The Effects of Skill-Based Health Education--- A Randomised-Controlled Intervention
in Primary Schools in Rural Bangladesh
Makiko Omura

Faculty of Economics
Meiji Gakuin University
Discussion Paper No.24-01
April 2024

The Effects of Skill-Based Health Education --- A Randomised-ControlledIntervention in Primary Schools in Rural Bangladesh
Makiko Omura*

Abstract
This paper investigates the impact of skill-based health education (SBHE) on school hygiene status,and child health and hygiene practices, and health status in rural Bangladesh. A weekly session of
SBHE was given to primary schools through a randomised-controlled trial (RCT) by locally recruited
trained para-teachers for the duration of one year. We found that SBHE exerted positive effects on
healthy practice and behavioural change at both school and child levels, including better school
hygiene infrastructure management. Improvements in cold-related symptoms were also observed
among the treated children. The analysis suggested a strong general improvement trend in healthy
practices regardless of the treatment status. Several measures of inter-school spillovers were employed,
which provided robust evidence of beneficial externalities pertaining to healthy practices at both school
and child levels. A cost-effectiveness analysis suggested that our SBHE was cost-effective, particularly
incorporating spillover effects. A cross-cutting soap provision treatment, although implemented
imperfectly, did not suggest any standalone positive health-related effects. This study is registrated in
the AEA RCT registry (No.0004265) and the ISRCTN registry (No.18002856).
* Department of Economics, Faculty of Economics, Meiji Gakuin University, 1-2-37 Shirokanedai, Minato-ku,
Tokyo 108-8636, Japan. makiko@eco.meijigakuin.ac.jp
This research project is executed in cooperation with Save the Children, Inc. and was enabled by the research grants, theJapanese Grant-in-Aid for Scientific Research (No. 23402033) from the Japan Society for the Promotion of Science andfrom Nomura Foundation, and by in-kind contribution and collaboration with Save the Children (SC), Inc. (originallySC, USA), Dhaka Office. I am particularly grateful to Mohini Venkatesh, Ikhtiar Uddin Khandaker, and Ataur Rahman,
who were at the SC at the time of intervention, Shuaib Muhammad and Ziaul Hasan of SURCH in Dhaka, as well as all
the recruited project staffs and survey staff for their dedicated work in enabling this project. My gratitude also goes to theofficials of the Bangladesh Ministry of Primary and Mass Education and the Director General of Health Services
officials, officials of the District of Jhenaidah, as well as the head teachers and all other people who enabled andsupported this project in one way or the other. Gratitude extends to all teachers, children and their families for theirvaluable contributions participating in this project. I also wish to express my deepest condolences for the premature deathof Taslima Khatun Gini, one of our field investigators in Jhenaidah, at the time of the endline survey. I am grateful to mycolleagues and various seminar and conference participants for their valuable comments. I am also thankful for theadvice provided for field experiment design by Pascaline Dupas, Rachel Glennerster, Marc Shotland, and Iqbal Dhaliwalof Abdul Latif Jameel Poverty Action Lab, MIT. All errors are our own.

1

Introduction
This research evaluates the effect and cost-effectiveness of skill-based health education (SBHE) in
inducing healthier and hygienic practices among schools and pupils, and additionally improved healthin pupils through a randomised-controlled trial (RCT) in primary schools in rural Bangladesh. In
Bangladesh, despite concerted efforts by both the government and international bodies to enhance
health outcomes among school-age children through the provision of anthelminthic drugs, nutritional
supplements, and improved sanitary infrastructure such as latrines, significant health challenges persist.
According to the Information provided by the Bangladesh Directorate General of Health Services at
the time of study [1], the major illnesses affecting school-age children were diarrhoea, micronutrient
deficiencies, malnutrition, intestinal helminths, pneumonia, acute respiratory infection, typhoid andscabies. Since then, school-based deworming programme as well as vaccination programme
progressed. Nonetheless, illnesses such as diarrhoea, malnutrition, pneumonia and acute respiratory
infection are still of concern [2]. Likewise, while current data shows some improvements in sanitationand hygiene, only 56% of schools have access to basic hand hygiene facilities, which can exacerbate
health risks [3]. This highlights the ongoing need for robust public health strategies and interventions
to secure the well-being of Bangladesh's children.
While the major interventions for child health improvements in such context is goods provision, ourprimary purpose is to examine the effectiveness of school-based SBHE focusing on skill acquisitionand habit development by integrating skill-building sessions in teaching, because the effects ofacquired skill and habit can be sustained without perpetual external inputs. The SBHE aims to improve
the health-related school environment, and children’s health and health-related knowledge, attitudes,
practices, and behaviour (KAPB). Healthy school environment and habit imply that school hygiene
status is better maintained. One of the major reasons for the lack of hygiene infrastructure indeveloping countries beyond limited budget is its inadequate management and resulting short lifetime
after its provision. The current situation in Bangladeshi schools suggests that the already poorly

2available infrastructural resources are poorly managed and not fully utilised – newly built latrines
become unusable and discarded after several years. Latrines are often locked and/or in bad conditions,and soaps are unavailable or available only at the teachers’ room upon request. A detailed case study
in primary schools in Niger by Tomokawa, Kasai, and Kobayashi (2005) also highlights this problem.
Changing default behaviour towards healthy and hygienic one can not only contribute to better
infrastructure maintenance but would be much less costly than building a new infrastructure, andrealisable in a shorter time with possibly life-long effects. A cluster-RCT focused on enhancing theupkeep of shared toilets in Dhaka’s urban slums demonstrated that promoting behaviour change alongwith the provision of low-cost simple goods resulted in improved maintenance of those hygiene
facilities [5]. Healthy habits can reinforce any other positive impacts of supply-based interventions,even inducing demand and habits for infrastructure maintenance activities.
Although the importance of school health education is generally recognised [6], relatively few
rigorous, comprehensive evaluation has been conducted possibly due to difficulties in measuring its
impact. Also, relatively less attention has been paid to health of primary school-aged children
compared to pre-school children or adolescents [7]. Among the few evaluation studies, healtheducation was often found to be ineffective in comparison to provision of drugs and/or supplements
which was relatively cheap with its benefit materialising in a shorter time-period [8–10]. Nonetheless,there have been some evidence of positive health education focusing on specific theme, such as oral
hygiene education [11,12] and handwashing [13,14]. The SBHE differs from typical lecture-based
health education as it focuses on the development of context-relevant knowledge, attitudes and skills
in children using a variety of participatory learning methods. An SBHE study conducted in Tanzania
showed significant behavioural impact such as reduced sugary food consumption, more frequent teethbrushing and improved brushing-skills compared to those who received conventional oral healtheducation [15]. A randomised pairwise evaluation of SBHE regarding schistosomiasis in Egyptian
primary schools found significant improvements not only in knowledge and attitudes but also in

3

schistosomiasis infection [16]. Although their findings cannot be taken at face value because of thestudy’s less rigorous experimental design, the SBHE approach is suggestive. A clustered-RCTintervention of weekly handwashing promotion targeting households in a Karachi squatter settlementin Pakistan, together with soap provision, resulted in major reductions in the incidence of pneumonia,
diarrhoea, and impetigo [13]. Another clustered-RCT of respiratory hygiene education and handsanitizer supply showed successful reduction in influenza among schoolchildren in urban Bangladesh[17]. A study comparing environmental nudging and intensive hygiene education in 20 Bangladeshi
primary schools showed that both methods improved child handwashing behaviour, although theprovision of hand-washing stations alone had no positive effect [14]. Education projects on HIV/AIDS
prevention in both developed and developing countries offered evidence of official educational
curriculum to be ineffective in contrast of simple but relevant information provision being effective[18,19]. They suggest that context-relevant information and practical skills can be the key elements ofsuccessful SBHE, for which school can be an effective setting as it allows targeting many pupils at thesame time inducing economies of scale and peer effects.
While successful health education intervention cited above often provided goods and/or
infrastructural supply along with education, our study employing cross-cutting intervention of SBHE
and soap provision intends to assess the impact of health education, with/without goods provision.
Healthy skills are introduced and practiced as a part of school curriculum. The reason for intervening
with school children is not only to give attention to those age group whose heath is paid relatively lessattention but also in expectation that they are more amenable to changes in habits. Children are
expected to form healthy habits through regular SBHE sessions at school with visual aid and repeatedlearning-by-doing together with classmates, receiving context-relevant advice. The importance ofrepeated learning is attested by [12]. While a child as an individual may feel it cumbersome in the
beginning to wash hands, wear shoes, dispose waste in a waste box or clean their classroom, etc., they
are induced to repeat these actions which can become habitual. Beneficial peer-effects are also4expected among the schoolmates as has been suggested by different works [20–24], although evidence
for negative peer-effects also exist [25,26]. Despite habit and behavioural inertia resistant to change,
psychological research has shown that habit formation could be achieved in two to three months
through simple and sustainable behavioural advice [27]. While habituation of elaborate actions suchas physical exercise is believed to require overcoming time inconsistency, where one’s short-run
actions are perceived as suboptimal from one’s long-run perspective [28,29], simpler action such as
washing hands or putting on a seatbelt can be automated with a lesser degree of time inconsistency.
This is because the action’s immediate cost is relatively low, and the realisation of its benefit, at leastin part, is also immediate. A short time lag can also contribute to action exertion since the net present
value of the future benefit is not heavily discounted relative to the present cost. Thus, habituation or
automaticity is faster and stronger for simple actions [27,30,31]. Through habit, the behaviour becomes‘second nature,’ which is cognitively efficient and does not require conscious effort. Thus, making
healthy behaviour habitual is the crucial aim of SBHE.
In addition to assessing the effect of SBHE, we conduct a cost-effectiveness analysis. The authors
are aware of limited study, such as one by [9], which explicitly evaluates the cost-effectiveness ofhealth education, as most studies do not offer cost analysis. In this regard, our claim that school-based
SBHE and the use of para-teacher to be cost-effective vis-à-vis regular-type of health education is
examined to a limited extent. The rest of the paper describes the project and intervention design, data,
analysis with empirical model and estimation results with additional spillover considerations and costeffectiveness analysis. In the conclusion, we summarise and succinctly discuss the findings from thestudy.

5

Methods
Project and intervention design
The project was conducted in Jhenaidah District (zila), which was a target district of Save theChildren (SC)’s PROTEEVA (Promoting Talent Through Early Education) project. However, ourintervention specifically took place in two non-PROTEEVA sub-districts (upazila), Moheshpur andKodchandpur, spanning areas of 21.16km2 and 20.16km2, respectively. These neighbouring subdistricts were chosen due to their need logistical convenience, given the existing SC offices and local
NGO partnerships in the region. To prevent any crossover effects, our project did not involve any
PROTEEVA-targeted schools/communities. The intervention was executed for over 12 months from
1 March 2012 to 25 March 2013, preceded by a four-month period of baseline data collection for which
recruitment commenced on 13 October 2011 and concluded on 29 November 2011. The endline datacollection was conducted for five months after the project's completion, from 7 April 2013 to 6 July
2013, complemented by further follow-up and data verification processes.
The proposed project applied a treatment-control pre-post evaluation based on a cross-cutting
randomisation design of SBHE (HE) and a soap-provision (SP) intervention. The unit of intervention
was school, and 180 randomly chosen schools out of total of 204 primary schools were stratified
according to the school type—government primary school (GPS) and registered non-government
primary school (RNGPS). A cross-cutting HE- SP treatments were then randomly assigned to 180
schools stratified by two school-type stratified. Thus, four groups (HE, SP, HESP, and control) with 45
school each were randomly chosen. In Bangladesh, the main types of schools GPS and RNGPS which,despite the name, are actually government-funded, although they receive only a fraction of government
funds received by GPS. Thus, RNGPS are disadvantaged in terms of resources and school
infrastructure. Therefore, it is important to stratify by school type. Half of the HE-treatment and control
schools was assigned to an additional SP-treatment for one year, where six soap bars were monthly
given to SP-treatment schools and three soap bars to randomly chosen children within the schools. The
6

SP-treatment was implemented to assess whether goods provision per se or interactively with HE could
induce healthy behaviour, in this case, handwashing. For SP-treatment, no health advice was provided
but schools and pupils were assigned to receive six and three small soap bars monthly, respectively.
The SP-treatment had another role in mitigating possible Hawthorne effects, especially given our
primary objective of discerning the impact of SBHE. Hawthorne and John Henry effects are changes
in the behaviour of HE-treatment and control groups, respectively, due to the sheer fact of evaluation
taking place. In order to mitigate John Henry effects among the pure controls, the project provided
game boards to the control schools, which did not affect the measurability of our intervention. Anotherway to amend these effects would be to collect data for some time, especially after the completion offormal evaluation, but that was outside the scope of this project.
The project was approved and supported by the Bangladesh Ministry of Education, Directorate ofPrimary and Mass Education in Dhaka, the Directorate of General of Health Services, and the District
Primary Education Officer in Jhenaidah, Upazila Education Officer the schools in the Moheshpur andKodchandpur upazilas. The project was designed to provide schools and school children with needed
health education and no particular health or physical risk was envisaged. This study obtained an ethicalapproval by the Research Integrity Review Board of Meiji Gakuin University. The field experiment
was registered with the American Economic Association’s registry for randomised controlled trials(AEARCTR-0004265) and the ISRCTN registry (ISRCTN-18002856). The registrations were done
retrospectively given the fact that AEARCTR was not yet established at the time of initial participant
recruitment, and that health education intervention was not considered to be a clinical trial by theauthors at the time of initial participant recruitment concerning the ISRCTN registry. We adhered toConsolidated Standards of Reporting Trials guidelines for reporting results of cluster randomised
controlled trials (see Supporting Information S1 File for CONSORT checklist [32]).

7

Skill-Based Health Education Strategies
SBHE projects are often considered expensive, labour intensive, and requiring long-term
involvement, which many practitioners find unsustainable and difficult to replicate to scale, while theirimpact is hard to discern. The project was thus designed with the following key features: (1) weekly
health education sessions using participatory learning methods to promote habit-formation; (2) use ofpara-teachers instead of primary school teachers as the SBHE facilitators, given their costeffectiveness and the incentive problems expected among the incumbent teachers; and (3) the use of amobile projector to assist pedagogy and skills learning and to motivate both pupils and teachers. The
device could be easily charged in advance when there was power supply, which was recommended
due to Bangladesh’s unreliable power supply.
The health education session consisted of 26 modules as shown in Table 1. The educational contents
utilised many images and videos relevant to the Bangladesh context, which were made appropriate forprimary school pupils, as well as learning-by-doing sessions, for handwashing, brushing teeth, saline
making, latrine usage, and latrine cleaning, aiding children to acquire health-related KAPB. Project
staff also recorded various videos, including one featuring a smartly dressed male staff member
demonstrating the correct method of cleaning a latrine. The use of these materials aimed to provide
practical guidance and to help diminish or eliminate biases against ‘dirty jobs.’ Each week, a one-hour
education session was conducted during the time slot allocated for Physical Education (PE) for each
grade. Typically, PE periods were used as free time for children to play. If playing around is assumed
to have positive health effects, which may well be the case, the estimated HE effects on health may bebiased toward zero.

