The Effect of School Lunch on Early Teenagers’ Body Weight
Shiko Maruyama a,*, Sayaka Nakamura baEconomics Discipline Group, University of Technology Sydney, PO Box 123, Broadway,
NSW 2007 Australia, Email: shiko.maruyama@uts.edu.aubSchool of Economics, Nagoya University, Furocho, Chikusa, Nagoya, 464-8601 Japan,
Email: nakamuras@soec.nagoya-u.ac.jp

THIS VERSION: January 21, 2016

JEL codes: H51, I18, I28, J13

The Effect of School Lunch on Early Teenagers’ Body Weight
Abstract
Previous findings on the effects of school meal on child obesity are highly mixed. We
examine the causal effect of school lunch on Japanese junior high school students based onmunicipality-level variation in school lunch provision. Unlike in previously studies,
individual selection into school lunch participation and stigma induced under-reporting ofparticipation are not issues in this study. We use individual level data drawn from the 19751994 National Nutrition Survey (NNS). To account for possible endogeneity of municipal
provision of school lunch, we employ difference in differences (DID) framework andcompare differences between junior high school students and elementary school students
between areas with and without school lunch at junior high schools. We find no evidence
that school lunch affects body weight in the full sample analyses. However, in subsample
analyses of children with below median per-capita household expenditure, we findsignificant negative effect of school lunch on Body Mass Index (BMI) and overweight.
These findings are robust to trimming based on propensity scores for ensuring sufficient
overlap of characteristics between areas with and without school lunch at municipal juniorhigh schools. Additionally, municipal school lunch provision at junior high school is not
significantly associated with height, BMI, or eating habits of local pre-school andelementary school children, which does not support the reverse causality.

1. Introduction
Child obesity is growing rapidly around the world, just like adult obesity (Ng et al.
2014), and its adverse health consequences are well established (e.g., Biro and Wien 2010;
Reilly and Kelly 2011; Pulgarón 2013). As a part of public health intervention against
obesity, school lunch reforms are recently enacted in the US and the UK: stricter nutritional
standards were introduced in the US, England, and Scotland, and eligibility for free school
lunch was significantly expanded in England and Scotland (Scottish Government 2014;
Woo-Baidal and Taveras 2014; Long 2015). These reforms are, however, under heateddebate due to their high costs and uncertainties regarding the effectiveness (Woo Baidal
and Taveras 2014). There are two remaining questions that are closely related to thiscontroversy. First, school meal participation has been scrutinized as a risk factor of obesity
but previous findings are mixed, with some studies finding support for this hypothesis for
the US (Whitmore-Schanzenbach 2009; Millimet et al 2010; Hernandez et al. 2011), andothers not finding support for the US (Gundersen et al. 2012; Mirtcheva and Powell 2013)
and for the UK (Rona et al., 1983; Rona and Chinn, 1989; von Hinke Kessler Scholder
2013). Gundersen et al. (2012) point out that the assessment of the causal effect of school
meal participation on obesity is made difficult by the combination of endogenous
individual selection into participation and significant underreporting of participation. In
their nonparametric bounds analysis possibility of large and systematic underreporting
prevents definitive conclusion. Second, while some nutrition scholars argue that highquality school meal is an effective tool against obesity (Woo Baidal and Taveras 2014;
Kaneda and Yamamoto 2015), evidence of successful cases is scarce.

