra
des
are
calc
ula
ted
asth
em
ean
nu
mer
ical
grad
e(o
na
scal
eof
50to
100)
over
allc
ours
es.
In-c
lass
bre
akfa
st(I
CB
)p
has
esin
du
rin
gp
erio
ds
7an
d8.
Tre
ated
isan
ind
icat
orse
tto
1d
uri
ng
any
per
iod
wh
ena
stu
den
t’ssc
hoo
lof
reco
rd—
def
ined
by
sch
ool
atte
nd
edin
Oct
ober
—h
asat
leas
ton
ew
eek
ofIC
B.
Fu
llyT
reat
edeq
ual
s1
ifth
esc
hoo
lh
asal
lw
eeks
ina
per
iod
wit
hIC
Bw
hil
eP
arti
ally
Tre
ated
equ
als
1if
the
sch
oolh
asat
leas
ton
e,b
ut
not
allw
eeks
ina
per
iod
wit
hIC
B.W
eeks
ofE
xpos
ure
den
ote
the
nu
mb
erof
wee
ksd
uri
ng
agr
adin
gp
erio
dfo
rw
hic
ha
stu
den
t’ssc
hoo
lh
asIC
B.C
ovar
iate
sin
clu
de
two
lags
ofgr
ades
inp
rior
per
iod
s,st
ud
ent’s
race
/eth
nic
ity,
gen
der
,an
dec
onom
icst
atu
sal
ong
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Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
696 / The Effect of Providing Breakfast in Class
CONCLUSION
Concerns about low take-up in SBPs have led education officials in many schooldistricts to provide free breakfast to all students in the classroom, so they do not needto get to school early to acquire breakfast from the cafeteria. These programs alsohave the potential to increase breakfast consumption over cafeteria-based programsas they reduce the potential for the stigma related to being identified as low-incomeby other students by going to the cafeteria for breakfast.
In this paper, we assess the impact of moving breakfast services from the cafeteriato the classroom on student achievement, attendance, and grades. Since such aprogram reduces the time and effort costs and potential social costs to studentsfrom consuming breakfast, such a program could affect student outcomes. We usedata from a LUSD-SW that phased in an ICB program quasi-randomly over thecourse of two months in 2010. Since the phase-in period overlaps with fifth-gradeachievement testing, we are able to identify the impact of the program on mathand reading achievement. Using a difference-in-differences strategy, we find thatproviding free breakfast to all students in the classroom increases math and readingachievement by 0.09 and 0.06 SD, respectively, relative to providing free breakfast inthe cafeteria. These effects almost entirely come from Hispanic students, with blackstudents showing little impact. Further, the effects are concentrated in studentswith low prior achievement and students with low body mass indices. We find noevidence of impacts on attendance or grades, however.
Since we cannot identify which students switch from not eating breakfast toeating breakfast, one should interpret this intention-to-treat effect as a lower boundestimate of the actual treatment effect of consuming more food prior to school.To get an idea of the treatment effect, we can use results from a nonrandomizedpilot study by the school district that found schools that implemented ICB hadtwice the consumption rate of schools that did not participate in the pilot. At facevalue, this indicates treatment effects are likely on the order of 0.12 SD in readingand 0.18 SD in math. However, this estimate should be used with caution due to thenonrandomness of the pilot. Alternatively, comparisons of take-up of classroom andcafeteria universal breakfast programs conducted by Bernstein et al. (2004) showthat take-up of those served breakfast in the classroom is 2.4 times the take-up forthose who get served in the cafeteria. Combining this statistic with our estimatesimplies treatment effects of 0.14 and 0.21 SDs in reading and math, respectively.Finally, we know that take-up after the ICB program in six case studies provided bythe district was 2.5 times take-up prior to the program. In these schools, which wererelatively more disadvantaged than the average school in the district, the treatmenteffects are estimated to be 0.16 SD in reading and 0.22 SD in math.