Table1. 26 Skill-based Health Education Modules
SBHE modules (26)Contents
handwashing, hair and nail trimming, bathing, cloth1. personal hygiene (3)cleanliness, eye care; teeth-brushing; tooth decay8

2. sanitation (3)3. safe water (3)4. common illness (6)5. nutrition (5)6. first aid (3)7. injury prevention (3)keeping premises clean and waste management; latrine
cleaning; sanitary latrine use
safe and unsafe water; water purification and preservation;arsenic problem and prevention
fever, cold and cough, diarrhoea cause, diarrhoea prevention
and treatment, causes and demerits of worms, deworming
balanced diet, symptoms of malnutrition, vitamin A, iodine,ironburns, cuts, fractures
burns, drowning, road accidents

Para-teachers conducted five classes daily at a single school and rotated among five different schools
throughout the week, resulting in a total of 25 hours of SBHE sessions each week. They were all
recruited locally who were either recent college graduate or previous NGO worker looking for jobs.
Some of them also had previous experiences in the health/education field. There were several reasons
for recruiting them. Firstly, incumbent teachers were already overwhelmed with their existing
curriculum, and similar SC projects had faced significant moral hazards when employing theseteachers, despite offering extra payment. Meanwhile, the para-teacher strategy proved to be successful
in another SC project in Meherpur district of Bangladesh that some schools that had not been supported
by SC started to recruit para-teachers with their own initiatives and funds. Also, an RCT study
promoting better school climate/environment showed that a new, low-cost lay counsellor produced
substantially beneficial effects while a regular teacher produced no effect [33]. Secondly, younger job
seekers would be more flexible and eager to learn and teach the SBHE contents that required theapplication of skill-based teaching methods and of a mobile projector. Indeed, para-teachers consideredthat going through the SBHE training and practice improved their capacity and skills as prospective
teachers. Thirdly, it was considered sustainable cost-wise even after the project, as the cost per parateacher was 5,000BT per month, approximately 67USD, based on the actual market exchange rate atthat time, at 1USD = 74.66BDT, which was marginally lower than the minimum salary of 5,900BT fora regular GPS assistant teacher, yet manageable for the hiring schools, costing them only 1,000BT or
13.4USD per school. Their honorarium was also equivalent to what was being paid by the Meherpur

9schools stated above, thus considered sustainable even after the project completion. The para-teachers
received 40+ hours of intensive participatory-training in SBHE by BRAC health education specialists
which enabled them to deliver specialised contents. A refresher workshop was conducted in the
midway for their experience-sharing, feed-back and suggestions.
To date, no comprehensive school health education is offered for primary schools in Bangladesh.
The latest health policy states the objective to train at least one teacher per school on health issues [2],however, this kind of arrangement would unlikely be successful for the reasons stated above.

Samples Selection
In the target area, there were 103 GPS and 101 RNGPS at the time of study in 2011-2013. Onehundred eighty primary schools, stratified by school types, were randomly selected from the list of all
GPS and RNGPS schools in Moheshpur and Kodchandpur in Jhenaidah District using Excel randomclassification formula. The process was repeated until statistical nondifference of baseline school
characteristics between the groups was ensured. While the intervention was conducted at school level,
data were collected at both school and child level. Children surveyed were chosen randomly using seatplacement based on the pre-determined randomly selected seat numbers prescribed by the lead author
who was also the principal investigator. Surveyors were masked about the treatment status in bothbaseline and endline surveys. The treatment assignment was done after the baseline data collection,
thus all participants were masked about the treatment at the time of baseline survey.
Observations were made for 180 schools at both baseline and endline, and for 7,200 and 9,000 pupils
for the baseline and endline surveys, respectively. The sample size calculation at the time of thebaseline survey was based on the expected improvement of 0.15 standardised effect size in childhealth-related indicators, detected with 80% power and 5% significance level, assuming the schoollevel covariates to explain 2.5% of the variance. The standardised effect size of 0.15 was a conservative
estimate yet deemed to be a reasonable degree of improvement. The sample size calculation utilised
the intracluster correlation of 0.058, which was derived from SC’s baseline survey data in a different
10area of Bangladesh in which SC implemented a school health and nutrition project. A sample size of
34 students per schools would have been sufficient. However, to account for partial compliance and/or
attrition, which was assumed to be 20%, we collected data for 40 students in grades 1–4 at baseline
and 50 students in grades 1-5 per school at endline.
Bangladeshi primary schools are for grades 1–5. Because the sample targets were those who werein primary school during the intervention period, the baseline survey prior to the intervention was
conducted for grades 1–4 who would be in grades 2–5 at the time of intervention, and grades 3–6 at
the time of the endline survey unless they repeated or dropped out of the class. Additionally, we
collected data from ten grade 1or 2 pupils per school in the endline survey, who would have been ingrade 1 at the time of the intervention. The endline data were thus collected from grades 1–6, where
grade 1 or 2 signified those who had been pre-primary at the baseline, and grade 6 students had alreadygraduated from primary school at the endline. Replacement child data of the same sex and samebaseline class were collected for attrition at the endline.
Data collection was done by the Dhaka based survey institute SURCH who received intensive
training on the questionnaires, measurement and interview methods, subject random selection methodby the lead author and conducted a pilot survey with the authors. For school data, interviews wereconducted to headteachers, and observational data were collected with photographs. For child level
data, interviews and observational data were collected. All interviews and data collection usedstructured questionnaires. Prior to the study commencement, participation agreement was obtained
from the District Primary Education Officer and school headteachers in the meeting. Parents/ guardians
were briefed on the project details and the possibility of their children participating in the survey
conducted at the school. They were then asked to provide their consent. At the time of the child survey,
written consent was obtained from them using the child assent form. Surveyors were available to assist
in completing the forms for the participants when necessary. Children were excluded from the survey
if either they or their parents or guardians did not provide consent.

11

Statistical analysis
We primarily assess the impact of HE intervention on health and health KAPB both at the school
and child level. Since the intervention was done at the school level, only intent-to-treat (ITT) effects
can be measured at the child level. At each level, the effects of treatment are measured for family-wisesummary indices in which the outcome variables y are categorised into families of related variables,
as explained below. The family-wise mean-standardised average treatment effects are estimatedthrough seemingly unrelated regressions (SUR) that allow contemporaneous errors to be correlated[34,35]. This analysis accounts for possible cherry-picking caused by the increased likelihood offinding significant results simply due to conducting many regressions [36,37]. We provide additional
estimation results for single outcomes in the Supporting Information. Note that the fact that many of
the outcome variables y being binary does not pose any particular challenge in obtaining the averagecausal effect of a random treatment, as the average treatment effect (ATE) exhibits differences inprobabilities of y = 1 [38]. All statistical analysis was conducted using STATA 16, except for thesummary statistics of baseline characteristics which used 'tableone package' in R (ver.4.2.2).

School-level outcomes
We have three school-level outcome families (denoted here with “S”), one primary outcome family
and two secondary outcome families. The primary outcome family, (S1) school hygiene practice &maintenance, which is considered as the direct indicators of healthy skill-building and healthy
behaviour, consists of routine latrine cleaning, latrine cleaning days/week, routine classroom cleaning,classroom cleaning days/week, classroom and corridor cleanness, classroom rubbish bin provision,
latrine brush provision, rubbish treatment methods, and soap provision at pupils' handwashing place.
While half of the schools were SP-treated, the schools had no instruction nor supervision on how to12use the distributed soap. Two secondary outcome families are indirect outcomes derived from theprimary outcomes. One is (S2) school hygiene infrastructure, composed of clean accessible sealed-pit
latrines for each sex, ratio of clean-usable latrine for each sex, closeness of handwashing facility to thelatrines (within 10~11 steps), and easiness of obtaining water by younger children. While sealed-pit
latrine can be affected by its structure, particularly water-sealed latrine rather than lid-sealed latrine, it
was commonly observed that latrines were not properly water-sealed due to their ill usage and illmaintenance. The number of latrines for each sex included unisex ones. Another family is (S3)children’s schooling which comprises absence rate, dropout rate, and repetition rate at each grade. A
related manuscript by the authors [39] provides a concise analysis at the school level, differing slightly
in the outcome-family construct and including additional aggregated child outcomes at the school level.

Base empirical model
The basic construct of the empirical model is the same across school and child-level analyses. While
the ITT estimate of average treatment effect (ATE) can be estimated by a simple OLS for a perfect
RCT design, an additional vector of control variables X for jth of J schools makes the estimates oftreatment effects β more precise and accounts for chance differences between groups in the distributionof pre-randomisation traits. In the equation below, Yj is an outcome and Tj is the treatment status forschool j, and Dj is a school-type dummy which is used for sample stratification of school types,
GPS/RNGPS.
(1) 𝑌! = 𝛼 + 𝛽 ∙ 𝑇! + 𝜑 ∙ 𝐗! + 𝜃𝐷! +𝜀! .
Because of its randomised design, we may assume that random assignment of schools to treatment andcomparison groups ensures that the schools in either group are similar in all other respects except forhaving/not receiving a treatment. To ensure this, school-type stratified randomisation process was
repeated until the statistical indifference between treatment and control groups was ensured on themain variables. Nonetheless, there is always a possibility of randomisation being imperfect. Thus, a13

difference-in-differences (DID) estimation is conducted to adjust for any pre-existing differences
between treatment and comparison schools:(2) 𝑌!" = 𝛼 + 𝛽 ∙ 𝑡 ∙ 𝑇! + 𝛿 ∙ 𝑡 + 𝛾 ∙ 𝑇! + 𝜑 ∙ 𝐗!" +𝜃𝐷! + 𝜀!" ,where t is the time-period, Tj is the treatment status for school j. School-level DID can also be estimatedwith school fixed effects that control for time-constant unobservable school characteristics.

Child-level outcomes
The primary child-level outcomes concern health-related KAPB change, measured through pre-post
questionnaires, measurements, and observations. Health-related KAPB indicators are: (P1)
handwashing practice: handwashing habit index (frequency and used materials before eating, after
defecation, and after playing), frequency of handwashing with soap on each occasion, washing under
running water, and correct handwashing procedure; (P2) dental-care practice: dentalcare index(frequency); tooth-brushing frequency using brush/branch; combination of used materials such as
fingers, branch to brush, with ash, coal, powder and/or paste; (P3) other hygiene practice: shoewearing habits at school; shoe-wearing habits at home (inside latrine and in courtyard), plus P1and P2;(P4) hand cleanliness: clean hands, trimmed nails, and clean nails; (P4E) hand cleanliness + adenosine
triphosphate (ATP): ATP improvement rate comparing before and after handwashing (detailed below,
taken for 10% of child samples for the endline only due to invalid measurement in the baseline); (P5)nutrition practice: breakfast taken, and food taken for the past three days, ordered by the richness ofthe nutrition score and categorised into seven categories (none; carbohydrate (and fat); carbohydrate
and vitamins; vegetable/animal protein and vitamins; vegetable/animal protein and carbohydrate;
vegetable protein, animal protein and carbohydrate; protein, carbohydrate and vitamins); (P6)
health/hygiene knowledge: handwashing procedure; breakfast important; (P6E) health/hygiene
knowledge + extra: P6 outcomes plus putting water in latrine before defecating (endline only), oral
rehydration solution (ORS) making (endline only), and food pyramid (endline only). Although it is not14easy to measure KAPB, handwashing evaluated pupils’ demonstration of handwashing procedures.
Also, hand cleanliness was not only checked by the surveyors but also measured via ATP whichmeasures actively growing microorganisms through the light produced through their reaction with thenaturally occurring firefly enzyme luciferase using a luminometer. Although smaller figures suggest
cleaner hands, i.e., less microorganisms, there are large individual variations in the figures. Thus, tests
were conducted before and after handwashing to measure whether appropriate handwashing had been
done. The ATP improvement rate was calculated as (ATP-before – ATP-after)/ATP-before; thus, thelarger the figure the higher the improvement.
The secondary, more indirect outcomes concern child health improvements as follows: (I1)
anthropometry: height-, weight- (net of clothes) and BMI-for-age-z-score; (I2) cold-related symptoms:
cough, breathing difficulty, sore throat, fever, running nose, and congested nose at present and in thepast two-weeks; (I3) other illness: diarrhoea, stomachache, scabies in the past two-weeks, fatigue,dizziness, and appetite loss. Indicators that require two-week recall might entail recall bias, although
no difference was expected across treatment groups. Apart from disease in the past two-weeks which
was report-based, children’s health status and outer symptoms were directly checked by the surveyors.
As for additional controls, child household wealth and parents’ literacy level are considered, sincefamily wealth and education level may affect child behaviour acquisition despite a school-based
intervention directly targeting the children may mitigate the family socioeconomic influence.

Child-level empirical model
For child-level analysis, it is essential to take care of non-random differences in mean values at thebaseline for several key outcome variables. The equivalent of DID equation (2) above, adding 𝐙#!" , avector of exogenous variables for child i in school j at period t is:(3) 𝑌#!" = 𝛼 + 𝛽 ∙ 𝑡 ∙ 𝑇! + 𝛿 ∙ 𝑡 + 𝛾 ∙ 𝑇! + 𝜑 ∙ 𝐗!" + 𝜃 ∙ 𝐙#!" + 𝜃𝐷! + 𝑣#!" ,15

A composite error is vijt = cj + ωij + uijt, where cj is unobserved group heterogeneity, ωij is unobserved
child heterogeneity, and uijt is the idiosyncratic error. Additionally, given that the RCT was executedat the school level, child-level estimations need to take into account possible intracluster correlation
within school, defined as the ratio of within-cluster variance divided by total variance, which is thesum of within-cluster and between-cluster variance. The usual practice of removing the effects of
intracluster correlation (ρ) is to conduct the analysis with intervention-unit clustering, adjusting forstandard errors (SE) in regressions. With the hierarchical structure of school, time and child, we apply
the most commonly and widely used DID estimation with multilevel random-effects (RE) with clusterrobust SE (CRSE) [40–42]. However, CRSE is not a panacea [38,42] that especially with a single
baseline and follow-up, it would require twice the sample size in DID to get the same power as obtainedwith the analysis of covariance (ANCOVA) [43]. Also, in case the treatment effect extends beyond theindividual level and affects the entire cluster, removing the intracluster correlation can underestimate
the true effect. We therefore conduct SUR for family-wise outcomes with and without CRSE. For
single outcome estimation, we also apply ANCOVA estimation and its likewise alternative constrainedbaseline analysis (CBA) which are reported to be more efficient than DID-CRSE or post estimatorwhich uses only the endline data. Detailed description of the model and the estimated treatment effects
and interpretation for child-level single outcomes are given in the Supporting Information (A-Table 5).
Exogenous covariates considered in the empirical models are child sex, age, child wealth index andparents’ literacy. Child wealth index is created through iterated principal factor, reflecting housestructure materials, roof materials, number of rooms, latrine structure and materials, possession ofelectronic appliances, mobile phones and bikes. The above-cited and another study [44], which
additionally provided households with drinking water disinfection, showed that households in thesquatter settlement with lower socioeconomic status took longer—several months—to acquire ahealthy habit. On the other hand, another study [45] showed that well-specific arsenic information16

induced costly behaviour of well change, with no influence of household wealth level although withpositive influence of education level.
Given the fact that 15% of children in the endline survey were replacement of those attritted, we
conduct estimations also for the subset of children who were in both baseline and endline.