This study examines the causal effect of school lunch on Japanese junior high school
students based on municipality-level variation in school lunch provision from 1975 to 1994.
In our sample school lunch is provided at almost all municipal elementary schools and at
about 80% of municipal junior high schools. There are no eligibility restrictions, and on thecontrary, at municipal schools that provide school lunch, students are not allowed to bringfood to school and in principle all students must eat school lunch. Thus, unlike inpreviously studies, individual selection and under-reporting arising from stigma areirrelevant to this study. We use individual level data drawn from the National Nutrition
Survey (NNS), a nationally representative, annual household survey with measured height
and weight data and nutritionist-assisted food records. To account for the possibility thatunobserved municipal characteristics affect both children’s body weight and municipal
decision to provide school lunch, we employ difference in differences (DID) frameworkand compare differences between junior high school students and children in higher gradesof elementary school between areas with and without school lunch at junior high schools.
We find no evidence that school lunch affects body weight in full sample analyses.
However, in subsample analyses of children with low per-capita household expenditure, wefind significant negative effect of school lunch on BMI and overweight. These findings arerobust to trimming based on propensity scores for ensuring sufficient overlap of observed
area characteristics between areas with and without school lunch at municipal junior highschools. Additionally, municipal school lunch provision at junior high school is not
significantly associated with either height, BMI, or eating habits of local pre-school andelementary school children, implying that growth and weight problems among local
children has little effects on municipal decision to provide school lunch.
2. Background
2.1 Obesity in Japan
In Japan, despite the low average BMI and the low prevalence of obesity with BMI
30 or over (less than 3% among adults), the prevalence of obesity-related diseases such as
diabetes is close to that in other developed countries with significantly higher prevalence ofobesity (Guariguata et al. 2014), pausing a serious public health issue (McCurry 2007).
Because the rise in obesity-related health risks starts at lower BMI among Asians including
Japanese than among Caucasians, since 2000 the Japan Society for the Study of Obesity
advocates defining obesity as BMI of 25 or over, as opposed to the WHO benchmark of
BMI 30. Under this criterion about 20% of Japanese men and women are obese since the1950s (Kanazawa et al. 2002; Kodama et al. 2013). Both obesity prevalence and mean
BMI have significantly increased among Japanese children from the late 1970s to around2000 in Japan (Matsushita et al. 2004; Yoshinaga et al. 2010; Maruyama and Nakamura2015). Japanese studies have found that child obesity is a strong predictor of adult obesity(Togashi et al. 2002; Ge et al. 2011) and causes child metabolic syndrome (Yoshinaga et al.
2005). Until the paradigm shift occurred in the 2000s, however, few Japanese pediatricians
were concerned about mild obesity or metabolic syndrome in childhood (Yoshinaga 2012).
Thus, our study period from 1975 to 1994 corresponds to the time when child obesity was
growing but little attention was paid to obesity, even less to child obesity in Japan, whichreduces concerns for reverse causality from local prevalence of obesity to municipal school
lunch provision.
2.2 The School Lunch Program
In Japan government subsidized school lunch program for elementary school children
with low-income background started in 1932, but were interrupted due the deterioration in
the war situation in 1944. Large-scale provision of school lunch without eligibility
restrictions started right after the WWII under American occupation as a measure againstchild malnutrition resulting from severe food shortage. School lunch for elementary school
children was resumed in Tokyo in 1946, and was gradually expanded to nationwide by1951. The nutritional and sanitary guidelines became the School Lunch Law in 1954, andthe law was revised to include junior high schools in 1956 (National Institute for
Educational Policy Research (NIEPR), 2013). The law obliges municipalities only to“make effort” to provide school lunch at its municipal elementary and junior-high schools,
allowing municipalities’ discretion. Despite this municipal variation, because school lunch
is regarded as a part of education in Japan, at schools with school lunch bringing food toschool is not allowed and all students must eat school lunch, except for students withspecial dietary needs such as food allergy (Ono 2007). Under the law municipalities pay thelabor costs and the cost of construction and maintenance of facilities, guardians pay
ingredient costs and energy bills, and the national treasury subsidizes facility construction(NIEPR, 2013). As a result, households’ payment for school lunch has been kept to a
relatively low level. In 2013, for example, the average monthly school lunch fee is about

4,200 yen for elementary schools and about 4,800 yen for junior high schools (MEXT
2015). Payments are exempted for children from low-income households, and all but few
municipalities provide school lunch to all students regardless of fee payment (Fujisawa
2008).
Since the enactment of School Lunch Law in 1954, Ministry of Education has set
nutritional standards for school lunch, including target values for energy, protein, total fat,calcium, and vitamin (Nozue 2011). The standards are overall stricter than theircounterparts in the US and UK. In particular, in the US even current federal requirements
lack target values for protein, total fat, calcium, and vitamin, and there were no maximum
requirements for energy supply until 2012 (Woo-Baidal and Taveras 2014). In Englandand Scotland there have been no legally binding nutrition requirements for school lunch
until the mid-2000s, although current nutrition requirements are similar to the Japanesestandards in many ways (Scottish Government 2008; Dimbleby and Vincent 2013). Since
1954 the standards have been revised eight times, mostly for minor changes. During ourstudy period the revision occurred only once in 1986. In the 1986 revision target values forfat was replaced with the maximum percentage of energy intake from fat of 30 percent, andthe target amount for energy and protein were slightly reduced (Nozue 2011).1 Nevertheless,apparently the revision did not cause significant changes either in energy supply or in fat
supply from 1985 to 1990 (Narusaka 1996).
1

Other changes include the introduction of target value for iron, reductions in the target

values for calcium, vitamins A, B1, and B2, and an increase in the target value for vitamin C(Nozue 2011).