Moreover, cost information provided by the district for the case studies indicatesthat the cost of breakfast per student also goes down when breakfast is provided inthe classroom rather than in the cafeteria. Food, labor, and supply costs per mealdecreases from $1.76 per student to $1.32 per student after breakfast is moved fromthe cafeteria to the classrooms. Total, rather than per meal, costs increase because ofincreased consumption—from 33 to 83 percent in the case study schools. While foodand supply costs increase in line with the number of students served, the labor costsassociated with serving breakfast increase by 36 percent, which indicates that thereare efficiencies of scale that can be gained from serving breakfast in the classroom.38
Further, we note that the cost per hour of labor did not change; rather, the additionalcosts come from increased labor hours.
38 The calculation is the average change in costs over all six schools for which we have case studies.Results are similar if we focus on the school most similar to the average LUSD elementary school.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class / 697
Using these figures we can calculate that a school would experience an increasein costs per student of $0.51 per day. Assuming a 180-day school year this amountsto $91 per year. Thus, the school gains an average of 0.09 SD in math and 0.06 SD inreading for $91 per student. These gains are, of course, not universal as only thosewho change consumption are likely to be affected. For these students, the gains arebetween 0.18 and 0.22 SD in math and between 0.12 and 0.16 SD in reading. Ineither case, this is a large impact for such a small amount of spending—equal to 1.2percent of the average elementary school operating expenditures in the district.
Nonetheless, one should also be cautious in interpretation. In particular, the re-sults are likely due to impacts on test performance from higher calorie intake ratherthan actual impacts on learning. Figlio and Winicki (2005) establish that schoolssometimes increase calories during testing days to generate these effects. We findsome evidence that supports this. First, if the breakfast program increases learning,we would expect to see schools that adopt earlier to have larger impacts than thosethat adopt later. Our results show little evidence of differences by adoption timing.Nonetheless, since the implementation period is short term, we cannot rule out thatexposure time effects would emerge over longer time periods. Second, we look atimpacts on course grades and find no evidence of any impact. While this couldbe due to teachers curving their grades to match the new higher performance ofstudents, this is also consistent with the achievement results being due to improvedtest performance rather than learning.
Regardless, this study does have important policy implications. Our results sug-gest that providing breakfast to students has a marked impact on achievementscores. Schools where breakfast consumption is high—which would tend to bewealthier schools—get higher test scores than low consumption schools—whichtend to be poorer schools—even independent of learning. Since sanctions and re-wards under accountability regimes are based on these tests, low consumptionschools may be more likely to suffer the sanctions or miss out on the awards due towhat essentially amounts to a potential statistical bias in the testing metric. Giventhe size of our estimates, such a testing error could potentially be quite large. Furtherresearch should assess to what extent malnutrition results in achievement testingmismeasuring actual learning.
SCOTT A. IMBERMAN is Associate Professor of Economics and Education, Depart-ment of Economics, Michigan State University, Marshall-Adams Hall, 486 W. CircleDrive, Room 110, East Lansing, MI 48824 (e-mail: imberman@msu.edu).
ADRIANA D. KUGLER is Professor of Public Policy, Georgetown Public Policy Insti-tute, Georgetown University, Old North Building, Suite 311, 37th and O Streets, N.W.,Washington, DC 20057 (e-mail: ak659@georgetown.edu).
ACKNOWLEDGMENTS
We would like to thank Katharine Abraham, Steven Craig, David Frisvold, Judy Hellerstein,Morris Kleiner, Aaron Sojourner, Diane Whitmore Schanzenbach, Dietrich Vollrath, employ-ees of an anonymous large urban school district, and participants at seminars at the Council ofEconomic Advisors, University of Minnesota, APPAM Conference, and Stata Texas EmpiricalMicroeconomics Conference for helpful comments and suggestions.
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Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class
APP
END
IX
Tab
leA
1.E
ffec
tof
in-c
lass
bre
akfa
ston
fift
h-g
rad
eac
hie
vem
ent
wit
hou
tsc
hoo
lfix
edef
fect
s.