Results
Project implementation
The baseline survey was conducted before the intervention, with participants recruitment occurringfrom 13 October–29 November 2011, mostly during cooler winter season, and the endline data werecollected during 7 April–6 July 2013, mostly during hot and humid summer, after the completion of
the intervention (March 2012–March 2013). The planned intervention period was from January to
November 2012, based on the academic year in Bangladesh. However, the start of the implementation
was shifted back for a few months due to a procedural delay. The randomisation was done in January
2012 after the beginning of academic year in Bangladesh, thus the treatment status should not haveaffected the choice of school by the children and their families. Treatment randomisation ensured
statistical nondifference between treatment and control schools at school level variables. We collected
data from 180 schools, with 45 each randomly assigned for a cross-cutting HE-SP treatments. The
project profile depicting the participant flow with randomisation design is provided in Figure 1.

17

Figure 1. Study Profile with Participant Flow and the Randomisation Scheme18

All HE-treatment schools were delivered all seven SBHE modules by the para-teachers. They also
guided a cleaning rota by pupils. There were a few extra weeks as well as several school health eventsin which skill-trainings for the first two modules and illness treatment, such as ORS making, wererepeated. As part of the intervention, the treatment group pupils were encouraged to measure theirweight and height occasionally, which was expected to increase their health awareness. The SP
intervention was not implemented as planned due to delayed procurement for a few months, resulting
in irregular distribution. The SP schools were not monitored in terms of how they used the distributed
soap bars, and 12 out of 45 SP schools and 8 out of 45 HESP schools reported not receiving soap,
although the target pupils/households of those soap treatment schools were reported to have received
the soap, suggesting possible recording errors at the school level. While the comparison of HEtreatment and SP-treatment schools was anticipated to provide insight into the direct effect of SBHE,
independent of Hawthorn effects, as well as to discern the effect of mere goods provision on promoting
healthy behaviour, the imperfect implementation of the SP-treatment raised certain concerns about thevalidity of this attempt. Thus, the cross-cutting intervention results are mainly contained in the
Supporting Information, with limited interpretation here. The statistical analysis results below focus
on the HE-treatment effect.
A refresher workshop was conducted in the midway for their experience-sharing, feed-back andsuggestions by para-teachers and project staff. Some feedbacks were: difficulty in teaching micronutrients; need of additional materials to explain vitamin deficiency; need of shades/curtains for thewindow in order to use the projector; problems with locked/unfunctional latrines; use of projector
being beneficial; students liking cartoons; children asking mothers to wash hands properly; children
asking mothers to cut their nails; making toilet brush with palm tree leaves.

19Child attrition
Between the baseline and endline, the study experienced a 15% attrition rate (1,087 out of 7,200
children), falling within the anticipated 20% attrition outlined in our sample size calculation.
Specifically, both HE-treatment and HE-control groups saw attrition rates of 15.0%. While our focus
is on HE-treatment effects, attrition rates were 13.7% and 16.3% for SP-treatment and SP-control
groups, respectively, and 15.5%, 12.9%, 14.6%, and 17.1% for the HE-only, SP-only, HESP-treatment,
and control schools, respectively. This could suggest that soap provision, though imperfectly
implemented, may have affected a higher retention rate in SP-schools.
Most of the children in grade 5 during the intervention, thus grade 4 at baseline, had already finishedprimary school at the time of the endline survey, and their data were collected at the nearby highschools, while household follow-ups for those who did not go on to the neighbouring high schools
proved to be mostly unsuccessful. For students who were in grade 4 at baseline and progressed to grade
5 during the intervention, endline data was gathered from 79.5% of them. Among these, 89.3% had
transitioned to a nearby high school, while the remainder repeated the grade in primary school. The
school-wide class repetition rate at the endline among our sample children amounted to 20.2%, ofwhich 91.3% were single repetition. The repetition figure among our sample was much higher thanofficial school figures of 9.4%. Attritted children mostly had gone to other regions. We collected
replacement data for 15% or 1,078 pupils for those who attritted at the endline. The replacements wereof the same sex and the same baseline class. With the addition of those who were in grade 1 at the timeof intervention, the total effective sample size was 8,991 at the endline compared to 7,192 at thebaseline (see Figure 1).
At the child level, there were some systematic differences between attritors and non-attritors
suggested by a simple t-test of baseline characteristics. More male, older (and thus, otheranthropometric measures), and unhealthy pupils—in particular, with more cold-related symptoms in
two weeks, more diarrhoea symptoms, and more incidences of fatigue and appetite loss—attritted,20although no statistical difference was seen between healthy behaviour and practices. The reasons for
attrition could not be retrieved. Regressing attrition on the baseline variables, each interacted with HE
treatment, HE-schools had higher rate of attritors experiencing diarrhoea, while HE-control schools
had higher rate of attritor experiencing stomachache. No other statistically significant difference wasobserved between HE-treatment and HE-control schools. This suggests that no clear one direction
treatment bias is expected due to attrition. Thus, if these attrition tendencies affected the evaluation
outcome, it would be a downward bias rather than an upward bias if the treatment exerted a positive
impact, especially on less healthy and/or older pupils.

General baseline observations
Descriptive statistics for the main variables at the baseline for HE-treatment and HE-control groups
as well as cross-cutting HESP-treatment groups are provided below. Outcome variables are categorised
into families as explained above. Below we provide general observations at baseline.

School-level data
Table 2 gives descriptive statistics for the baseline school characteristics and outcomes variables
categorised in their analytic families for (1) HE-treatment groups (non-HE and HE) and for (2) crosscutting HESP-treatment groups (control, SP, HE and HESP). Although school type, GPS/RNGPS is
not specified in the table, female teacher numbers and ratios are 3.1 (1.72) and 0.58 (0.27) for GPS,and 1.01 (0.87) and 0.28 (0.25) for RNGPS, respectively (SD in parentheses). The randomisation
ensured no statistical difference for school-level outcomes, namely, (S1) school hygiene practice &maintenance, (S2) school hygiene infrastructure, and (S3) schooling, and school baselinecharacteristics, apart from female teacher ratio. There was no baseline statistical difference among the
HESP-treatment groups. In terms of (S1) school hygiene practice & maintenance, relatively low value
for routine cleaning was noticeable, being less than once a week, although there were high variabilities
21

across schools. Relatively high variabilities across schools were also suggested for clean latrine,
rubbish bin provision, and rubbish treatment. Latrine availability was an important issue in
Bangladeshi primary schools, that they were often lacking in numbers, unclean and/or locked up. For(S2) school hygiene infrastructure, the availability of usable latrines was notably low in both HEcontrol and HE-treatment schools, with an average of 0.34 and 0.39 clean, unlocked, pit-sealed latrines
for boys, and 0.34 and 0.41 for girls, respectively. Considering that most grades had half-day school
sessions, there were, on average, 37 boys and 41 girls attending school per each unlocked and clean
latrine. Regarding (S3) schooling, the absenteeism rate differed across grades, ranging from 33% in
grade 1 to 12% in grade 5, while the average number of students was highest in grade 1, with 52.7
students, and lowest in grade 5, with 31 students. Dropout rate, available except for grade 1, was nothigh but increasing in grade, from an average rate of 0.05% in grade 2 to an average rate of 0.85% in
and grade 5. Repetition rate, available except for grade 1, showed its highest value for grade 3 with its
mean value at 9.5%, while it is the lowest for grade 5 at 1.1%.

Table 2. Summary Statistics of Baseline School Characteristics by the HE-treatment and the
Cross-cutting HESP-treatment
HE-intervention
nonHE
HE
schoolschoolnumber of schools
total number of enrolled pupils
total number of attended pupils
ratio of female pupils enrolled
ratio of female pupils attended
ratio of female teachers

Mean
(SD)

Mean
(SD)

90
232.4
(95.7)
164.9(73.3)
0.51(0.04)
0.53(0.05)
0.48(0.31)

90
220.8(97.5)
159.8(71.2)
0.50(0.05)
0.53(0.05)
0.38(0.28)

0.37(0.48)

0.38(0.49)

Cross-cutting HESP-intervention

p-value
(a)

0.420
0.640
0.525
0.529
0.026

controlschoolSPschool

HEschool

HESPschool

Mean
(SD)

Mean
(SD)

Mean
(SD)

Mean
(SD)

45
229.2
(89.0)
157.4(68.0)
0.50(0.04)
0.53(0.05)
0.49(0.31)

45
235.6(102.9)172.4(78.2)
0.51(0.04)
0.53(0.06)
0.47(0.31)

45
219.9(108.3)157.4(77.8)
0.50(0.04)
0.52(0.04)
0.37(0.25)

45
221.7(86.5)
162.2(64.7)
0.51(0.05)
0.54(0.06)
0.39(0.31)

0.38(0.49)

0.36(0.48)

0.40(0.50)

0.36(0.48)

pvalue
(a)

0.860
0.730
0.453
0.357
0.163

Primary Outcomes by Families
S1: school hygiene & maintenanceroutine latrine cleaning

22

0.878

0.968

latrine cleaning days/week
routine classroom cleaningclassroom cleaning days/weekclassroom rubbish bin provision
latrine brush provisionrubbish treatment(b)clean classrooms
soap for pupil at handwashing facility
Secondary Outcomes by Families
S2: school hygiene infrastructureusable unlocked clean pit-sealed
latrine for boys(c)usable unlocked clean pit-sealed
latrine for girls(c)ratio of clean-usable latrines forboys(c)ratio of clean-usable latrines forgirls(c)school hand washing facility near
latrineyoungest children can get water bythemselves
S3: schooling
grade1 absence rate
grade2 absence rate
grade3 absence rate
grade4 absence rate
grade5 absence rate
grade2 dropout rate
grade3 dropout rate
grade4 dropout rate
grade5 dropout rate
grade2 repetition rate
grade3 repetition rate
grade4 repetition rate
grade5 repetition rate

0.61
(1.09)
0.80(0.40)
3.48(2.49)
0.29(0.71)
0.63(0.49)
0.40(0.86)
1.19(0.89)
0.46(0.80)

0.66(1.17)
0.72(0.45)
2.98(2.59)
0.20(0.60)
0.61(0.49)
0.42(0.86)
1.00(0.91)
0.40(0.75)

0.39(0.63)
0.41(0.65)
0.47(0.48)
0.49(0.48)
0.68(0.47)
0.62(0.49)

0.34(0.56)
0.34(0.62)
0.51(0.47)
0.46(0.47)
0.71(0.46)
0.63(0.48)

0.35(0.18)
0.30(0.18)
0.30(0.18)
0.30(0.17)
0.12(0.12)
0.07(0.45)
0.16(1.10)
0.45(1.52)
0.80(2.47)
6.62(8.71)
9.54(10.13)9.37(13.23)0.94(2.82)

0.31(0.17)
0.28(0.15)
0.31(0.16)
0.29(0.15)
0.12(0.14)
0.04(0.33)
0.12(0.65)
0.40(1.39)
0.89(2.42)
6.00(7.26)
9.40(9.74)
8.80(10.68)1.21(2.71)

23

0.792
0.223
0.188
0.365
0.804
0.863
0.160
0.630

0.619
0.483
0.567
0.675
0.63
0.878
0.135
0.577
0.964
0.415
0.852
0.666
0.739
0.82
0.805
0.606
0.923
0.753
0.512

0.58
(0.92)
0.84(0.37)
3.47(2.48)
0.31(0.73)
0.57(0.50)
0.40(0.89)
1.07(0.91)
0.44(0.78)

0.64(1.25)
0.76(0.43)
3.49(2.53)
0.27(0.69)
0.69(0.47)
0.40(0.84)
1.31(0.85)
0.47(0.81)

0.73(1.27)
0.82(0.39)
3.24(2.46)
0.22(0.64)
0.69(0.47)
0.49(0.94)
0.96(0.90)
0.47(0.81)

0.58(1.08)
0.62(0.49)
2.71(2.71)
0.18(0.58)
0.53(0.50)
0.36(0.77)
1.04(0.93)
0.33(0.67)

0.38(0.53)
0.40(0.58)
0.51(0.47)
0.52(0.48)
0.76(0.43)
0.56(0.50)

0.40(0.72)
0.42(0.72)
0.43(0.49)
0.46(0.49)
0.60(0.50)
0.69(0.47)

0.27(0.54)
0.31(0.60)
0.49(0.47)
0.44(0.47)
0.64(0.48)
0.67(0.48)

0.42(0.58)
0.38(0.65)
0.53(0.47)
0.48(0.47)
0.78(0.42)
0.60(0.50)

0.38
0.32(0.20) (0.16)
0.33
0.27
(0.21) (0.14)
0.33
0.27
(0.20) (0.16)
0.31
0.29
(0.17) (0.16)
0.12
0.13
(0.12) (0.12)
0.00
0.14
(0.00) (0.63)
0.15
0.18
(0.99) (1.20)
0.58
0.32
(1.71) (1.31)
0.85
0.76
(2.94) (1.93)
7.52
5.71
(10.15) (6.98)
10.06
9.03
(9.56) (10.76)
10.26
8.48
(11.18) (15.07)
0.72
1.16
(2.34) (3.23)

0.32(0.17)
0.31(0.16)
0.31(0.15)
0.29(0.15)
0.15(0.16)
0.00(0.00)
0.00(0.00)
0.51(1.71)
1.00(2.66)
6.55(7.77)
8.56(9.98)
8.01(9.97)
0.77(1.78)

0.30(0.17)
0.26(0.15)
0.30(0.17)
0.28(0.14)
0.09(0.11)
0.08(0.46)
0.24(0.91)
0.30(0.99)
0.79(2.17)
5.45(6.77)
10.23(9.53)
9.60(11.40)1.66(3.36)

0.903
0.059
0.439
0.797
0.294
0.904
0.279
0.821

0.617
0.859
0.764
0.87
0.199
0.545
0.154
0.127
0.411
0.79
0.323
0.276
0.64
0.74
0.966
0.608
0.83
0.806
0.343

Notes: (a) t-test for numerical variables and chi-square test for categorical variables; (b) ranging from 0 none, 1 dump inriver/pond, 2 dump in another land, 3 burn/burry in school; (c) includes unisex toilets.

Child-level data
For child-level variables in Table 3, base characteristics shown are child sex, age, child wealth index
and parents’ literacy. While randomisation assured statistical indifference across school-levelvariables, it was not possible to do so across various child-level variables. Since we focus on HEtreatment, those variables with statistical difference at the 5% statistical significance level among theHE-treatment groups are indicated with an asterisk in the p-value column. Such non-random baseline
differences are dealt by applying the DID estimator. For the primary child-level outcomes concerning
health-related KAPB change, non-HE (control) group exhibited higher mean values for most outcomesin the (P1) handwashing practice family. Nonetheless, one of the outcomes in the (P4) handcleanliness, clean hands, which could be indicative of their proper handwashing practice was more
frequent in the HE-children. Among other healthy KAPB outcomes, shoes/footwear wearing at schoolin (P3) overall hygiene had higher mean value for non-HE children, while HE children have slightly
higher mean scores for (P5) nutrition practice.