The School Lunch Law lists educational goals of the school lunch program, such as
to encourage good eating habits, foster sociability, promote health, and gain knowledge on
foods. Since 1958 Ministry of Education placed the school lunch program as a part of thecurriculum, and instructed teachers to guide table manners and discourage picky eating.
Since 1970, Ministry of Education encourages each municipal school to hire a licensed
nutritionist for nutrition planning, supervision over food preparation and hygiene control,and guidance to students on desirable diet (NIEPR, 2013).
While some speculate that the school lunch program has contributed to the lower
prevalence of obesity in Japan to the school lunch program (Fisher 2013; Kaneda andYamamoto 2015), we know of no study that examines the causal effect of Japanese school
lunch program on child obesity. Previous studies find significant positive association
between school lunch and intake of vegetables and dairy products by comparing foodintake in weekdays and weekends among students of elementary schools with school lunch(Nozue et al. 2010) and comparing junior high school students attending municipal schools
with and without school lunch (Kawaraya and Mori 2009).
3. Data
3.1 The National Nutrition Survey
We construct a sample of 9 to 15 years old children in elementary school or juniorhigh school using individual-level data drawn from the 1975-1994 National Nutrition
Survey (NNS). The NNS is a nationally representative, annual cross-sectional survey
conducted by the Ministry of Health, Labour and Welfare (MHLW) (Katanoda et al.

2005).2 Census districts, which are subdivisions of a municipality, are cluster sampled fromall 47 Japanese prefectures, and all households within the sampled districts are asked toparticipate in the survey. This sampling scheme fits well with this study because we exploit
municipality-level variation in school lunch program. The response rate is not reported forearlier years, but in 2002, for instance, among about 5,000 households invited to the survey,
4,160 participated in the survey (MHLW 2003). Height and weight data are measured
without shoes and with adjustment for the weight of clothes by health professionals, andthus are accurate and free from the reporting bias associated with self-reports (Connor
Gorber et al. 2007). The NNS also contains a self-administered questionnaire on
demographic and socioeconomic characteristics of the household and household members
and a nutritionist-assisted questionnaire on food intake. A certified nutritionist visits eachparticipating household to provide further guidance and correct for misreporting.
The NNS is conducted on a random subsample of the Comprehensive Survey of
Living Conditions (CSLC), another annual survey conducted by the MHLW since 1986.
We merge the NNS and the CSLC to utilize age in month information from the CSLC for
years 1986-1994.
3.2 Municipal provision of school lunch

2

The NNS was renamed the National Health and Nutrition Survey in 2003.

Until 1994, the NNS survey period covers three consecutive days not including
Sunday, and the questionnaire asks whether the respondent had school lunch.3 The NNS
includes a masked identifier for census district, and each census district belongs to only one
municipality. We categorize whether the municipality of each census district provides
school lunch at its municipal junior high schools based on the majority rule; if the half ormore of junior high school students with valid type of lunch information report having
school lunch, then the area is regarded as providing school lunch at municipal junior highschools. We exclude areas with less than two reports, and areas with exactly two conflicting
reports. We analogously categorize school lunch provision at municipal elementary schools.
There are two possible reasons for children not having school lunch during the survey
period other than the municipal school not providing school lunch. First, some children
might miss school lunch due to sickness or extra-curricular activities such as excursion. Our
categorization based on the majority rule and exclusion of areas with insufficient
information would take account of this kind of noise. Second, a nonnegligible number ofchildren attend non-municipal schools, such as private and national schools, althoughchildren attending non-municipal elementary schools are very rare in Japan. We exclude
prefectures with high rate of junior-high students attending non-municipal schools from3

Since 1995, the food diary only covers one day from Monday to Saturday. Because school

lunch is not served on Saturdays, if a respondent household chooses Saturday for food diarythen no information is collected regarding whether children regularly have school lunch.
Thus we do not use data collected after 1994.

sample.4 The rate of junior high school students attending non-municipal schools has
increased over time, so we exclude prefectures where the rate is 5% or higher in the early
1990s (Tokyo, Kochi, Nara, Kanagawa, Kyoto, Hyogo, Hiroshima, Osaka, Chiba, Mie),
dropping about 35% of the observations.
3.3 Identifying elementary school and junior-high school students
The questionnaire asks if children are in compulsory education, but does notdistinguish between elementary school and junior high school. Because the school grade isstrictly determined by the child’s age on April 1st in Japan, among children in compulsory
education we categorize 6-11 year olds as elementary school students and 13-15 year olds
as junior-high students. Additionally, for years 1986-1994 the birth month is available in
the CSLC, so we categorize 12 year olds born between April and October as elementary
school students and those born between December and March as junior high students. We
exclude 12 year olds for years 1975-1985 and 12 year olds born in November for years
1986-1994 from sample, because we cannot determine whether they attend elementary
school or junior high school.
3.4 Exclusion criteria
As described above, our sample consists of 9 to 15 years old children in elementary
school or junior high school, excluding 12 year olds for years 1975-1985 and 12 year olds
born in November for years 1986-1994, and excluding prefectures with high rate of juniorhigh students in non-municipal schools. These criteria lead to a sample with 13,186
4

Information on school ownership type is not available in the NNS.