By
abov
e/b
elow
med
ian
2008
to20
09ac
hie
vem
ent
By
2008
to20
09ac
hie
vem
ent
quin
tile
s
Fu
llsa
mp
leB
elow
Ab
ove
Bot
tom
Sec
ond
Th
ird
Fou
rth
Top
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(A)
Mat
hI.
Red
uce
d-f
orm
trea
tmen
tef
fect
Pos
t×
Tre
ated
0.07
30.
100*
0.05
70.
064
0.13
8**0.
014
0.11
1*0.
067
(0.0
47)
(0.0
57)
(0.0
48)
(0.0
90)
(0.0
65)
(0.0
58)
(0.0
64)
(0.0
42)
Tre
ated
0.01
60.
022
0.00
9−0
.005
0.04
00.
034
−0.0
200.
015
(0.0
28)
(0.0
34)
(0.0
26)
(0.0
54)
(0.0
37)
(0.0
40)
(0.0
29)
(0.0
28)
Ob
serv
atio
ns
30,7
7115
,557
15,2
145,
055
7,01
16,
977
6,45
55,
273
II.T
reat
men
tef
fect
and
exp
osu
reti
me
Pos
t×
Tre
ated
0.12
8*0.
202**
0.06
20.
171
0.15
90.
128
0.08
40.
056
(0.0
70)
(0.0
95)
(0.0
62)
(0.1
35)
(0.1
23)
(0.0
90)
(0.0
80)
(0.0
63)
Pos
t×
Exp
osu
reT
ime
(wee
ks)
−0.0
11−0
.019
−0.0
01−0
.015
−0.0
06−0
.024
0.01
0−0
.003
(0.0
10)
(0.0
14)
(0.0
08)
(0.0
20)
(0.0
19)
(0.0
15)
(0.0
11)
(0.0
09)
Tre
ated
0.00
4−0
.009
0.01
0−0
.089
−0.0
26−0
.023
−0.0
240.
005
(0.0
48)
(0.0
59)
(0.0
44)
(0.0
93)
(0.0
65)
(0.0
56)
(0.0
45)
(0.0
34)
Exp
osu
reT
ime
(wee
ks)
0.00
20.
006
−0.0
000.
001
0.01
00.
008
−0.0
03−0
.000
(0.0
07)
(0.0
08)
(0.0
06)
(0.0
12)
(0.0
08)
(0.0
07)
(0.0
06)
(0.0
04)
Ob
serv
atio
ns
30,7
7115
,557
15,2
144,
925
5,71
65,
564
5,02
34,
022
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class
Tab
leA
1.C
onti
nu
ed.
By
abov
e/b
elow
med
ian
2008
to20
09ac
hie
vem
ent
By
2008
to20
09ac
hie
vem
ent
quin
tile
s
Fu
llsa
mp
leB
elow
Ab
ove
Bot
tom
Sec
ond
Th
ird
Fou
rth
Top
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(B)
Rea
din
gI.
Red
uce
d-f
orm
trea
tmen
tef
fect
Pos
t×
Tre
ated
0.08
1**0.
077*
0.08
2***
0.11
90.
067
0.08
2*0.
070*
0.05
7(0
.035
)(0
.045
)(0
.031
)(0
.082
)(0
.057
)(0
.042
)(0
.041
)(0
.035
)T
reat
ed−0
.017
−0.0
06−0
.021
−0.0
470.
020
0.02
3−0
.009
−0.0
57**
(0.0
30)
(0.0
38)
(0.0
28)
(0.0
58)
(0.0
41)
(0.0
40)
(0.0
36)
(0.0
23)
Ob
serv
atio
ns
25,4
4514
,065
11,3
805,
227
5,98
25,
617
4,88
63,
733
II.T
reat
men
tef
fect
and
exp
osu
reti
me
Pos
t×
Tre
ated
0.10
0*0.
081
0.10
9**0.
204
0.02
90.
179**
*0.