Table 3. Summary Statistics of Baseline Child Characteristics by the HE-treatment and the
Cross-cutting HESP-treatment
HE-intervention

number of surveyed childrensex (male:1; female:2)age (year)
child (family) wealth index(b)parents’ literacy (read)parents’ literacy (write)

non-HE
schoolHE
school

Mean
(SD)

Mean
(SD)

3,594
9.15
(1.59)
9.15(1.59)-0.09(0.87)
1.03(0.85)
1.02(0.85)

3,598
9.19(1.60)
9.19(1.60)-0.13(0.83)
1.02(0.86)
1.01(0.86)

Cross-cutting HESP-intervention
controSPHEHESPl
school school school
schoolp-value
(a)

0.369
0.369
0.056
0.688
0.508

Primary Outcomes by Families

24

Mean
(SD)

Mean
(SD)

Mean
(SD)

Mean
(SD)

1,797
9.07
(1.58)
9.07(1.58)-0.06(0.91)
1.05(0.84)
1.04(0.84)

1,797
9.24(1.61)
9.24(1.61)-0.13(0.84)
1.02(0.86)
1.01(0.86)

1,799
9.21(1.60)
9.21(1.60)-0.14(0.82)
1.05(0.86)
1.04(0.86)

1,799
9.17(1.59)
9.17(1.59)-0.13(0.83)
1.00(0.86)
0.98(0.86)

pvalue
(a)

0.437
0.007
0.037
0.252
0.113

P1: handwashing practice
handwashing with soap before
eatinghandwashing with soap after
defecation
handwashing with soap after
playing
handwashing index before eating(c)handwashing index after
defecation(c)handwashing index after playing(c)handwashing under running water
handwashing procedures(d)0.19(0.39)
0.65(0.48)
0.09(0.29)
3.17(1.49)
4.79(1.31)
2.25(1.46)
0.99(0.22)
1.41(0.80)

0.14(0.35)
0.62(0.48)
0.07(0.25)
3.06(1.36)
4.72(1.30)
2.27(1.37)
0.97(0.22)
1.38(0.85)

9.30(5.04)
1.49(0.97)
4.53(2.25)

9.17(4.78)
1.47(0.93)
4.56(2.24)

0.88(0.33)
2.97(1.11)

0.85(0.35)
2.92(1.14)

0.48(0.50)
0.79(0.92)
0.57(0.85)

0.51(0.50)
0.81(0.93)
0.58(0.85)

1.89(0.34)
1.54(0.53)
1.93(0.26)

1.91(0.30)
1.57(0.52)
1.94(0.23)

0.59(0.49)
1.41(0.80)

0.54(0.50)
1.38(0.85)-1.58(1.23)-0.84(1.24)-1.56(1.06)-1.57(1.20)-0.83(1.23)-1.55(1.02)<0.001*0.03*<0.001*0.002*
0.031*
0.4
0.011*
0.101

0.20(0.40)
0.65(0.48)
0.10(0.30)
3.15(1.52)
4.74(1.33)
2.20(1.47)
0.98(0.22)
1.43(0.79)

0.19(0.39)
0.65(0.48)
0.08(0.27)
3.19(1.45)
4.84(1.29)
2.29(1.44)
0.99(0.21)
1.40(0.80)

0.14(0.35)
0.63(0.48)
0.06(0.23)
3.10(1.31)
4.73(1.28)
2.38(1.32)
0.97(0.23)
1.42(0.85)

0.15(0.36)
0.62(0.48)
0.08(0.27)
3.03(1.40)
4.71(1.33)
2.17(1.40)
0.98(0.21)
1.34(0.84)

9.52(5.03)
1.54(0.95)
4.63(2.20)

9.08(5.05)
1.45(0.97)
4.44(2.30)

8.97(4.81)
1.43(0.94)
4.46(2.28)

9.36(4.74)
1.50(0.92)
4.65(2.20)

0.87(0.34)
2.97(1.13)

0.89(0.32)
2.98(1.09)

0.85(0.36)
2.89(1.16)

0.86(0.34)
2.96(1.13)

0.46(0.50)
0.82(0.93)
0.59(0.86)

0.50(0.50)
0.76(0.91)
0.54(0.83)

0.51(0.50)
0.84(0.94)
0.59(0.86)

0.50(0.50)
0.78(0.92)
0.58(0.85)

1.89(0.34)
1.55(0.53)
1.93(0.25)

1.89(0.35)
1.53(0.53)
1.92(0.27)

1.92(0.29)
1.60(0.51)
1.94(0.24)

1.90(0.31)
1.55(0.52)
1.95(0.22)

0.57(0.49)
1.43(0.79)

0.60(0.49)
1.40(0.80)

0.54(0.50)
1.42(0.85)

0.54(0.50)
1.34(0.84)-1.51(1.23)-0.76(1.26)-1.53(1.08)-1.66(1.22)-0.92(1.22)-1.59(1.04)-1.57(1.23)-0.86(1.22)-1.52(1.05)-1.57(1.17)-0.79(1.24)-1.58(0.98)<0.0010.19<0.0010.006
0.016<0.001
0.033
0.015

P2: dental care practicedentalcare index(e)tooth brushing frequency
dental care materials(f)

0.26
0.214
0.683

0.002
0.002
0.007

P3: overall hygiene practice (+P1~P3)
wearing shoes at school
wearing shoes at home (latrine +yard)
P4: hand cleanlinessclean hands
trimmed nails
clean nails

0.002*
0.056

0.02*
0.333
0.338

0.002
0.078

0.014
0.033
0.234

P5: nutrition practice
breakfast habit
breakfast eaten today(g)food taken in 3 days (g)0.008*
0.017*
0.004*

0.022
0.001
0.015

P6: health knowledge
breakfast important
handwashing procedures<0.001*
0.101<0.001
0.015

Secondary Outcomes by Families
I1: anthropometry (h)height z-scoreweight z-score (net of clothes)
BMI z-score

0.632
0.633
0.706

I2: cold-related symptoms

25

0.004
<0.0010.074

coughbreathing difficultysore throat
fever
running nose
congested nose
cough within 2 weeks
breathing difficulty within 2 weeks
sore throat within 2 weeks
fever within 2 weeks
running nose within 2 weeks
congested nose within 2 weeks

0.22(0.42)
0.01(0.10)
0.01(0.07)
0.07(0.25)
0.31(0.46)
0.05(0.22)
0.38(0.49)
0.02(0.14)
0.02(0.13)
0.25(0.43)
0.48(0.50)
0.09(0.29)

0.23(0.42)
0.01(0.11)
0.01(0.08)
0.08(0.27)
0.32(0.47)
0.04(0.21)
0.41(0.49)
0.03(0.16)
0.02(0.15)
0.26(0.44)
0.51(0.50)
0.10(0.29)

0.15(0.36)
0.36(0.48)
0.14(0.35)

0.16(0.36)
0.37(0.48)
0.13(0.34)

0.533
0.394
0.297
0.041*
0.142
0.238
0.027*
0.114
0.094
0.331
0.002*
0.5

0.23(0.42)
0.01(0.10)
0.01(0.09)
0.07(0.25)
0.30(0.46)
0.05(0.22)
0.39(0.49)
0.02(0.15)
0.02(0.14)
0.23(0.42)
0.47(0.50)
0.10(0.30)

0.22(0.41)
0.01(0.11)
0.00(0.05)
0.07(0.26)
0.31(0.46)
0.05(0.21)
0.38(0.49)
0.02(0.12)
0.02(0.12)
0.27(0.44)
0.48(0.50)
0.08(0.27)

0.22(0.42)
0.01(0.10)
0.01(0.07)
0.08(0.28)
0.32(0.47)
0.04(0.20)
0.41(0.49)
0.02(0.14)
0.02(0.13)
0.27(0.44)
0.52(0.50)
0.09(0.28)

0.23(0.42)
0.02(0.13)
0.01(0.10)
0.08(0.27)
0.32(0.47)
0.05(0.21)
0.41(0.49)
0.03(0.17)
0.03(0.17)
0.25(0.43)
0.51(0.50)
0.10(0.31)

0.15(0.36)
0.40(0.49)
0.15(0.36)

0.15(0.36)
0.33(0.47)
0.14(0.34)

0.17(0.37)
0.37(0.48)
0.12(0.33)

0.15(0.36)
0.36(0.48)
0.14(0.34)

0.685
0.248
0.056
0.17
0.464
0.559
0.176
0.029
0.008
0.011
0.014
0.059

I3: infectious disease (+I2)
diarrhoea within 2 weeks
stomachache within 2 weeks
scabies

0.527
0.765
0.12

0.46
<0.0010.185

I4: overall health (+I2, I3)
0.33
0.31
0.34
0.31
0.31
0.31
0.09
0.161
(0.47)(0.46)(0.47) (0.46) (0.46) (0.46)
0.36
0.35
0.36
0.35
0.35
0.34
dizziness
0.335
0.398
(0.48)(0.48)(0.48) (0.48) (0.48) (0.47)
0.27
0.27
0.27
0.27
0.26
0.27
appetite loss
0.617
0.66
(0.44)(0.44)(0.44) (0.45) (0.44) (0.45)
Notes: (a) t-test for numerical variables and chi-square test for categorical variables; (b) child family wealth index iscreated through iterated principal factor, reflecting house structure materials, roof materials, number of rooms, latrine
structure and materials, possession of electronic appliances, phones and bikes; (c) index reflects handwashing frequencies
on each occasion and usage of soap, ash, mud, only water on each occasion, ordered [0, 6]; (d) correct handwashingprocedure [0, 8]; (e) index reflects frequency of brushing (never, sometimes, always; once a day, twice a day) and use of
brush/branch finger, use of toothpaste, ash, or nothing; (f) dental care material combinations (fingers, branch to brush,with ash, coal, powder and/or paste) ordered [0, 6]; (g) ordered by the richness of the nutrition score (none; carbohydrate(and fat); carbohydrate and vitamins; vegetable/animal protein and vitamins; vegetable/animal protein and carbohydrate;
vegetable protein, animal protein and carbohydrate; protein, carbohydrate and vitamins); (h) 5, 2, 14 outliers omitted forweight, height, BMI anthropometric indicator, respectively; significant differences between no-HE and HE schools foundby regressing them on the HE dummy are noted with asterisk (* p<0.05, ** p<0.01, *** p<0.001).
fatigue

As for the health-related outcomes, (I1) anthropometric measurements all showed negative values,
indicating Bangladeshi children's below international-mean values on average by a standard deviation(SD) of 1.58, 0.84 and 1.56, respectively. Note that recording accurate child age was not astraightforward task in developing countries, where records were often unavailable. The uncertainty
26

in determining an accurate age posed difficulty when calculating children’s anthropometric z-scores.
Four pieces of age information were gathered in the following order of priority: (1) birth certificate;(2) vaccination card; (3) mother’s memory; (4) school register; (5) child’s memory. In case of no
official child-age record and sizable discrepancies among the figures in (3)~(5), height/weightmeasurements were also taken into accounts in determining the most probable age information. There
were 79 cases for which no child age data was available, which were all at the baseline. Among thebaseline and endline household survey data, accounting for 2,159 and 2,698 valid observations,
respectively, 61% and 71% possessed either child’s birth certificates or immunization cards; however,about 7% of those who possessed the certificate still could not remember the birthdate of the childcorrectly. Given the fact that WHO Growth Chart for weight was only available for 5-10 years, whilethat for height and BMI was available for 5-19 years, the British 1990 Growth Charts available for 020 years were used to calculate the z-scores. There were 21 flagged outlier z-scores, set at +-6SD for
weight and -6SD~+5SD for height and BMI. The standard flag criteria for the British formula was set
at +-5SD, however, given the general thinness of Bangladeshi children and the majority of outliers
having weights between -6SD~-5SD, it was set as +-6SD. There were 5 cases of weight, 2 cases ofheight, and 14 cases of BMI z-score outliers.
In terms of (I2) cold-related symptoms, 35% pupils reported having at least one symptom presently.
Outer symptoms such as cough, running/congested nose, fever, beathing difficulty, sore throat andscabies were verified by the enumerators at the time of interview. Among cold-related symptoms,
cough and running nose were most common, seen in about 20~30% plus children. These symptoms in
the past two-weeks were reported by about 40~50% of the children, and fever by about 25%. In termsof other symptoms in the past two-weeks (2w), diarrhoea, stomachache, scabies, fatigue, dizzinessand appetite loss were experienced by 15.5%, 36.4%, 13.6%, 31.7%, 35.1% and 26.8% of children,
respectively. Girls showed higher prevalence of fatigue, dizziness and appetite loss than boys withstatistical significance at 0.1% or 1%. These observations seemed to suggest relatively unhealthy27

children on average. Among health symptoms, fever, cough (2w) and running nose (2w) prevalence
were higher among the HE school children with statistical significance. The average temperature in
Dhaka was 24 and 29 Celsius at the baseline and endline, respectively. Temperature-wise, cold
symptoms were expected to be more prevalent in the baseline survey, although a period dummy would
take care of such period-wise differences. While the intracluster correlation ρ was assumed to be 0.058
at the time of sampling calculation, ρ for some outcome variables, such as infectious disease symptoms,
was higher than the assumed figure.

Analysis results and interpretation: SBHE effects
School-level estimation results
Table 4 gives the average-effect estimates for each of family-wise outcome for the HE-intervention
using the DID estimator. Only the primary outcome related to hygiene practices had positive HEtreatment effect with statistical significance at the 1% level. The effect of HE-treatment on (S1) school
hygiene practice & maintenance was 0.36 [0.17–0.55] (p<0.001). The result was robust for the
inclusion of additional control, female teacher ratio. The negative significant coefficient of school typein primary outcome estimations suggests that GPS, which were better funded and generally larger inscale than RNGPS, had higher level of school hygiene practice. No observed HE-treatment effect on
the secondary outcomes could well be that overall hygiene infrastructure did not significantly change
since no infrastructural intervention was provided, and as for schooling, the official record of schooling(absence, dropout and repetition, as well as the admitted number of pupils) might not have reflected
the real situation. As a piece of evidence, the repetition figures provided by the school records was far
less than what we found in our randomly chosen surveyed pupils. The total repetition rate in the endline
reported by the schools was 9% for all grade 1~5, while it was 20% for our child data. The period
effect was pronounced with high positive effects (i.e., negative β-coefficient for illness) with statistical28

significance except for schooling. Statistically significant positive effects of period suggest that
schools, regardless of treatment status, improved in these outcomes at the endline since the baseline.