elementary school children and 11,388 junior high school children. Other exclusion criteria
are as follows. We limit our sample to children with valid information on height and weight,
and exclude a small number of children whose height is less than 100cm and/or whose
weight is larger than 100kg as deviants. A small number of children without mother inhousehold or without valid report on household expenditure are also excluded from sample.
We further limit our sample to areas with at least one elementary school student and one
junior-high school student because our identification is based on difference-in-differences.
We also exclude areas with unreliable school lunch information due to too few and/or
conflicting reports as described above and a small number of areas without school lunch forelementary school children. Our final sample consists of 9,633 elementary school children
and 8,353 junior high school children.
3.5 Outcome measures
As outcome measures we use height, BMI, and overweight. We use several
definitions for overweight. First, for comparability with other studies we use “international
definition” (Cole et al. 2000): based on the estimated gender-specific BMI distribution for
the Japanese by Kato et al. (2011), we use the percentage corresponding to BMI 25 at age
17.5 as the cutoff value for overweight. Second, we also use a modified version of weightfor height called “percentage of overweight” (POW), which is widely used in Japan:
Children whose measured weight exceeds the standard weight for height by more than 20%
are categorized as overweight. We use the standard weight for height by age and gender
estimated by Murata and Ito (2003). POW has an advantage over BMI-based measures inthat BMI increases with height for children in puberty and because reliable estimate ofstandard weight is available for Japanese children due to little ethnic heterogeneity and theavailability of high quality data (Sugiura and Murata 2011; Dobashi 2016). The first point
is particularly relevant to this study because we compare junior high school students withelementary school students.
3.6 Individual characteristics
As demographic characteristics we control for age and gender. To allow for genderspecific age effects, we include dummy variables for the interaction of gender and age. We
also control for various parental and household characteristics, including presence of father,
grandfather, grandmother in household, parental age, parental height and BMI (in z-scoresby gender, age, and five year cohort), parental occupations (laborer, white collar worker,
self-employed, agriculture, other), the number of children (17 years old or younger) inhousehold, and the percentage ranking of total monthly household expenditure per capita.5
The household expenditure is reported by eight categories, and the categorization varies by
survey year. Thus we calculate the percentage rank of each category by survey year over all
households.6
3.7 Area characteristics
5

A small number of children without mothers in household are excluded from sample.

Household members 27-59 years old are regarded as parents and those aged 60 or older are
regarded as grandparents. Mean values are used in the case of multiple “fathers” or“mothers”.
6

Mean values of categories are used in the case of conflicting reports within household.

In the analysis of determinants of municipal school lunch provision at junior highschools we control for various area characteristics, including geographic characteristics and
NNS-based aggregate values. Geographic characteristics include prefecture specific effects
or regional block specific effects (Hokkaido and Tohoku, Kanto, Chubu, Kinki, Chugoku
and Shikoku, Kyushu and Okinawa), dummies for municipal size (town and village, cities
with less than 50 thousand population, cities with 50-150 thousand population, cities withmore than 150 thousand population, 11 largest cities), and annual prefectural population
density obtained from Statistics Bureau (2012). NNS-based aggregate values include meanage, median percentage ranking of per-capita household expenditure, mean household size,means of height and BMI among adults (in z-scores by age, gender, and five year cohort),mean frequencies of dining out and breakfast skipping among adults, and occupational
composition among 23-54 year olds (laborer, white collar worker, self-employed,
agriculture, other).
4. Estimation Strategy
We examine the effect of not having school lunch at junior high school on children’s
BMI and overweight status, because children in census districts with school lunch atmunicipal junior high schools (the control areas) outnumber those without (the treatmentareas). Cross sectional comparison between junior high school students who have and do
not have school lunch would be problematic if there are systematic differences in local
obesogenic environment between municipalities that provide and do not provide school
lunch. Community characteristics, such as availability of healthy and unhealthy food, urban

sprawl, access to parks and sports facilities, and transportation systems are significantly
associated with local obesity prevalence (Booth et al. 2005, Lake and Townshend 2006). To
account for possible unobserved heterogeneity, we use difference in differences (DID)
framework and compare differences in body weight measures between the target andcontrol areas. Specifically, using a sample of junior high school students and elementary
school students in higher grades, we estimate a linear regression model in which outcome
measures are regressed on the interaction of Junior High dummy, indicating that a child is ajunior high school student, and No School Lunch Area dummy, a dummy variableindicating no school lunch for junior high school in the census district, individual
characteristics, and census district specific effects.7 To allow for correlation of the error
term within each census district, we cluster standard errors at census district level.
DID framework relies on the assumption that unobserved differences in area
characteristics between the target and control areas have similar effects on both elementary
school students and junior high school students. This assumption would be violated if there
are systematic differences in the growth pattern, in particular in the timing of puberty onset,
between the target and control areas, although the use of POW-based obesity definitions
would mitigate this potential issue. To examine this possibility we include height as aregressand in DID analyses as a placebo test. Because height is determined primarily by
genetic factors and early-life environment (Beard and Blaser 2002), school lunch isunlikely to have a strong, immediate effect on height. At the same time, if the timing ofpuberty onset differs between the target and control areas, then the coefficient of on the
7