061
0.02
4(0
.052
)(0
.073
)(0
.047
)(0
.124
)(0
.080
)(0
.065
)(0
.066
)(0
.055
)P
ost×
Exp
osu
reT
ime
(wee
ks)
−0.0
04−0
.001
−0.0
05−0
.016
0.00
8−0
.019
*0.
002
0.00
7
(0.0
08)
(0.0
11)
(0.0
08)
(0.0
21)
(0.0
11)
(0.0
10)
(0.0
10)
(0.0
10)
Tre
ated
−0.0
10−0
.016
−0.0
02−0
.047
−0.0
00−0
.027
0.03
8−0
.026
(0.0
44)
(0.0
52)
(0.0
41)
(0.0
76)
(0.0
62)
(0.0
51)
(0.0
53)
(0.0
39)
Exp
osu
reT
ime
(wee
ks)
−0.0
010.
002
−0.0
040.
000
0.00
40.
010
−0.0
09−0
.006
(0.0
05)
(0.0
06)
(0.0
06)
(0.0
09)
(0.0
08)
(0.0
06)
(0.0
07)
(0.0
06)
Ob
serv
atio
ns
25,4
4514
,065
11,3
805,
227
5,98
25,
617
4,88
63,
733
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Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class
Tab
leA
2.E
ffec
tofi
n-c
lass
bre
akfa
ston
fift
h-g
rad
eac
hie
vem
ent—
by
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ove
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owA
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agge
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tive
tom
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n(1
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)(1
0)(1
1)(1
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3)(1
4)(1
5)(1
6)(1
7)(1
8)
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Mat
hP
ost×
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ated
0.02
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.015
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109**
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120*
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Ob
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ated
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4,97
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Not
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ugh
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0ac
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ieve
men
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ores
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dar
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ith
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ade
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year
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eto
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ange
inth
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alin
gp
roce
du
rein
2009
to20
10,
we
stan
dar
diz
eu
sin
gra
wsc
ores
.T
reat
edis
anin
dic
ator
for
wh
eth
era
sch
ool
star
tsIC
Bp
rior
toth
ete
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isth
en
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wee
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dth
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ated
Ear
lyre
fers
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eate
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wee
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ugh
4.T
reat
edL
ate
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rsto
sch
ool
trea
ted
inw
eeks
5th
rou
gh8.
Sch
ools
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ted
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tpon
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de
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vem
ent,
twic
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gen
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onom
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sal
ong
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ade-
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ies.
Sch
ool-
leve
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aria
tes
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geof
stu
den
tsw
ho
are
wh
ite,
bla
ck,H
isp
anic
,Nat
ive
Am
eric
an,A
sian
,ec
onom
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lyd
isad
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tage
d,
LE
P,
invo
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onal
edu
cati
on,
insp
ecia
led
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tion
,gi
fted
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refe
rred
toan
alte
rnat
ive
dis
cip
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ary
pro
gram
,an
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lfi
xed
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cts.
*,**
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d**
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esi
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ican
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nt,
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ls,r
esp
ecti
vely
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nd
ard
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ered
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sch
ooli
np
aren
thes
es.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class
Tab
leA
3.
Ach
ieve
men
tp
lace
bo
test
—sa
mp
leli
mit
edto
2008
to20
09an
dea
rlie
ran
dse
t20
08to
2009
asP
ost
per
iod
:n
oco
ntr
ols
orfi
xed
effe
cts.
By
abov
e/b
elow
med
ian
lagg
edac
hie
vem
ent
By
lagg
edac
hie
vem
ent
quin
tile
s
Fu
llsa
mp
leB
elow
Ab
ove
Bot
tom
Sec
ond
Th
ird
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rth
Top
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(A)
Mat
hI.
Red
uce
d-f
orm
trea
tmen
tef
fect
Pos
t×
Tre
ated
0.01
60.
029
−0.0
170.
090
0.00
70.
080
−0.0
30−0
.007
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(0.0
62)
(0.0
38)
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26)
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78)
(0.0
66)
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49)
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36)
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ated
−0.0
200.