Table 4. School-level analysis results: family-wise mean-standardised HE-treatment effect on
five outcome families (N=360)
primary outcomes

secondary outcomes(S1) school hygiene
practice & maintenance(S2) hygiene
infrastructure(S3) schooling

β-coefficient
β-coefficient
β-coefficient[95%CI] [p-value][95%CI] [p-value][95%CI] [p-value]
HE-treatment
0.37***
0.37***
-0.02
-0.02
0.09
0.09
[0.17,0.57][0.17,0.57][-0.27,0.24] [-0.27,0.23][-0.07,0.25] [-0.07,0.25][0.000][0.000][0.905][0.898][0.259][0.260]HE-group
-0.08
-0.08
-0.01
0.02
-0.04
-0.05
[-0.24,0.08][-0.24,0.08][-0.20,0.19] [-0.18,0.21][-0.14,0.06] [-0.15,0.06][0.349][0.340][0.954][0.880][0.385][0.392]
period0.60***
0.60***
0.53***
0.53***-0.06-0.06[0.47,0.74][0.47,0.74][0.34,0.71][0.34,0.71][-0.19,0.06] [-0.19,0.06][0.000][0.000][0.000][0.000][0.338][0.338]school type-0.18***-0.19**
-0.04
0.02
0.07+
0.07
[-0.28,-0.08] [-0.30,-0.07][-0.17,0.08] [-0.13,0.16][-0.01,0.15] [-0.02,0.16][0.000][0.002][0.493][0.842][0.075][0.125]female
-0.01
0.21
-0.01
teacher ratio[-0.22,0.19][-0.06,0.47][-0.18,0.15][0.896][0.127][0.897]
N
360
360
360
360
360
360
Note: Each column represents a separate regression on a family of outcomes estimated by seemingly unrelated
regressions (SUR), estimated by a feasible generalised least squares (FGLS) estimator. β-coefficient is the mean
standardised effect from SUR applying the difference-in-differences (DID) model, controlling for school type. An
additional covariate is female teacher ratio. Outcome family compositions are: (S1) school hygiene practice: latrine
cleaning rota, latrine cleaning days per week, classroom cleaning rota, classroom cleaning days per week,classrooms/corridors cleanness, rubbish bin provision, latrine brush provision, rubbish disposal method, soap
provision at pupils’ handwashing facility; (S2) school hygiene infrastructure: clean accessible pit-sealed latrines
for boys; clean accessible pit-sealed latrines for girls, ratio of clean-usable latrine for boys, ratio of clean-usable
latrine for girls, closeness of handwashing facility to the latrines (within 10~11 steps), easiness of obtaining waterby younger children; (S3) schooling: absence rate for each grade1~5, repetition rate and dropout rate for each grade
2~5. Significance level: + p<0.1, * p<0.05, ** p<0.01, ***p<0.001; 95% confidence interval and p-value in brackets.
360 samples are 90 HE-schools and 90 control-schools in the baseline and endline.

As mentioned above, the results of HESP-intervention are given in the A-Table 1. To briefly
summarise the equivalent estimates of cross-cutting HESP-treatments, the lone-HE-treatment had noeffect on any outcome, while the HESP-treatment exerted positive effect on school hygiene practiceand maintenance with similar magnitude to that of HE-treatment in Table 4 at the 1% significancelevel. No lone-SP-treatment effect was observed. The estimated HE-treatment effects for single
school-level outcomes are given in the A-Table 2.

29

Child-level estimation results
The main results of average-effects model are presented in Table 5 for all children (all) and thesubset of children who were in both baseline and endline surveys (in both). As in the school-level
results, statistically significant positive HE-treatment effects were observed for primary outcomes
related to hygiene practice and behaviour, namely, (P1) handwashing by 0.21 [0.12–0.30] (p<0.001),(P2) dentalcare by 0.17 [0.08–0.26], (p=0.002), (P3) overall hygiene by 0.22 [0.14–0.31] (p<0.001),(P6) health/hygiene knowledge 0.44 [0.33–0.55] (p<0.001), (P6E) knowledge + extra 0.20 [0.15–0.25](p<0.001). These significant effects were observed across all and in both estimated with CRSE.
Estimations without CRSE were highly similar. While (P4) clean hands, observed by the surveyor did
not find any difference between treatment groups, the non-CRSE estimate for (P1E) cleans hands +ATP indicated possible improved cleanliness of hands by handwashing among HE pupils, althoughsignificant only at the 10% level. Note, however, that the number of pupils with ATP measurement
was limited to 10%, one per class. For secondary outcomes, the HE-treatment had positive effect(denoted by a negative coefficient) on (H2) cold-related symptoms by -0.05 [-1.0–0.01] (p=0.086).
The statistical significance was only at the 10% level for estimation with CRSE yet it was at the 1%
level for estimation without CRSE, which could be due to the possibility of the treatment affecting theentire cluster especially for infectious disease symptoms. The average intracluster correlation for coldrelated symptoms was reduced from the baseline of ρ=0.051 to the endline of ρ=0.023. This could
suggest a positive change in the school-level dynamics related to cold symptoms, due to reduced spreador severity of cold symptoms within schools, better hygiene practices and increased awareness leading
to fewer instances of cold symptoms being transmitted within the school, improved overall healthenvironment within the clusters reducing the risk of cold symptom.
The selected results of cross-cutting HESP-intervention analysis are given in the A-Table 4 formodels which HE-treatment had statistically significant effects in HE-treatment analysis. Briefly
summarising the estimates, HE-only-treatment and HESP-treatment had positive effect with statistical30

significance on these outcomes, namely, handwashing dentalcare, overall hygiene, and health/hygiene
knowledge, while SP-only-treatment had no impact. HESP treatment had higher impact than HE-only
treatment on handwashing and overall hygiene, indicating possible additional positive effect of soap
availability. Particularly for handwashing and overall hygiene, the sum of HE-only and HESPtreatment effects were higher than that of HE-treatment in Table 5, while the magnitudes of coefficientswere same or highly similar for other outcomes which were not considered to involve soap utilisation,i.e., dentalcare and knowledge.

Table 5. Child-level analysis results: family-wise mean-standardised HE-treatment effect onnine outcome families for all children and children in both surveys
Primary Outcomes

HE

HEgroupperiodschool
type

sex

N

(P1) handwashing(P2) dentalcare(P3) overall hygiene

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]allall (noCRSE)

in bothallall (noCRSE)

in bothallall (noCRSE)

in both0.21***

0.22***

0.23***

0.17***

0.17***

0.17***

0.22***

0.22***

0.24***[0.12,
0.30][0.18,
0.25][0.14,
0.31][0.08,
0.26][0.12,
0.22][0.07,
0.26][0.14,
0.31][0.19,
0.25][0.15,
0.32][0.000][0.000][0.000][0.000][0.000][0.001][0.000][0.000][0.000]-0.05-0.05***

-0.06

-0.03

-0.03

-0.03

-0.06+-0.06***-0.07*[-0.13,0.03][-0.07, 0.03][-0.13,0.01][-0.10,0.05][-0.07,0.01][-0.11,
0.04][-0.12,0.00][-0.08, 0.04][-0.13, 0.01][0.200][0.000][0.112][0.486][0.190][0.413][0.069][0.000][0.033]0.64***

0.64***

0.67***

0.40***

0.40***

0.43***

0.53***

0.53***

0.57***[0.58,
0.71][0.61,
0.66][0.61,
0.73][0.33,
0.47][0.36,
0.43][0.36,
0.50][0.47,
0.59][0.51,
0.55][0.51,
0.63][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000]-0.08**-0.07***-0.08**-0.10**-0.10***-0.10***-0.08**-0.08***-0.09***[-0.13, 0.02][-0.09, 0.05][-0.13, 0.02][-0.16, 0.03][-0.12, 0.07][-0.16, 0.04][-0.13, 0.03][-0.10, 0.07][-0.14, 0.04][0.008][0.000][0.004][0.002][0.000][0.001][0.001][0.000][0.000]0.10***

0.09***

0.09***

0.04**

0.04***

0.04**

0.08***

0.08***

0.07***[0.08,
0.12][0.07,
0.11][0.07,
0.11][0.01,
0.07][0.02,
0.07][0.01,
0.07][0.06,
0.09][0.06,
0.09][0.06,
0.09][0.000][0.000][0.000][0.002][0.001][0.008][0.000][0.000][0.000]

16181

16181

12224

16183

16183

12226

16163

16163

12211

31

HE

HEgroupperiodschool
type

sex

N

HE

HEgroupperiod(P4) clean hands(P4E) clean hands + ATP(endline)(P5) nutrition

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]allall (noCRSE)

in bothallall (noCRSE)

in bothallall (noCRSE)

in both

-0.01

-0.01

-0.01

0.08

0.08+

0.08

-0.04

-0.04*-0.05+[-0.11,0.10][-0.06,0.04][-0.12,0.10][-0.02,0.17][-0.01,
0.16][-0.03,0.19][-0.10,0.01][-0.08,-0.00][-0.10,0.01][0.906][0.802][0.871][0.122][0.076][0.175][0.122][0.030][0.080]

0.03

0.03+

0.04

0.06*

0.06***

0.06**[-0.04,0.11][-0.00,0.07][-0.04,0.11][0.01,
0.10][0.03,
0.08][0.02,
0.11][0.412][0.086][0.368][0.012][0.000][0.007]

0.40***

0.40***

0.45***

-0.01

-0.01

0.007

[0.31,
0.48][0.36,
0.43][0.36,
0.53][-0.05,0.03][-0.04,0.02][-0.03,0.05][0.000][0.000][0.000][0.604][0.475][0.725]-0.04-0.04**

-0.04

0.01

0.01

0.03

-0.01

-0.01

-0.01

[-0.10,0.02][-0.07, 0.02][-0.10,0.02][-0.09,0.10][-0.08,0.09][-0.08,0.14][-0.04,0.02][-0.03,0.01][-0.04,0.02][0.184][0.002][0.173][0.870][0.851][0.620][0.423][0.252][0.387]0.15***

0.15***

0.16***

0.06

0.06

0.04

0.04***

0.04***

0.04**[0.12,
0.18][0.12,
0.17][0.13,
0.20][-0.02,0.15][-0.02,0.15][-0.05,0.14][0.02,
0.06][0.02,
0.06][0.01,
0.06][0.000][0.000][0.000][0.136][0.144][0.391][0.000][0.000][0.002]

16178

16178

12221

867

867

600

16183

16183

12226

(P6) knowledge(P6E) knowledge + extra(endline)

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]allall (noCRSE)

in bothallall (noCRSE)

in both0.44***

0.44***

0.45***

0.20***

0.20***

0.21***[0.33,
0.55][0.39,
0.48][0.34,
0.56][0.15,
0.25][0.18,
0.22][0.15,
0.26][0.000][0.000][0.000][0.000][0.000][0.000]-0.08-0.08***-0.06[-0.18,0.02][-0.11, 0.04][-0.16,0.03][0.130][0.000][0.204]0.80***

0.80***

0.84***[0.72,
0.88][0.77,
0.83][0.76,
0.92][0.000][0.000][0.000]32

school
type

sex

N

-0.02

-0.03*

-0.03

-0.04

-0.04**-0.03[-0.09,0.04][-0.05, 0.00][-0.10,0.04][-0.09,0.02][-0.06, 0.01][-0.09,0.02][0.433][0.022][0.375][0.171][0.002][0.244]

0.03*

0.03*

0.02

0.03*

0.03**

0.02[0.00,
0.05][0.00,
0.05][-0.00,0.05][0.01,
0.05][0.01,
0.05][-0.00,0.05][0.023][0.017][0.107][0.016][0.009][0.112]

16183

16183

12226

8991

8991

6113

Secondary Outcomes

HE

HEgroupperiodschool
type

sex

N

(H1) anthropometry(H2) cold-related symptoms(H3) other illness

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]allall (noCRSE)

in bothallall (noCRSE)

in bothallall (noCRSE)

in both

-0.02

-0.02

-0.02

-0.05+-0.05**-0.05+

0.02

0.02-0.004[-0.06,0.02][-0.08,0.03][-0.05,0.01][-0.10,0.01][-0.08, 0.01][-0.10,0.01][-0.04,0.08][-0.02,0.05][-0.07,0.06][0.262][0.416][0.107][0.086][0.007][0.078][0.573][0.324][0.906]

0.01

0.01

0.04

0.03

0.030*

0.029

-0.02

-0.02

-0.01

[-0.06,0.08][-0.03,0.05][-0.03,0.10][-0.01,0.07][0.01,
0.05][-0.01,0.07][-0.08,0.04][-0.05,0.01][-0.07,0.05][0.756][0.592][0.279][0.166][0.015][0.177][0.569][0.227][0.719]0.14***

0.14***

0.18***-0.05*-0.05***-0.05*-0.19***-0.19***-0.16***[0.11,
0.17][0.10,
0.18][0.16,
0.20][-0.09, 0.00][-0.07, 0.02][-0.09, 0.00][-0.23, 0.14][-0.21, 0.16][-0.21, 0.11][0.000][0.000][0.000][0.032][0.000][0.042][0.000][0.000][0.000]

-0.01

-0.01

-0.003-0.003-0.003-0.003

-0.01

-0.01

-0.01

[-0.07,0.06][-0.03,0.02][-0.07,0.06][-0.03,0.03][-0.02,0.01][-0.04,0.03][-0.05,0.03][-0.02,0.01][-0.05,0.03][0.846][0.656][0.929][0.830][0.699][0.880][0.735][0.437][0.692]-0.05**-0.05**-0.04+

0.002

0.002

0.003

0.08***

0.08***

0.07***[-0.09, 0.01][-0.08, 0.02][-0.08,0.00][-0.02,0.02][-0.02,0.02][-0.02,0.02][0.06,
0.09]

[0.06
0.09]

[0.05,
0.09][0.008][0.000][0.065][0.838][0.830][0.768][0.000][0.000][0.000]

16130

16130

12207

16183

16183

12226

16173

16173

12217

Notes: Each column represents a separate regression on a family of outcomes applying seemingly unrelated regressions(SUR), estimated by a feasible generalised least squares (FGLS) estimator with cluster-robust standard errors (CRSE)
unless stated otherwise. β-coefficient is the mean-standardised effect from SUR applying the difference-in-differences(DID) model, controlling for school type and child sex. For each outcome family, estimates are provided for all sample
children and children present in both baseline and endline. Each indicator family includes the following variables: (P1)
handwashing practice: handwashing habits (washing frequency in each occasion (before eating, after defecation, after
playing); used substances (soap, ash, mud and/or water only); washing with soap in each occasion; wash with runningwater; correct washing procedure); (P2) dental care practice: dentalcare (frequency and use of brush/branch, toothpaste,etc.), type of materials used; (P3) overall hygiene practice: shoes/footwear wearing at school (frequency),
shoes/footwear wearing at home (frequency in latrine and in courtyard), + P1 & P2; (P4) clean hands: clean hands by33

observation, trimmed nails, clean nails; (P4E) clean hands + ATP: additional hand cleanliness measured by ATP
improvement rate (10% of samples); (P5) nutrition practice: breakfast habit, breakfast taken in 3 days, food taken in 3days, ordered by the richness of nutrition score (none; carbohydrate (and fat); carbohydrate and vitamins;
vegetable/animal protein and vitamins; vegetable/animal protein and carbohydrate; vegetable protein, animal protein
and carbohydrate; protein, carbohydrate and vitamins); (P6) health/hygiene knowledge: handwashing procedure,
breakfast significance; (P6E) health/hygiene knowledge + extra: additional knowledge measured only in the endline,i.e., putting water in latrine before defecating, oral rehydration solution (ORS) making, food pyramid; (H1)anthropometry: height-, weight-, BMI-z-score; (H2) cold: cold-related symptoms at present and in the past two-weeks,
cough, breathing difficulty, sore throat, fever, running nose, congested nose; (H3) other illness: diarrhoea, stomachache,skin disease, fatigue, dizziness, appetite loss;. Significance level: + p<0.1, * p<0.05, ** p<0.01, ***p<0.001; 95%
confidence intervals and p-value in brackets. The results are robust with additional adjustment with child age, wealthindex, and parent literacy (see A-Table 3).