Year effects and area characteristics are absorbed in census district specific effects.

interaction of Junior High dummy and No School Lunch Area dummy would capture that
effect.
If the nutritional content of lunch children bring from home to school in the absenceof school lunch varies with socioeconomic status (SES), the effect of school lunch on bodyweight could also differ by SES.8 Japanese school lunch is universal in that regardless ofhousehold income all children eat school lunch at municipal schools with school lunch,whereas in the previously studied countries school lunch participation is more prevalent
among children from low income families due to income based subsidies. This might leadto a greater heterogeneity in the effect of school lunch in Japan than in those countries. To
examine this possibility we conduct a subsample analysis of children with lower per capita
household expenditure than the median.
Because linear regression adjustment is not robust to nonlinearity, we trim the sample
based on propensity scores to ensure sufficient overlap in characteristics between the target
and control areas for a robustness check. Using a logistic regression model we regress No
School Lunch Area dummy on the area characteristics described above to obtain propensity
scores.9 For each of the target and control groups we define the support as the interval

8

The literature indicates inverse association between SES and child obesity in developed

countries (Mclaren 2007; Shrewsbury and Wardle 2008). Findings of socioeconomic
gradient of obesity in childhood and adolescent in Japan are mixed, and there are no studies
based on a large scale, individual level data (Sakai 2013; Kachi et al. 2015).
9

Following Imbens (2015), to increase the matching quality we reduce the number ofregressors and also add the interaction terms of important variables to the regressors.

between the first and 99th percentiles of the estimated propensity scores, and exclude
observations outside the common support (Stuart 2010). We also exclude observations
whose estimated propensity score is smaller than 0.1 or larger than 0.9 (Imbens 2015). It
turned out that this trimming reduces the absolute standardized difference of all the
explanatory variables except for prefecture dummies between the target and control areas toless than 0.25 for both the full and sub samples, where the values above 0.25 are considered
as problematic (Rubin 2001).
Another possible source of endogeneity is the reverse causality from child obesityand other growth problems to school lunch provision. To examine this possibility weregress No School Lunch Area dummy on mean height and mean BMI (in z-scores bygender, age, and five year cohort), the mean frequencies of restaurant dinner and breakfast
skipping during the survey period of elementary and preschool children, as well as otherarea characteristics using census district level, aggregate data.10 We estimate both OLS andlogistic regression models.
5. Results
Summary statistics for the full sample are shown in Table 1. About 79% of children
live in areas with school lunch at municipal junior high schools. 7.7% of elementary school

Specifically, we replace year dummies with a linear time trend and replace prefecturedummies with regional block dummies. We also add interaction terms of municipal size
dummies and annual prefectural population density.
10

We exclude districts with less than five elementary school or preschool children.

students and 6.7% of junior high school students are overweight according to the POW, and7.7% of elementary school students and 5.9% of junior high school students are overweightaccording to the international definition, which is based on the percentage of individuals
with BMI 25 or over at age 17.5. Children whose POW exceeds 30% or whose BMI
percentile exceeds the threshold corresponding to BMI 30 at age 17.5 are rare. Comparing
the treatment and control areas, overweight among elementary school children is moreprevalent in the control areas than in the treatment areas but overweight among junior highschool children is more prevalent in the treatment areas than in the control areas for both
POW and the international standard. On the other hand, the differences in height are small
for both elementary school children and junior high school children. Additionally, themajority of children in the target group live in cities with 50,000 population or more,
whereas the majority of children in the control group live in towns, villages, or small cities
with less than 50,000 population.
Table 2 shows the summary statistics of outcome measures for the subsample ofchildren with below median per capita total household expenditure. For both elementary
school students and junior high school students there are little differences in the means of
any of the outcome measures between the subsample and the full sample shown in Table 1.
Comparison between the target and control areas yields similar observations to those fromthe full sample: greater overweight prevalence among elementary school children in thecontrol areas than in the treatment areas, greater overweight prevalence among junior highschool children in the treatment areas than in the control areas, and small differences inheight for both elementary school children and junior high school children.

Results of the regression of No School Lunch Area dummy, i.e., lack of school lunch
provision at municipal junior high schools at the census district, are shown in Table 3.
Model 1 and Model 2 are linear regression models, and Model 3 and Model 4 are Logit
models. Estimated coefficients are shown for linear regression models, and odds ratios areshown for logit models. Model 1 and Model 3 control for prefecture fixed effects and Mode
2 and Model 4 control for regional block fixed effects instead of prefecture fixed effects. In
all specifications means of height, BMI, frequencies of breakfast skipping and restaurant
dinner among elementary school and preschool children are both independently and jointly
insignificant, implying that municipal provision of school lunch at junior high school is not
influenced by obesity prevalence or other problems related to growth and eating habits ofelementary school and preschool children. In addition, few of the aggregate area
characteristics are significant. Major determinants of school lunch are the municipal size:
the likelihood of school lunch provision sharply decreases with the municipal population
size. The estimated coefficients of year dummies are omitted for space reasons, but theresults indicate that school lunch provision has only weakly increased over time.
Results of the difference in differences regression analysis for the full sample areshown in Table 4. The estimated average treatment effects are insignificant for all of theweight measures, for both the untrimmed and trimmed samples. These results imply thatthe effect of school lunch for junior high school students on BMI and overweight status isinsignificant on average. The estimated effect of school lunch on height is also insignificant,
implying that there are no systematic differences in the growth pattern between children inareas with and without school lunch at junior high schools.