032
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00.
022
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30.
023
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140.
017
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32)
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39)
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33)
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46)
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37)
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44)
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32)
Ob
serv
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25,4
5312
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54,
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II.T
reat
men
tef
fect
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exp
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me
Pos
t×
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ated
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ime
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ks)
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13)
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ated
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ime
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05)
Ob
serv
atio
ns
25,4
5312
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333,
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(B)
Rea
din
gI.
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uce
d-f
orm
trea
tmen
tef
fect
Pos
t×
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ated
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001
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38)
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ated
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29−0
.016
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26)
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52)
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22)
Ob
serv
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ns
20,1
2111
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8,92
94,
116
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64,
399
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42,
917
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class
Tab
leA
3.C
onti
nu
ed.
By
abov
e/b
elow
med
ian
lagg
edac
hie
vem
ent
By
lagg
edac
hie
vem
ent
quin
tile
s
Fu
llsa
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leB
elow
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ove
Bot
tom
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ond
Th
ird
Fou
rth
Top
(1)
(2)
(3)
(4)
(5)
(6)
(7)
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II.T
reat
men
tef
fect
and
exp
osu
reti
me
Pos
t×
Tre
ated
0.04
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t×
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ime
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−0.0
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10)
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12)
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ated
−0.0
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45−0
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23−0
.026
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(0.0
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35)
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68)
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53)
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(wee
ks)
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011
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006
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000.
007
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Ob
serv
atio
ns
20,1
2111
,103
8,92
94,
116
4,74
64,
399
3,85
42,
917
Not
e:D
ata
cove
rth
e20
03–2
004
thro
ugh
2007
–200
8ac
adem
icye
ars.
Ach
ieve
men
tsc
ores
are
stan
dar
diz
edw
ith
ingr
ade
and
year
.Du
eto
ach
ange
inth
esc
alin
gp
roce
du
rein
2009
to20
10,
we
stan
dar
diz
eu
sin
gra
wsc
ores
.T
he
Pos
tin
dic
ator
isse
teq
ual
to1
in20
07to
2008
.T
reat
edis
anin
dic
ator
for
wh
eth
era
sch
ool
star
tsIC
Bp
rior
toth
ete
stin
gw
eek.
Sch
ools
trea
ted
inw
eek
9ar
ed
rop
ped
asth
isis
the
fift
h-g
rad
ete
stin
gw
eek
and
som
esc
hoo
lsm
ayh
ave
pos
tpon
edth
est
art
ofIC
Bfo
rfi
fth
-gra
de
stu
den
ts.
*,**
,an
d**
*d
enot
esi
gnif
ican
ceat
the
10-p
erce
nt,
5-p
erce
nt,
and
1-p
erce
nt
leve
ls,
resp
ecti
vely
.S
tan
dar
der
rors
clu
ster
edb
ysc
hoo
lin
par
enth
eses
.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class
Tab
leA
4.E
ffec
tof
in-c
lass
bre
akfa
ston
atte
nd
ance
—gr
ades
1to
5w
ith
stu
den
tfi
xed
effe
cts.
By
abov
e/b
elow
med
ian
2008
to20
09m
ath
ach
ieve
men
t(f
ourt
han
dfi
fth
grad
eson
ly)
By
grad
e
Fu
llsa
mp
leB
elow
Ab
ove
1st
2nd
3rd
4th
5th
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
I.A
ny
trea
tmen
tT
reat
ed−0
.038
0.03
30.
016
−0.0
300.
047
−0.0
83−0
.047
−0.0
91(0
.072
)(0
.088
)(0
.075
)(0
.125
)(0
.098
)(0
.113
)(0
.119
)(0
.103
)II
.Fu
llor
par
tial
trea
tmen
tF
ull
yT
reat
ed−0
.075
0.00
5−0
.162
0.03
10.
049
−0.1
82−0
.061
−0.2
46(0
.136
)(0
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)(0
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)(0
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)(0
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)(0
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)(0
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)(0
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)P
arti
ally
Tre
ated
−0.0
380.