The above findings were robust to the exclusion of sex, as well as the inclusion of extra controls,child age, child wealth index, and parents’ literacy which themselves often had statistically significant
effects on the outcome families (estimation results with additional covariates in the A-Table 4).
Estimated positive sex coefficients indicate that girls had better hygiene practice, yet negative
coefficients for anthropometry and other illness indicate that girls had worse health status. Adding
child age did not affect the HE-effect estimates but slightly reduced the magnitude of period effects.
Child age had statistically significant positive effects on all primary outcomes while it had statisticallysignificant negative effects on all secondary outcomes.
There was no sign of systematic baseline differences between treatment groups apart from nutritionand overall hygiene in which HE-group showed higher and lower baseline figures, respectively. Period
was statistically significant at the 1% level for all estimation apart from nutrition model. Negative
period coefficients in cold-related symptoms and other illness demonstrated improved health frombaseline to endline. While the former could encompass seasonality effects as noted above, the latter
was not obviously related to seasons. For other health-KAPB relevant outcomes, period exhibited
positive effects with higher magnitude than HE-treatment. This signals general improvement in healthKAPB across pupils since the baseline, regardless of treatment status. Statistically significant negative
effects of school type on handwashing practice, dental-care practice and other hygiene practicesuggested that pupils going to GPS school fared better in these aspects. Negative effects of school typeon these outcome families indicate that RNGPS pupils had less of healthy practice. As a matter of fact,

34

not only the government school funding level but also the child household wealth indicator statisticallydiffered between these school types. Thus, the type of school seems to be highly correlated to pupils’socioeconomic status. The estimated HE-treatment effects and interpretation for single child-level
outcomes are given in the A-Table 5.

Period effect and of health education spillovers
Looking at the estimations results at both school and child levels, period had significant positivecoefficient with large magnitudes regardless of treatment status for all outcomes but schooling andchild nutrition. We can speculate several reasons for this: (1) there were general improvements inhealth practice and status across all schools due to external physical improvement such as hygiene
infrastructure (but not general nutrition level); (2) there were certain HE-externality effects acrossschools in terms of skill and/or information transmission. While general health improvements areplausible along with general economic development, improvements in health KABP are suggestive ofinter-school spillovers, possibly including John Henry effects among the control schools.
There were several factors that could have induced inter-school spillover effects. One was school
proximities; schools were distributed in two sub-districts and their mean distance among two schools
were 15 km, while minimum distance was 0.3 km and maximum was 41.4 km (see school location
map in A-Figure 1). The average and maximum number of schools within 2 km, 3 km and 6 km of the
HE schools were 1.7 and 6 schools, 3.9 and 10 schools, and 13.7 and 28 schools, respectively. In thearea with several schools in proximity, it was not unusual for children from neighbouring households
to go to different schools. In such circumstances, the skills and knowledge they had learned in the HE
sessions might have been talked of. On the other hand, despite being in an era of communication
technology, health skills which children learnt at schools would not be a likely topic discussed overmobile phones by their guardians, nor were such skills easily transmittable this way. Although the
project implementation attempted to mitigate possible John Henry effects among non-treatment35

schools as described above, such effects and consequent spillovers might have arisen especially giventhat HE-skills and knowledge were not complicated per se. Even for schools not in proximities, there
were occasional school headteachers meetings and other casual teacher meetings where someconversation over this external project could have taken place. Thus, information transmission barrier
could be low if sufficient interests existed, although practicing it and having behaviour change would
be different. Examples of informational spillovers in field experiments abound [46–49]. We thus
investigate whether there were any such spillovers, and if so, how they might have affected thetreatment outcomes.

Empirical model with an externality term
We add a variable to capture possible inter-school spillover effects to the above estimation equations.
Such externalities are captured by distances between schools, the number of schools in proximity, andby the number of pupils attending the school since HE-school pupils might have spoken about the HE
contents to pupils from other schools. In the influential work by [8], inter-school externalities ofdeworming drug effects depended on the sums of the total number of students at school within a certain
distance of a treatment school. In a recent work by [49], the spillover effects of agricultural advisory
services were captured by the ratio of farmers who benefit via their social network.
Given that there is no clear theory or model construct as to how such information and skills/practices
spread across distances and entities, the following externality variables are considered here: (1) acomposite externality index which incorporates distances from the HE-treatment schools as well as thenumber of attending pupils; (2) the number of schools within certain radius of the HE-treatment
schools (as well as in-between-distances). The composite externality index takes the following form:$%∙'∙ ∑( 𝑙𝑛𝑁() ∙ 𝑙𝑛𝑁! ∙ 𝑒 *+!" , where dk is the distance of school j from a treatment school k, whose effectmanifests exponential decay – the further the distance, the quicker the effects decay – multiplied by
the natural logarithm of the total number of attending students in school j, lnNj, and that in the treatment36

school k, lnNk, both measured at the baseline, summed up for all K treatment schools, and divided bytotal number of schools J times μ, the natural logarithm of the average total attending students of all
J schools at the baseline, in order to normalise. Distances are calculated using global positioning
system (GPS) data of each school location embedded in photos taken during the survey. For e-d, thecloser the schools, the lager the figure, and thus the spillover effect, e.g., for 500m the figure is 0.606,
while for 3km and 6km, it is 0.050 and 0.002, respectively. We use baseline attending students’ datarather than admitted students’ data because the latter can be significantly different from the actual
number of pupils. It was a routine practice for RNGPS schools to inflate the number of admitted
students in order to obtain more school funds (a pairwise correlation between admitted and attended
pupils for GPS and RNGPS at baseline were 0.933 and 0.819, respectively). It applies a log form
because an increase in the number of contacts does not increase proportional to the number of pupils,
given an increased probability of contacting the same person. Thus, incorporating the treatment
externality index, the school-level DID estimation equation (2) becomes:(5) 𝑌!" = 𝛼 + 𝛽 ∙ 𝑡 ∙ 𝑇! + 𝛿 ∙ 𝑡 + 𝛾 ∙ 𝑇! + 𝜑 ∙ 𝐗!" + 𝜆 ∙$%∙'∙ ∑( 𝑙𝑛𝑁() ∙ 𝑙𝑛𝑁! ∙ 𝑒 *+!" + 𝜃𝐷! + 𝜀!" .

Here, 𝜆 is expected to reflect the magnitude of treatment externalities across schools, intensified by
the number of attending students and closeness of schools both in a diminishing manner. The extent of
externalities, that is, health-related information children learnt at the HE schools spreading beyond
their families, thus depends on the number of students and the distance between the schools for thecomposite externality index.

Externality/spillover general observations
Considering the possibility of HE information dispersed by children, 2.6% of HE schools pupils
reported to have communicated what they had learnt in SBHE with their school mates, 16.9% of themwith their family members, 0.7% with pupils from other schools. Although children may have talked
about the HE matters unconsciously and not remembering it, this observation suggests that37

informational externality among children might not have been large. As noted, online information
diffusion was neither likely among children nor parents. While there could be some informational
spillover through family members, another possible route was at the school/teacher level throughvarious meetings, particularly for the headteachers. It was quickly known that certain schools werereceiving HE treatments, and the control schools were likely to have wished to be the next treatment
target. Naturally, prior to the project commencement, we had to explain about the HE intervention to
the district officer with the presence of several head teachers to obtain an agreement about the field
experiment. If regular teachers induced spillovers, its effect could be partly captured by thestratification variable school type since GPS had higher number of teachers on average; the mean
number of teachers for GPS and RNGPS at the baseline were 5.73 (1.48) and 3.72 (0.65), and of
students are 198.0 (75.24) and 126.66 (47.0), respectively (SD in parentheses). A similar thing could
be said regarding the female teachers’ ratio. Note however, that teachers were not the direct target of
SBHE and their active role was not necessarily expected. Below in Table 6, we provide a summary
statics of externality measures used for the analysis, namely, externality index, and the number ofschools within1km, within 1-2km, and within 2-3km.

Table 6. Summary statistics of externality measures (N=180)

Mean (SD)HE Schools
N=90
Mean (SD)p-value (a)HE externality index

0.44 (0.30)

0.39 (0.27)

0.254

number of HE schools within 1km

0.33 (0.56)

0.32 (0.52)

0.89

number of HE schools within 1-2km

1.42 (1.29)

1.22 (1.10)

0.264

number of HE schools within 2-3km

2.28 (1.50)

2.17 (1.41)

0.609

non-HE school
N=90

Notes: (a) t-test for numerical variables.

Externality model estimation results
We provide externality model estimation results for selected school- and child-level outcomes
which had HE-treatment estimated with statistical significance in the original analysis.
38

School-level Spillover
The results of Table 7 indicate that there were likely to be positive inter-school spillover for the
primary outcome, (S1) school hygiene practice & maintenance. The externality measures exhibited
positive effect with statistical significance except for within 2-3km, while the significance, directions
and magnitudes of HE-treatment, period effects as well as school type (omitted from Table 7) remainedrobust and unaffected. The externality index had the highest magnitude of effect, followed by the
number of schools within 1km, within 1-2km, and within 2-3km, indicating larger spillovers among
schools with more HE-schools in the nearby neighbourhood. The effect of within 4km or within 3-4km(neither shown here) was in the same direction with lesser magnitude. These results provide a strong
support to the existence of positive HE externality in addition to the general period effects.

Table 7. Family-wise mean-standardised treatment effect and externality effect on the schoollevel primary outcome
School-level Mean-standardized Treatment Effects with Externality Effects(S1) school hygiene practice & maintenanceβ-coefficient[95%CI] [p-value]

β-coefficient[95%CI] [p-value]

β-coefficient[95%CI] [p-value]

β-coefficient[95%CI] [p-value]

externality measuresexternality index
within 1km
within 1-2km
within 2-3km
HE- treatment
0.36***
0.36***
0.36***
0.36***[0.18, 0.55][0.18, 0.55][0.18, 0.55][0.17, 0.55][0.000][0.000][0.000][0.000]period0.59***
0.59***
0.59***
0.59***[0.47, 0.72][0.46, 0.72][0.47, 0.72][0.46, 0.72][0.000][0.000][0.000][0.000]externality measures
0.27**
0.10*
0.05*
-0.01
[0.11, 0.43][0.01, 0.20][0.01, 0.09][-0.04, 0.02][0.001][0.025][0.020][0.596]
N
360
360
360
360
Notes: Each column represents a separate regression with different externality measures. Outcome families are same asin Table 4 primary outcomes. All estimations apply DID SUR, adjusted for HE-group and school type (estimatesomitted). Externality index incorporate distances from all HE-treatment schools as well as the number of attending pupils(1/(𝐽 ∙ 𝜇) ∙ ∑# 𝑙𝑛𝑁#$ ∙ 𝑙𝑛𝑁% ∙ 𝑒 &'!" , where dk is the distance of school j from a treatment school k, whose effect manifests
exponential decay multiplied by the natural logarithm of the total number of attending students in school j, lnNj, and thatin the treatment school k, lnNk, both measured at the baseline, summed up for all K treatment schools, and divided bytotal number of schools J times μ, the natural logarithm of the average total attending students of all J schools at thebaseline, in order to normalize). Other externality measures (“within x-ykm”) correspond to the number of schools within39

x-ykm radius. Significance level: + p<0.1, * p<0.05, ** p<0.01, ***p<0.001; 95% confidence intervals and p-value inbrackets. 360 samples are 90 HE schools and 90 HE-control schools in the baseline and endline.

Child-level Spillover
As in the school-level analysis, externality measures demonstrated significant impacts on (P1)
handwashing, (P2) dentalcare, (P3) overall hygiene, (P6) health/hygiene knowledge, and (H2) coldrelated symptoms. As in the school-level, externality index displayed the largest coefficient magnitude,
followed by within 1km, within 1-2km, and within 2-3km (omitted here) among the externality
measures (Table 8, estimates for HE-group, school type and sex are omitted). All estimated coefficients
showed identical results as those in the original analysis given in Table 5. This not only reaffirms therobustness of HE, period and other estimates, but also indicates that externality effects were at workin addition to HE-treatment and period effects, and that they improved model’s explanatory power.
These statistically significant positive spillovers suggest that health-related information and possibly
healthier behaviour spread to non-HE school pupils in proximity, with stronger effects for those having
more of HE-schools closer by.

Table 8. Family-wise mean-standardized treatment effect and externality effect on the Childlevel selected outcomesChild-level Mean-standardized Treatment Effects with Externality Effects(P1) handwashing(P2) dentalcare(P3) overall hygiene

externality measuresexternality measuresexternality measuresexternalit
y indexwithin1km

within 12km

externali
ty index

within1km

within 12km

externali
ty index

within1km

within 12km

HE-

0.21***

0.21***

0.21***

0.17***

0.17***

0.17***

0.22***

0.22***

0.22***

treatment[0.13,0.30][0.13,0.30][0.13,0.30][0.08,0.26][0.08,0.26][0.08,0.26][0.14,0.31][0.14,0.31][0.14,0.31][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000]0.64***

0.64***

0.64***

0.40***

0.40***

0.40***

0.53***

0.53***

0.53***[0.58,0.70][0.58,0.70][0.58,0.70][0.33,0.47][0.33,0.47][0.33,0.47][0.47,0.59][0.47,0.59][0.47,0.59][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000]externality

0.18**

0.09***

0.04***

0.28***

0.10**

0.06***

0.23***

0.10***

0.04***

measures[0.06,0.30][0.04,0.14][0.02,0.06][0.16,0.39][0.04,0.17][0.03,0.08][0.12,0.33][0.05,0.15][0.02,0.06]

period40

N[0.003][0.000][0.000][0.000][0.002][0.000][0.000][0.000][0.000]

16181

16181

16181

16183

16183

16183

16163

16163

16163

(P6) knowledge(P6E) knowledge + extra(endline)(H2) cold-related symptoms(no CRSE)

externality measuresexternality measuresexternality measuresexternalit
y indexwithin1km

within 12km

externali
ty index

within1km

within 12km

externali
ty index

within1km

within 12km

HE-

0.44***

0.44***

0.44***

0.20***

0.20***

0.20***-0.05**-0.05**-0.05**

treatment[0.33,0.55][0.33,0.55][0.33,0.55][0.15,0.25][0.15,0.25][0.15,0.25][-0.08,-0.01][-0.08,-0.01][-0.08,-0.01][0.000][0.000][0.000][0.007][0.007][0.007]0.80***

0.80***

0.80***-0.05***-0.05***-0.05***[0.72,0.88][0.72,0.88][0.72,0.88][-0.07,-0.02][-0.07,-0.02][-0.07,-0.02][0.000][0.000][0.000][0.000][0.000][0.000]externality

0.15*

0.07*

0.03*

0.10+

0.05+

0.02+

-0.05***-0.04***

0measures[0.03,0.27][0.01,0.13][0.01,0.06][-0.01,0.21][-0.00,0.10][-0.00,0.04][-0.08,-0.02][-0.05,-0.02][-0.00,0.01][0.018][0.021][0.016][0.089][0.056][0.081][0.001][0.000][0.526]

16183

16183

16183

8991

8991

8991

16183

16183

16183

periodN

Notes: Each column represents a separate regression with different externality measures. Outcome families are same as in Table 5
primary outcomes. All estimations apply DID SUR with CRSE, except for (H2) model, adjusted for HE-group, school type, and sex(estimates omitted). Externality index incorporate distances from all HE-treatment schools as well as the number of attending pupils(1/(𝐽 ∙ 𝜇) ∙ ∑! 𝑙𝑛𝑁!" ∙ 𝑙𝑛𝑁# ∙ 𝑒 $%!" , where dk is the distance of school j from a treatment school k, whose effect manifests exponential
decay multiplied by the natural logarithm of the total number of attending students in school j, lnNj, and that in the treatment schoolk, lnNk, both measured at the baseline, summed up for all K treatment schools, and divided by total number of schools J times μ, thenatural logarithm of the average total attending students of all J schools at the baseline, in order to normalize). Other externality
measures (“within x-ykm”) correspond to the number of schools within x-ykm radius. Significance level: + p<0.1, * p<0.05, ** p<0.01,***p<0.001; 95% confidence intervals and p-value in brackets. 16183 samples belonged to 90 HE schools and 90 HE-control schools
in the baseline and endline.