Results of the subsample analysis of children whose per capita household expenditureis below the median are shown in Table 5. In contrast to the results of the full sample
analysis, the estimated average treatment effects are all significantly positive for all of theweight measures, for both the untrimmed and trimmed samples. These results imply that
school lunch significantly reduces BMI and overweight status for children from lessaffluent households. The estimated average treatment effect (ATE) is nonnegligible: thelack of school lunch increases BMI by about 0.4, and increases overweight prevalence by
about 5%. Similarly to the results of the full sample analysis the estimated effect of school
lunch on height is insignificant, implying that there are no systematic differences in thegrowth pattern between children with low household expenditure in areas with and withoutschool lunch at junior high schools.
6. Conclusion
We examine the causal effect of school lunch on Japanese junior high school students
using individual level data drawn from the 1975-1994 National Nutrition Survey (NNS). To
account for possible endogeneity of municipal provision of school lunch, we employ
difference in differences (DID) framework and compare differences between junior highschool students and elementary school students in higher grades between areas with andwithout school lunch at junior high schools. We find no evidence that school lunch affects
body weight in full sample analyses. However, in subsample analyses of children with low
per-capita household expenditure, we find significant negative effect of school lunch on
overweight.

Acknowledgments: The authors are grateful for a Grant-in-Aid for Young Scientists (B)No. 25780187 from the Japan Society for the Promotion of Science. We also wish to thankEiji Mangyo and other seminar participants at Nagoya University for their helpfulcomments. Both authors declare that they have no conflicts of interest. An official
permission to utilize the National Nutrition Survey (NNS) and the National Health andNutrition Survey (NHNS) was obtained from the Statistics and Information Department of
the Ministry of Health, Labour and Welfare on October 2, 2015 (Permission Number 10026) and November 20, 2015 (Permission Number 1120-1), respectively.

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Table 1. Summary statistics for the full sample.
All

N
No School Lunch Area
Junior HighHeight: elementary
Height: junior high
BMI: elementary
BMI: junior highObesity: POW 20%+: elementary
Obesity: POW 20%+: junior highObesity: BMI 25+ at age 17.5:
elementary
Obesity: BMI 25+ at age 17.5: juniorhighObesity: POW 30%+: elementary
Obesity: POW 30%+: junior highObesity: BMI 30+ at age 17.5:
elementary
Obesity: BMI 30+ at age 17.5: junior
high
Year
Male
Age
Prefectural population density
Hokkaido & Tohoku
Kanto
Chubu
Kinki
Chugoku & Shikoku
Kyushu & Okinawa
11 largest cities
Cities: 150k+ population
Cities: 50-150k population
Cities: 50k- population
Towns & villages

0.407
0.499
8.549
7.707
2.472
2.643
0.266
0.250

Treatment Areas(Without school
lunch at junior
high)
Mean
SD
3758
1
1
0.459 0.498
138.523 8.369
158.290 7.637
17.174 2.397
19.649 2.680
0.068 0.253
0.071 0.257

0.077

0.266

0.069

0.253

0.079

0.269

0.059

0.236

0.063

0.243

0.058

0.235

0.033
0.030

0.179
0.172

0.031
0.037

0.172
0.188

0.034
0.029

0.181
0.167

0.004

0.067

0.004

0.063

0.005

0.068

0.007

0.084

0.006

0.080

0.007

0.086

1983.851
0.516
11.805
0.474
0.177
0.160
0.520
0.030
0.113
0.205
0.059
0.269
0.220
0.106
0.346

5.465
0.500
2.008
0.438
0.382
0.366
0.500
0.171
0.317
0.403
0.236
0.443
0.414
0.308
0.476

1983.092
0.520
11.758
0.500
0.170
0.072
0.604
0.069
0.085
0.261
0.180
0.348
0.236
0.106
0.131

5.561
0.500
1.996
0.423
0.376
0.258
0.489
0.254
0.279
0.439
0.385
0.476
0.425
0.307
0.337

1984.051
0.515
11.817
0.467
0.179
0.183
0.498
0.020
0.121
0.190
0.027
0.248
0.215
0.106
0.403

5.422
0.500
2.011
0.442
0.383
0.387
0.500
0.139
0.326
0.392
0.162
0.432
0.411
0.308
0.491