030
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01−0
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7−0
.081
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47−0
.091
(0.0
71)
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88)
(0.0
78)
(0.1
24)
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97)
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12)
(0.1
18)
(0.1
03)
III.
Tre
atm
ent
and
wee
ksof
exp
osu
rein
rep
orti
ng
per
iod
Tre
ated
0.03
9−0
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0.20
2*−0
.124
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20.
137
−0.0
690.
127
(0.0
89)
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30)
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12)
(0.1
60)
(0.1
46)
(0.1
47)
(0.1
55)
(0.1
56)
Wee
ksof
Exp
osu
re−0
.024
0.02
3−0
.061
**0.
029
−0.0
23−0
.067
0.00
7−0
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*
(0.0
24)
(0.0
29)
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30)
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44)
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40)
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41)
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38)
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38)
Ob
serv
atio
ns
225,
901
27,1
3223
,329
49,0
9146
,301
45,7
7345
,127
39,6
09
Not
e:D
ata
cove
rsi
xre
por
tin
gp
erio
ds
in20
09to
2010
.A
tten
dan
cera
teis
calc
ula
ted
asth
en
um
ber
ofd
ays
pre
sen
td
ivid
edb
yth
en
um
ber
ofd
ays
enro
lled
du
rin
ga
rep
orti
ng
per
iod
.In
-cla
ssb
reak
fast
(IC
B)
ph
ases
ind
uri
ng
per
iod
s4,
5,an
d6.
Tre
ated
isan
ind
icat
orse
tto
1d
uri
ng
any
per
iod
wh
ena
stu
den
t’ssc
hoo
lof
reco
rd—
def
ined
by
sch
ool
atte
nd
edin
Oct
ober
—h
asat
leas
ton
ew
eek
ofIC
B.
Fu
llyT
reat
edeq
ual
s1
ifth
esc
hoo
lh
asal
lw
eeks
ina
per
iod
wit
hIC
Bw
hil
eP
arti
ally
Tre
ated
equ
als
1if
the
sch
oolh
asat
leas
ton
e,b
ut
not
allw
eeks
ina
per
iod
wit
hIC
B.W
eeks
ofE
xpos
ure
den
ote
the
nu
mb
erof
wee
ksd
uri
ng
are
por
tin
gp
erio
dfo
rw
hic
ha
stu
den
t’ssc
hoo
lh
asIC
B.
Cov
aria
tes
incl
ud
ecy
cle
du
mm
ies.
Oth
ersc
hoo
lan
dst
ud
ent-
leve
lco
vari
ates
are
omit
ted
asth
eyar
eab
sorb
edb
yst
ud
ent
fixe
def
fect
s.*,
**,a
nd
***
den
ote
sign
ific
ance
atth
e10
-per
cen
t,5-
per
cen
t,an
d1-
per
cen
tle
vel,
resp
ecti
vely
.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
The Effect of Providing Breakfast in Class
Tab
leA
5.E
ffec
tof
in-c
lass
bre
akfa
ston
grad
es—
grad
es1
to5
wit
hsc
hoo
lan
dst
ud
ent
fixe
def
fect
s.
By
abov
e/b
elow
med
ian
2008
to20
09m
ath
ach
ieve
men
t(f
ourt
han
dfi
fth
grad
eson
ly)
By
grad
e
Fu
llsa
mp
leB
elow
Ab
ove
1st
2nd
3rd
4th
5th
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
I.A
ny
trea
tmen
tT
reat
ed0.
015
0.04
30.
047
0.00
40.
002
−0.0
05−0
.004
*−0
.000
(0.0
22)
(0.0
31)
(0.0
29)
(0.0
06)
(0.0
04)
(0.0
03)
(0.0
03)
(0.0
01)
II.F
ull
orp
arti
altr
eatm
ent
Fu
lly
Tre
ated
0.04
90.
062
0.07
60.
005
−0.0
02−0
.006
−0.0
060.