Cost-effectiveness of health education
The project's direct labour expenses for 18 para-teachers amounted to 14,466USD for the year.
Additionally, there were costs for the initial training workshop and subsequent refresher workshops,
totalling 1,156USD. Beyond these operational expenses, which summed up to 15,622USD, there werealso fixed costs: 4,943USD for developing digital health education materials and 3,460USD forpurchasing 23 mini-projectors, with five extras included. The HE digital materials can be used almost
perpetually in principle, however, considering that material contents can get outdated, we may
conservatively suppose its lifetime as 10 years without major revision. As for mini-projector, we may
assume that such equipment gets depreciated in 5-year time. Presuming their equal use value in each41

time-period, simply dividing the costs of materials and projector, their annual costs were 494USD and
346USD, respectively. Therefore, the total annual cost of HE was 16,462USD including the fixed cost.
Per school annual running cost (excluding workshops) was 161USD or 183USD including theworkshops and other fixed cost. Converting the annual cost to monthly cost, it was 13USD or 15USD,
respectively. This monthly cost of our project was far less than that of the four weekly school hygiene
education sessions and hygiene nudge construction project cited above[14] which was 127USD or
124USD per school, respectively, which would be 98USD or 96USD adjusting for the annual average
inflation rate of 6.56% during 2013-2016 in Bangladesh.[50] The number of attended pupils in HE
schools at the endline, thus assumed to have been treated during the project, were 15,622 (those in
non-HE schools were 14,381). This makes the annual labour cost of para-teachers as well as of thetotal HE per attended student at 0.92USD and 1.05USD, respectively. As a reference, this was twice
expensive than per student deworming cost of 0.49USD in large scale Tanzanian government projectcited in [8].
In terms of cost-effectiveness of HE project, dividing the annual total HE cost per school (183USD)
by the HE-treatment effect on school hygiene practice (0.36 as shown in Table 4 and Table 7) for
instance, one percentage improvement in school hygiene was achieved by 5.08USD per school.
Divided by the mean attended 173.8 students in an HE school at the endline, the respective costeffectiveness was 0.029USD per pupil. Incorporating the effects of the composite externality index
shown in Table 7, the total effect of HE became 0.63 points improvements in school hygiene practiceper school, then one percentage improvement in school hygiene in HE schools was achievable by
2.90USD per school or 0.017USD per pupil, taking into account the spillover effects. The costeffectiveness of one percentage improvement in school-level outcomes and in child-level outcomesare provided in Table 9 and Table 10, respectively. As a reference, the above-cited work [9] which
conducted community intervention estimated the cost of health knowledge improvement by one
percentage to be around 0.75~0.82USD, and personal hygiene improvement by one percentage to be42

around 1.10~1.32USD per household. Although our case was per school/child, and the content ofhealth education was not the same, comparing these HE costs, our school-based SBHE seem to behighly cost effective.

Table 9. Para-teacher and total HE cost in USD for 1% improvement in family-wise school health
KAPB
Cost per 1% improvement in school-level outcome(S1) school hygiene practice & maintenancepara-teacher cost

total cost

HE effects
4.46
5.08
HE + spillover
2.55
2.90
Notes: Figures shown are calculated running (para-teachers) cost and total cost (including fixed cost) per
school in USD per 1% improvement in outcomes for which estimated coefficient of HE and eternality index
had statistical significance of at least 5% given in Table 7.

Table 10. Para-teacher and total HE cost in USD for 1% improvement in family-wise child health
KAPB

HE effects
HE + spillover

Cost per 1% improvement in child-level outcome(P1) handwashing(P2) dentalcare
para-teacher
para-teacher
total cost
total cost
cost
cost
0.04
0.05
0.05
0.06
0.02
0.03
0.02
0.02
(P6) knowledge(P6E) knowledge + extra(P3) overall hygienepara-teacher
total cost
cost
0.04
0.05
0.02
0.02
(H2) cold-related symptoms

para-teacher
para-teacher total costpara-teacher
total cost
total cost
cost
cost
cost
HE effects
0.02
0.02
0.05
0.05
-0.18
-0.21
HE + spillover
0.02
0.02
0.03
0.04
-0.09
-0.11
Notes: Figures shown are calculated running (para-teachers) cost and total cost (including fixed cost) per child in USD
per 1% improvement in outcomes for which estimated coefficient of HE and eternality index had statistical significanceof at least 5% given in Table 8.

Discussions and Conclusions
Our SBHE had positive effects on improved healthy/hygienic practice and behaviour both at theschool and child levels. The evidence indicated possible establishment of new healthier norms. Cleaner
and better maintained school infrastructure and self-hygiene, as sustained by the evidence, were
expected to also serve as infection prevention from diseases and parasites. This was in part confirmed
by children’s health improvements related to cold symptoms. The fact that no impact was observed for

43child anthropometry or child nutrition practice suggests that one-year SBHE alone was not sufficientin improving child nutrition intake. This resonates to the findings that neither educational nor foodvoucher provision was effective in nutrition practice on its own but only when provided together[51].The fact that the growth spurts happens at around puberty might also explain the no-effect onchild anthropometry. Despite being a school-based intervention, our analysis showed that parents’
literacy and particularly household wealth positively affected all child-level outcomes including childhealth and healthy behaviour as seen in [44]. While some outcomes such as handwashing habits werebased on self-reports by the children, their knowledge regarding correct handwashing procedures
performed and evaluated by the surveyors did support their responses. Also, the fact that children did
not reply to have done the ‘correct practice’ in all demanded occasions buttressed the objectivity of
their responses. The SBHE effects on dentalcare resonated with those of other successful SBHE studies[11,15,52]. Our results suggest that school-aged children were sufficiently amenable to new habit
formation with possible peer effects in schools, achieved through a one-year intervention of weekly
SBHE sessions with an emphasis on practical skill-building and context-relevant active learning.
Several plausible reasons limited our study findings particularly for the secondary outcomes ofschooling and child health. Either a weekly SBHE for one year was insufficient to make improvementin these outcomes, insufficient statistical power to detect the effects given sample size of 180 schools
and the actual effect size, or contamination due to spillover effects diluting the true effect measure.
Other reasons could be the attrition of relatively unhealthy children that could have caused a downward
bias for the estimated effects, or the cancelling-off effects between HE session and PE session, as HE
session was carried out using PE class allotment. If PE classes, playing around, were assumed to havepositive health effects, estimated HE effects could be biased toward zero. Concerning schooling, notreatment effect could be due to the likely undercounted dropout/repetition figures provided by theschools. The repetition figure provided by the school record amounting to 8.5% for grade 1~5 in theendline was far less than that of 20% in our randomly chosen surveyed pupils. Although the inclusion44

of spillover/externality measures did not affect the effect size of other estimated coefficients, such
spillover effects might have diluted the treatment effect estimate itself. Spillover effects were indeed
statistically significant, and the closer and the more HE-schools, the larger was the effect of spillover.
The period effect might have also captured part of the effects; as we have seen, there was a large
general improvement from the baseline to the endline across most of the outcomes.
As for the strengths of our study, our SBHE was designed to overcome previously identified
difficulties in conducting health education in terms of its cost, context relevance and effectiveness, andunmotivated teachers [9,33,53,54]. Our SBHE intervention employing para-teachers and using mobile
projectors simultaneously overcame the problems of over-burdened primary school teachers, limited
budgets, and knowledge gaps among the instructors. The use of mini-projectors with digital materials
was also supportive for pupils to acquire healthy behaviours, by engaging them in the context relevant
animations and images. The study rigorously conducted with randomly chosen samples and treatment
allocation. Analysis-wise, we estimated the mean-standardised average treatment effect on the familyof outcomes in order to avoid over identification of statistically significant results. Another strengthand the novelty of our study was that we explicitly measured the spillover effects of SBHE. While
spillover effects are generally considered as nuisance to the RCT evaluation, in actuality, they could
disseminate part of SBHE without extra cost to other schools and pupils. Spillover effects could also
reduce the de facto cost of intervention, as shown in our cost-effectiveness analysis. The results
suggested that relatively uncostly SBHE could possibly induce healthy behaviours not only intreatment schools and their children, but also in other non-treated schools and pupils. Once acquired,healthy habits are non-costly financially and sustainable per se, and they can reinforce the positive
impacts of other supply-based intervention, as proven by the better management and maintenance ofschool hygiene infrastructure. To the extent that Jhenaidah district was a typical rural area and that the
samples were sufficiently representative of the population, the findings would be generalisable to otherrural areas in Bangladesh. Although our study was for a limited period, further research looking into45

how well such healthy environment and behaviour sustain would be warranted. Along with economic
development, healthy behavioural changes and norms are expected to contribute to breaking thevicious circle of ill health, poor education and poverty, and to overcome a problem of low investmentin own health toward sustainable health improvement.

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Appendices
A-Figure 1. Project School Map in Jhenaidah, Bangladesh.
A-Table 1. School-level analysis results: family-wise mean-standardised cross-cutting HESP
treatment effect on five outcome families (N=360).
A-Table 2. HE-treatment effects on each school level outcome (selected outcomes).
A-Table 3. Child-level analysis results: family-wise mean-standardised HE treatment on nineoutcome families for all children and children in both surveys
A-Table 4. Child-level analysis results: family-wise mean-standardised cross-cutting HESP
treatment effect on nine outcome families for all children with additional covariates.
A-Table 5. HE-treatment effects on each child level outcome (selected outcomes).

49

A-Figure 1. Project School Map in Jhenaidah, Bangladesh

HE Schools
HESP schools
SP schools
Control schools

Note: Map created using Google map.Map data ©2019

2 km

A-Table 1. School-level analysis results: family-wise mean-standardised cross-cutting HESP treatment
effect on five outcome families (N=360)
primary outcomes(S1) school hygiene
practice & maintenancesecondary outcomes(S2) hygiene infrastructure(S3) schooling

β-coefficient
β-coefficient
β-coefficient[95%CI] [p-value][95%CI] [p-value][95%CI] [p-value]
HE-treatment
0.19
0.19
0.09
0.1
0.13
0.13
[-0.07,0.45][-0.07,0.45][-0.26,0.44][-0.24,0.44][-0.10,0.36] [-0.10,0.36][0.150][0.150][0.606][0.571][0.280][0.280]HESP- treatment
0.37**
0.37**
-0.14
-0.15
0.01
0.01
[0.10,0.64][0.10,0.64][-0.50,0.22][-0.50,0.21][-0.21,0.22] [-0.21,0.22][0.008][0.008][0.449][0.417][0.952][0.950]SP-treatment
-0.16
-0.16
-0.02
-0.02
-0.04
-0.04
[-0.42,0.09][-0.42,0.09][-0.38,0.35][-0.38,0.35][-0.29,0.20] [-0.29,0.20][0.211][0.212][0.932][0.933][0.727][0.727]HE-group
0.02
0.02
-0.08
-0.06
-0.09
-0.09
[-0.18,0.22][-0.18,0.22][-0.34,0.18][-0.31,0.20][-0.22,0.05] [-0.22,0.05][0.852][0.842][0.534][0.655][0.209][0.211]HESP-group
-0.23
-0.23
0.16
0.15
0.09
0.09
[-0.54,0.07][-0.54,0.07][-0.22,0.53][-0.22,0.52][-0.11,0.28] [-0.11,0.28][0.138][0.137][0.419][0.430][0.385][0.383]SP-group
0.03
0.03
-0.05
-0.05
-0.1
-0.1
[-0.18,0.24][-0.18,0.24][-0.33,0.23][-0.33,0.23][-0.26,0.05] [-0.26,0.05][0.759][0.758][0.718][0.727][0.193][0.192]
period0.68***
0.68***
0.53***
0.52***-0.03-0.03[0.50,0.86][0.50,0.86][0.26,0.79][0.26,0.78][-0.21,0.15] [-0.21,0.15][0.000][0.000][0.000][0.000][0.723][0.724]school type-0.16**-0.15**
-0.04
0.02
0.06
0.05
[-0.25,-0.06] [-0.27,-0.04][-0.16,0.08][-0.12,0.16][-0.02,0.13] [-0.03,0.14][0.001][0.008][0.524][0.783][0.164][0.225]female
0.01
0.21
-0.01
teacher ratio[-0.18,0.21][-0.05,0.47][-0.16,0.15][0.909][0.109][0.939]
N
360
360
360
360
360
360
Note: Each column represents a separate regression on a family of outcomes estimated by seemingly unrelated
regressions (SUR), estimated by a feasible generalised least squares (FGLS) estimator. β-coefficient is the mean
standardised effect from SUR applying the difference-in-differences (DID) model, controlling for school type. An
additional covariate is female teacher ratio. Outcome family compositions are: (S1) school hygiene practice: latrine
cleaning rota, latrine cleaning days per week, classroom cleaning rota, classroom cleaning days per week, rubbish bin
provision, latrine brush provision, rubbish disposal method, soap provision at handwashing facility; (S2) school
hygiene infrastructure: clean classrooms, clean accessible latrines for boys; clean accessible latrines for girls, ratio ofclean-usable latrine for boys, ratio of clean-usable latrine for girls, closeness of handwashing facility to the latrines(within 10~11 steps), easiness of obtaining water by younger children; (S3) schooling: absence rate for each grade1~5,
repetition rate and dropout rate for each grade 2~5. Significance level: + p<0.1, * p<0.05, ** p<0.01, ***p<0.001; 95%
confidence interval and p-value in brackets. 360 samples are 90 HE-schools and 90 control-schools in the baseline
and endline.