Mean
17986
0.209
0.464
138.454158.058
17.271
19.631
0.077
0.067

SD

Control Areas(With school lunchat junior high)
Mean
14228
0
0.466
138.436
157.99717.29719.626
0.079
0.066

SD
0
0.499
8.597
7.724
2.491
2.634
0.270
0.248

Table 1. Summary statistics for the full sample. (cont.)
All

Area: mean age
Area: HH median % ranking of HH
expenditure
Area: mean HH size
Area: mean adult height (in z score)
Area: mean adult BMI (in z score)
Area: mean adult #restaurant
dinner
Area: mean adult #breakfast
skipped
Area: fraction of laborer
Area: fraction of white collar worker
Area: fraction of self-employed
Area: fraction of agriculture
Area: fraction of other occupation
Area: mean child height (in z score)
Area: mean child BMI (in z score)
Area: mean child #restaurant dinner
Area: mean child #breakfast
skippedFather in HH
Father's age
Father's height (in z score)
Father's BMI (in z score)
Father's BMI missing
Father: laborer
Father: self-employed
Father: agriculture
Father: other occupation
Father: white collar worker
Mother's age
Mother's height (in z score)
Mother's BMI (in z score)
Mother's BMI missing
Mother: laborer
Mother: white collar worker
Mother: self-employed
Mother: agriculture
Mother: other occupation
Grandfather in HH
Grandmother in HH#child in HH% ranking of HH expenditure

Mean
34.777SD
5.342

Treatment Areas(Without school
lunch at junior high)MeanSD
34.498
5.126

Control Areas(With school lunchat junior high)MeanSD
34.851
5.395

0.585

0.195

0.534

0.192

0.599

0.193

4.326
-0.063
0.026

0.624
0.254
0.227

4.217
-0.002-0.008

0.587
0.252
0.225

4.354
-0.079
0.035

0.630
0.252
0.227

0.253

0.148

0.249

0.153

0.254

0.147

0.221

0.163

0.244

0.164

0.215

0.162

0.273
0.284
0.146
0.081
0.216
-0.009
0.005
0.106

0.151
0.148
0.122
0.154
0.106
0.356
0.352
0.145

0.272
0.288
0.156
0.056
0.228
0.019
-0.012
0.105

0.152
0.148
0.123
0.135
0.100
0.354
0.340
0.145

0.273
0.283
0.144
0.087
0.213
-0.016
0.009
0.106

0.151
0.148
0.121
0.158
0.107
0.356
0.354
0.145

0.056

0.107

0.063

0.125

0.054

0.102

0.907
38.113-0.025
0.008
0.252
0.292
0.187
0.074
0.009
0.437
39.558-0.063
0.074
0.036
0.244
0.162
0.131
0.087
0.375
0.196
0.307
2.305
0.658

0.290
12.945
0.847
0.851
0.434
0.453
0.389
0.260
0.091
0.495
4.385
0.976
0.984
0.187
0.426
0.365
0.336
0.279
0.479
0.397
0.461
0.787
0.249

0.906
38.1780.017-0.021
0.256
0.299
0.193
0.053
0.008
0.447
39.550-0.016-0.003
0.036
0.235
0.170
0.136
0.055
0.404
0.165
0.271
2.280
0.629

0.291
13.024
0.855
0.870
0.436
0.457
0.395
0.223
0.086
0.496
4.315
0.976
0.927
0.187
0.420
0.372
0.342
0.226
0.486
0.371
0.445
0.818
0.246

0.907
38.095-0.036
0.015
0.251
0.291
0.186
0.079
0.009
0.435
39.560-0.076
0.094
0.036
0.247
0.160
0.130
0.096
0.367
0.204
0.317
2.312
0.665

0.290
12.924
0.844
0.846
0.434
0.452
0.388
0.268
0.092
0.495
4.403
0.976
0.997
0.187
0.428
0.363
0.334
0.290
0.477
0.403
0.465
0.778
0.249

Table 2. Summary statistics of selected variables for children with low household expenditure.
All

Mean
N
No School Lunch Area
Height: elementary
Height: junior high
BMI: elementary
BMI: junior highObesity: POW 20%+: elementary
Obesity: POW 20%+: junior highObesity: BMI 25+ at age 17.5:
elementary
Obesity: BMI 25+ at age 17.5:
junior highSD

Treatment Areas(Without school
lunch at junior high)
Mean
SD
1671
1
1
138.0468.421
157.809
7.614
17.1362.326
19.764
2.844
0.065
0.247
0.093
0.290

Control Areas(With school lunchat junior high)
Mean
SD
7282
0
0
137.9428.606
157.6147.795
17.2762.523
19.648
2.681
0.082
0.274
0.064
0.245