002
(0.0
39)
(0.0
52)
(0.0
49)
(0.0
11)
(0.0
08)
(0.0
06)
(0.0
04)
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02)
Par
tial
lyT
reat
ed0.
002
0.03
60.
037
0.00
30.
003
−0.0
04−0
.004
*−0
.001
(0.0
17)
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25)
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25)
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04)
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03)
(0.0
03)
(0.0
02)
(0.0
01)
III.
Tre
atm
ent
and
wee
ksof
exp
osu
rein
grad
ing
per
iod
Tre
ated
0.03
30.
121**
0.04
70.
005
0.01
5**−0
.008
−0.0
000.
000
(0.0
42)
(0.0
58)
(0.0
54)
(0.0
10)
(0.0
06)
(0.0
06)
(0.0
04)
(0.0
02)
Wee
ksof
Exp
osu
re−0
.003
−0.0
13**
0.00
0−0
.000
−0.0
02**
*0.
001
−0.0
01−0
.000
(0.0
04)
(0.0
06)
(0.0
06)
(0.0
01)
(0.0
01)
(0.0
01)
(0.0
00)
(0.0
00)
Ob
serv
atio
ns
286,
105
51,0
4044
,807
62,4
0859
,004
58,7
1355
,718
50,2
62
Not
e:D
ata
cove
rei
ght
grad
ing
per
iod
sin
2008
–200
9an
d20
09–2
010.
Gra
des
are
calc
ula
ted
asth
em
ean
nu
mer
ical
grad
e(o
na
scal
eof
50to
100)
over
all
cou
rses
.In
-cla
ssb
reak
fast
(IC
B)
ph
ases
ind
uri
ng
per
iod
s7
and
8.T
reat
edis
anin
dic
ator
set
to1
du
rin
gan
yp
erio
dw
hen
ast
ud
ent’s
sch
ool
ofre
cord
—d
efin
edb
ysc
hoo
latt
end
edin
Oct
ober
—h
asat
leas
ton
ew
eek
ofIC
B.F
ully
Tre
ated
equ
als
1if
the
sch
oolh
asal
lwee
ksin
ap
erio
dw
ith
ICB
wh
ile
Par
tial
lyT
reat
edeq
ual
s1
ifth
esc
hoo
lh
asat
leas
ton
e,b
ut
not
all
wee
ksin
ap
erio
dw
ith
ICB
.W
eeks
ofE
xpos
ure
den
ote
the
nu
mb
erof
wee
ksd
uri
ng
agr
adin
gp
erio
dfo
rw
hic
ha
stu
den
t’ssc
hoo
lh
asIC
B.
Cov
aria
tes
incl
ud
est
ud
ent
fixe
def
fect
san
dec
onom
icst
atu
sal
ong
wit
hgr
adin
gp
erio
dan
dgr
ade-
leve
ld
um
mie
s.In
dic
ator
sfo
rra
cean
dge
nd
erar
eab
sorb
edb
yth
est
ud
ent
fixe
def
fect
s.S
choo
l-le
vel
cova
riat
esin
clu
de
per
cen
tage
ofst
ud
ents
wh
oar
ew
hit
e,b
lack
,H
isp
anic
,N
ativ
eA
mer
ican
,A
sian
,eco
nom
ical
lyd
isad
van
tage
d,L
EP
,in
voca
tion
aled
uca
tion
,in
spec
iale
du
cati
on,g
ifte
d,i
nb
ilin
gual
edu
cati
on,i
nea
chgr
ade
leve
l,re
ferr
edto
anal
tern
ativ
ed
isci
pli
nar
yp
rogr
am,a
nd
sch
oolf
ixed
effe
cts.
*,**
,an
d**
*d
enot
esi
gnif
ican
ceat
the
10-p
erce
nt,
5-p
erce
nt,
and
1-p
erce
nt
leve
l,re
spec
tive
ly.
Journal of Policy Analysis and Management DOI: 10.1002/pamPublished on behalf of the Association for Public Policy Analysis and Management
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