8953
0.187
137.962
157.650
17.25019.670
0.079
0.069

0.390
8.571
7.761
2.488
2.712
0.270
0.254

0.078

0.268

0.065

0.247

0.081

0.272

0.062

0.240

0.080

0.272

0.057

0.232

Table 3. Regression results of No School Lunch Area dummy

Prefecture dummies
Regional block dummies: reference group: Chubu
Hokkaido & Tohoku
Kanto
Kinki
Chugoku & Shikoku
Kyushu & Okinawa
Municipal size dummies: reference group: towns andvillages
11 largest cities
Cities: 150k+ population
Cities: 50-150k population
Cities: 50k- population
Population density
Year dummies: reference group: Year 1975
Year 1976
Year 1977
Year 1978
Year 1979
Year 1980
Year 1981
Year 1982
Year 1983
Year 1984
Year 1985
Year 1986
Year 1987
Year 1988
Year 1989
Year 1990
Year 1991
Year 1992
Year 1993
Year 1994

OLS
Coefficients
Model1Model2
Yes
No

Logit
Odds Ratio
Model3Model4Yes
No‐0.016‐0.090***0.318***
‐0.04
0.037

0.943
0.464***
5.858***
0.775
1.304

0.538***
0.190***
0.142***
0.099***‐0.382*

0.524***
0.215***
0.163***
0.116***
0.003

30.674*** 19.501***
5.507*** 5.253***
4.003*** 4.050***
2.979*** 2.915***0.047*
1.065

0.045
0.062
0.017
0.008
‐0.017‐0.013‐0.026‐0.119**‐0.061‐0.058‐0.111**‐0.068‐0.054‐0.136**‐0.088‐0.028‐0.077‐0.101*
‐0.1

0.056
0.059
0.007
0.008
‐0.017‐0.003‐0.039‐0.117**‐0.078‐0.058‐0.127**‐0.083‐0.051‐0.133**‐0.075‐0.044‐0.078‐0.111*‐0.105*

1.36
1.443
1.174
1.043
0.837
0.86
0.818
0.369**
0.583
0.625
0.379**
0.61
0.648
0.329**
0.471*
0.824
0.544
0.407*
0.433

1.371
1.341
1.046
1.013
0.879
0.952
0.758
0.402**
0.565
0.661
0.390**
0.543*
0.701
0.379**
0.587
0.757
0.596
0.439*
0.455*

Table 3. Regression results of No School Lunch Area dummy (cont.)

Area: mean age
Area: HH median % ranking of HH expenditure
Area: mean HH size
Area: mean adult height
Area: mean adult BMI
Area: mean adult #restaurant dinner
Area: mean adult #breakfast skipped
Area occupation fraction: reference group: white collar
workers
Area: fraction of laborer
Area: fraction of self-employed
Area: fraction of agriculture
Area: fraction of other occupation
Area: mean child height
Area: mean child BMI
Area: mean child #restaurant dinner
Area: mean child #breakfast skipped
Constant
N

OLS
Coefficients
Model1Model2

Logit
Odds Ratio
Model3Model40.003‐0.055‐0.0180.012‐0.023‐0.0860.095

0.002‐0.101*‐0.0040.014‐0.051‐0.142**
0.035

1.017
0.694
0.896
1.135
0.728
0.55
1.691

1.012
0.566
0.931
1.113
0.683
0.385**
1.173

0.013
0.029
‐0.091‐0.0890.008‐0.007‐0.019‐0.0020.607**
2080

0.058
0.059
‐0.056‐0.1560.027
0.009‐0.043
0.053
0.182
2080

1.011
1.261
0.335
0.407
1.053
0.897
0.926
0.903
4.496
1978

1.355
1.476
0.667
0.282
1.21
1.048
0.795
1.201
0.198*
2080

Notes: *, ** and *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.

Table 4. Average treatment effects for the full sample.

BMI

ATE
SE
p-valueN

Untrimmed
sample
0.103
0.089
0.246
17986

Overweight (POW)
Trimmedsample
0.137
0.109
0.207
11116

Untrimmed
sample
0.009
0.010
0.348
17986

Trimmedsample
0.014
0.011
0.225
11116

Overweight(International
Standard)
Untrimmed Trimmed
samplesample
0.011
0.012
0.009
0.011
0.248
0.274
17986
11116

HeightUntrimmed
sample
0.264
0.218
0.226
17986

Trimmedsample
0.280
0.255
0.272
11116

Table 5. Average treatment effects for children with low household expenditure.

BMI

ATE
SE
p-valueN

Untrimmed
sample
0.358
0.149
0.017
8953

Overweight (POW)
Trimmedsample
0.430
0.170
0.012
5552

Untrimmed
sample
0.055
0.015
0.000
8953

Trimmedsample
0.051
0.017
0.003
5552

Overweight(International
Standard)
Untrimmed Trimmed
samplesample
0.051
0.050
0.015
0.017
0.001
0.005
8953
5552

HeightUntrimmed
sample
0.013
0.346
0.970
8953

Trimmedsample
0.078
0.378
0.837
5552