A-Table 2. HE-treatment effects on each school level outcome (selected outcomes)
HE-treatment effects
Outcome(1)pooled(1e)endline

(2)
DID

(3)
logit

(4)
ologitrubbish bin in each classroom

0.34***

0.34***

0.43***

3.45**

3.32**[0.002][0.006][0.002][0.030][0.030]0.32**

0.32*

0.30*

2.55*

1.74

[0.036][0.072][0.097][0.061][0.228]0.33***

0.33***

0.39***

3.10*

2.52**[0.002][0.001][0.006][0.054][0.035]0.24***

0.24***

0.23**

3.60***[0.000][0.000][0.011][0.008]0.11*

0.11***

0.13

5.29**[0.062][0.009][0.124][0.022]0.53***

0.53**

0.49*[0.008][0.022][0.074]

0.12*

0.12*

0.09

[0.084][0.068][0.307]

0.12

0.12

0.39**[0.409][0.383][0.021]rubbish disposal methodsoap for pupil at handwashingfacility (a)routine latrine cleaning

latrine cleaning brush provision

weekly latrine cleaning days
SMC executed health related
activity
sealed latrine-pit
latrine detergent provided (b)0.11**

2.44**[0.039][0.042]

Note: HE-treatment effects are given for each outcome shown in the first column for separate regression models (1)~ (4). Significance level: + p<0.1, * p<0.05, ** p<0.01, ***p<0.001, and p-value in brackets. Although results are notshown here, (a) for soap for pupil at handwashing facility, estimated coefficient of HE in HESP DID model is
0.400*, while that of HESP or SP is non-significant; (b) estimates for latrine detergent provided is available only for
endline. Shown estimates for logit model and ordered-logit (ologit) are exponentiated (odds-ratio).

A-Table 3. Child-level analysis results: family-wise mean-standardised HE-treatment effect on nine outcome families for all children with additional
covariates
Primary Outcomes

HE-treatment

HE-group

periodschool typesex

child age

wealth index(P1) handwashing(P2) dentalcare(P3) overall hygiene

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]

0.215***

0.211***

0.207***

0.169***

0.173***

0.168***

0.223***

0.225***

0.221***[0.13,0.30][0.12,0.30][0.12,0.30][0.08,0.26][0.08,0.27][0.07,0.26][0.14,0.31][0.14,0.31][0.14,0.30][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000]-0.053-0.047-0.05-0.028-0.025-0.026-0.060+-0.056+-0.057+[-0.12,0.02][-0.12,0.03][-0.12,0.03][-0.11,0.05][-0.10,0.05][-0.10,0.05][-0.12,0.00][-0.11,0.00][-0.12,0.00][0.147][0.206][0.195][0.486][0.491][0.488][0.065][0.059][0.065]0.603***

0.621***

0.641***

0.362***

0.375***

0.396***

0.491***

0.509***

0.529***[0.54,0.66][0.56,0.69][0.58,0.71][0.29,0.43][0.31,0.44][0.33,0.46][0.43,0.55][0.45,0.57][0.47,0.59][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000]-0.067*-0.058*-0.068*-0.094**-0.079**-0.087**-0.081**-0.067**-0.076**[-0.12,-0.01][-0.11,-0.01][-0.12,-0.01][-0.16,-0.03][-0.13,-0.02][-0.14,-0.03][-0.13,-0.03][-0.11,-0.02][-0.12,-0.03][0.012][0.027][0.013][0.003][0.005][0.003][0.002][0.004][0.002]0.089***

0.097***

0.093***

0.040**

0.042**

0.036**

0.075***

0.077***

0.073***[0.07,0.11][0.08,0.12][0.07,0.11][0.01,0.07][0.02,0.07][0.01,0.06][0.06,0.09][0.06,0.09][0.06,0.09][0.000][0.000][0.000][0.003][0.002][0.007][0.000][0.000][0.000]0.035***

0.036***

0.041***[0.03,0.04][0.03,0.05][0.03,0.05][0.000][0.000][0.000]0.108***

0.111***

0.104***[0.09,0.12][0.09,0.13][0.09,0.12]

[0.000]
parents' literacy

NHE-treatment

HE-group

periodschool typesex

child age

16102

16171

[0.000][0.000]0.061***

0.078***

0.062***[0.05,0.08][0.06,0.10][0.05,0.08][0.000][0.000][0.000]

16181

16104

16173

16183

16084

16153

16163

(P4) clean hands(P4E) clean hands + ATP (endline)(P5) nutrition

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]-0.004-0.007-0.008

0.06

0.07

0.07

-0.04

-0.042-0.044[-0.11,0.10][-0.11,0.10][-0.11,0.10][-0.03,0.16][-0.02,0.17][-0.03,0.17][-0.09,0.01][-0.10,0.01][-0.10,0.01][0.940][0.897][0.878][0.196][0.128][0.146][0.141][0.124][0.105]

0.026

0.037

0.032

0.051*

0.057**

0.055*[-0.05,0.10][-0.04,0.11][-0.04,0.11][0.01,0.09][0.01,0.10][0.01,0.10][0.501][0.332][0.400][0.020][0.008][0.010]0.331***

0.374***

0.394***

0

0.03

0.01

-0.029-0.023-0.012[0.25,0.41][0.29,0.46][0.31,0.48][-0.10,0.10][-0.07,0.13][-0.08,0.11][-0.07,0.01][-0.06,0.02][-0.05,0.03][0.000][0.000][0.000][0.971][0.545][0.806][0.146][0.233][0.554]-0.037-0.024-0.035-0.01-0.001-0.007[-0.10,0.02][-0.08,0.03][-0.09,0.02][-0.04,0.02][-0.03,0.03][-0.03,0.02][0.228][0.402][0.243][0.472][0.923][0.610]0.144***

0.149***

0.146***

0.07+

0.06

0.06

0.039***

0.041***

0.038***[0.11,0.17][0.12,0.18][0.12,0.17][-0.01,0.16][-0.02,0.14][-0.02,0.14][0.02,0.06][0.02,0.06][0.02,0.06][0.000][0.000][0.000][0.070][0.163][0.150][0.000][0.000][0.000]0.066***

0.05***

0.019***[0.06,0.07][0.03,0.08][0.01,0.02][0.000][0.000][0.000]wealth index

0.105***

0.07**

0.065***[0.09,0.12][0.02,0.11][0.05,0.08][0.000][0.002][0.000]parents' literacy

NHE-treatment

HE-group

periodschool typesex

child age

16099

16169

0.052***

0.03

0.039***[0.03,0.07][-0.02,0.08][0.03,0.05][0.000][0.185][0.000]

16178

867

867

867

(P6) knowledge(P6E) knowledge + extra (endline)

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]

0.44***

0.44***

0.44***

0.19***

0.20***

0.20***[0.32,0.55][0.33,0.55][0.33,0.55][0.14,0.25][0.15,0.25][0.15,0.25][0.000][0.000][0.000][0.000][0.000][0.000]

-0.08

-0.08

-0.08

[-0.18,0.02][-0.17,0.02][-0.18,0.02][0.134][0.131][0.128]0.75***

0.79***

0.80***

-0.03

-0.02

-0.03

[0.68,0.83][0.71,0.87][0.72,0.88][-0.09,0.02][-0.07,0.03][-0.08,0.02][0.000][0.000][0.000][0.229][0.376][0.201]

-0.02

-0.02

-0.02

[-0.08,0.04][-0.08,0.04][-0.08,0.04][0.516][0.600][0.506]

0.02+

0.03*

0.02*

0.02+

0.03*

0.03*

[-0.00,0.04][0.00,0.05][0.00,0.05][-0.00,0.05][0.01,0.05][0.00,0.05][0.053][0.021][0.040][0.059][0.017][0.024]0.06***

0.08***

16104

16173

16183

[0.05,0.06][0.07,0.09][0.000][0.000]

wealth index

0.06***

0.08***[0.04,0.07][0.07,0.10][0.000][0.000]parents' literacy

N

16104

16173

0.04***

0.03***[0.02,0.06][0.01,0.04][0.000][0.001]

16183

8991

8991

8991

Secondary Outcomes

HE-treatment

HE-group

periodschool typesex(H1) anthropometry(H2) cold-related symptoms(H3) other illness

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]-0.019-0.021-0.021-0.047+-0.047+-0.046+

0.02

0.017

0.018

[-0.06,0.02][-0.06,0.02][-0.06,0.02][-0.10,0.01][-0.10,0.01][-0.10,0.01][-0.04,0.08][-0.05,0.08][-0.04,0.08][0.313][0.282][0.285][0.082][0.085][0.088][0.528][0.587][0.568]

0.011

0.014

0.011

0.031

0.029

0.029

-0.019-0.018-0.018[-0.05,0.08][-0.05,0.08][-0.06,0.08][-0.01,0.07][-0.01,0.07][-0.01,0.07][-0.08,0.04][-0.08,0.04][-0.08,0.04][0.736][0.698][0.761][0.149][0.174][0.166][0.536][0.571][0.569]0.192***

0.119***

0.140***-0.031-0.041+-0.045*-0.171***-0.182***-0.184***[0.16,0.22][0.09,0.15][0.11,0.17][-0.07,0.01][-0.08,0.00][-0.09,-0.00][-0.22,-0.12][-0.23,-0.14][-0.23,-0.14][0.000][0.000][0.000][0.155][0.052][0.034][0.000][0.000][0.000]-0.0080.008-0.009-0.004-0.006-0.005-0.007-0.009-0.008[-0.06,0.05][-0.05,0.07][-0.07,0.05][-0.04,0.03][-0.04,0.02][-0.04,0.03][-0.05,0.03][-0.05,0.03][-0.05,0.03][0.794][0.809][0.768][0.788][0.678][0.756][0.741][0.664][0.712]-0.046*-0.050**-0.048*

0.003

0.002

0.003

0.076***

0.075***

0.076***

child age[-0.08,-0.01][-0.09,-0.01][-0.08,-0.01][-0.01,0.02][-0.02,0.02][-0.02,0.02][0.06,0.10][0.06,0.09][0.06,0.10][0.010][0.007][0.011][0.721][0.853][0.761][0.000][0.000][0.000]-0.057***-0.016***-0.015***[-0.07,-0.04][-0.02,-0.01][-0.02,-0.01][0.000][0.000][0.000]

wealth index

0.092***-0.021***-0.015**[0.07,0.11][-0.03,-0.01][-0.03,-0.00][0.000][0.000][0.005]parents' literacy

N

16062

16120

-0.030**-0.014*-0.006[-0.05,-0.01][-0.03,-0.00][-0.02,0.01][0.008][0.014][0.346]

16130

16104

16173

16183

16094

16164

16173

Notes: Each column represents a separate regression on a family of outcomes applying seemingly unrelated regressions (SUR), estimated by a feasible generalised leastsquares (FGLS) estimator with cluster-robust standard errors (CRSE) except for (H2). β-coefficient is the mean-standardised effect from SUR applying the difference-indifferences (DID) model, controlling for school type, child sex and additional covariates, namely, child age, wealth index, and parent literacy. Wealth index is created throughiterated principal factor, reflecting house structure materials, roof materials, number of rooms, latrine structure and materials, possession of electronic appliances, mobile
phones and bikes. Parents’ literacy reflects whether the child mother and father can read. For each outcome family, estimates are provided for all sample children and children
present in both baseline and endline. Each indicator family includes the following variables: (P1) handwashing practice: handwashing habits (washing frequency in eachoccasion (before eating, after defecation, after playing); used substances (soap, ash, mud and/or water only); washing with soap in each occasion; wash with running water;correct washing procedure); (P2) dental care practice: dentalcare (frequency and use of brush/branch, toothpaste, etc.), type of materials used; (P3) overall hygiene practice:
shoes/footwear wearing at school (frequency), shoes/footwear wearing at home (frequency in latrine and in courtyard), + P1 & P2; (P4) clean hands: clean hands byobservation, trimmed nails, clean nails; (P4E) clean hands + ATP: additional hand cleanliness measured by ATP improvement rate (10% of samples); (P5) nutrition practice:breakfast habit, breakfast taken in 3 days, food taken in 3 days, ordered by the richness of nutrition score (none; carbohydrate and vitamins; carbohydrate, protein and plus);(P6) health/hygiene knowledge: handwashing procedure, breakfast significance; (P6E) health/hygiene knowledge + extra: additional knowledge measured only in the endline,i.e., putting water in latrine before defecating, oral rehydration solution (ORS) making, food pyramid; (I1) anthropometry: height-, weight-, BMI-z-score; (I2) cold: coldrelated symptoms at present and in the past two-weeks, cough, breathing difficulty, sore throat, fever, running nose, congested nose; (I3) other illness: diarrhoea, stomachache,skin disease, fatigue, dizziness, appetite loss. Significance level: + p<0.1, * p<0.05, ** p<0.01, ***p<0.001; 95% confidence intervals and p-value in brackets.

A-Table 4. Child-level analysis results: family-wise mean-standardised cross-cutting HESP-treatment effect on selected outcome families for all
children with additional covariates
Primary Outcomes

HE-treatment

HESP- treatment

SP-treatment

HE-group

HESP-group

SP-group

period(P1) handwashing(P2) dentalcare(P3) overall hygiene

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]

0.146*

0.142*

0.140*

0.174**

0.171**

0.168*

0.167**

0.164**

0.162*[0.03,0.27][0.02,0.26][0.02,0.26][0.04,0.31][0.04,0.30][0.04,0.30][0.04,0.29][0.04,0.29][0.04,0.29][0.017][0.020][0.021][0.010][0.010][0.012][0.008][0.009][0.010]0.203**

0.208***

0.199**

0.110+

0.119+

0.107

0.181**

0.186**

0.176**[0.08,0.32][0.09,0.33][0.08,0.32][-0.02,0.24][-0.01,0.25][-0.02,0.24][0.06,0.30][0.07,0.30][0.06,0.29][0.001][0.001][0.001][0.097][0.070][0.108][0.003][0.002][0.004]-0.078-0.079-0.084-0.041-0.043-0.047-0.087-0.089-0.093[-0.20,0.04][-0.20,0.04][-0.20,0.03][-0.17,0.09][-0.17,0.09][-0.18,0.09][-0.20,0.03][-0.20,0.02][-0.21,0.02][0.196][0.183][0.161][0.546][0.521][0.487][0.138][0.120][0.110]-0.04-0.024-0.031-0.108+-0.093+-0.101+-0.077-0.062-0.069[-0.14,0.06][-0.11,0.07][-0.13,0.06][-0.22,0.00][-0.19,0.01][-0.21,0.01][-0.17,0.02][-0.15,0.02][-0.16,0.02][0.426][0.599][0.516][0.052][0.069][0.063][0.104][0.150][0.134]-0.061-0.052-0.053-0.039-0.032-0.032-0.06-0.051-0.051[-0.17,0.04][-0.15,0.04][-0.15,0.05][-0.14,0.07][-0.13,0.07][-0.13,0.07][-0.15,0.03][-0.13,0.03][-0.14,0.04][0.249][0.287][0.297][0.472][0.518][0.544][0.208][0.238][0.260]

0.004

0.019

0.015

-0.095-0.079-0.083-0.02-0.004-0.008[-0.10,0.11][-0.08,0.12][-0.09,0.12][-0.22,0.02][-0.19,0.03][-0.20,0.03][-0.12,0.08][-0.09,0.08][-0.10,0.09][0.945][0.717][0.780][0.120][0.157][0.159][0.691][0.933][0.865]0.637***

0.649***

0.671***

0.378***

0.394***

0.416***

0.528***

0.547***

0.567***[0.55,0.73][0.56,0.74][0.58,0.76][0.28,0.48][0.30,0.49][0.32,0.51][0.43,0.62][0.46,0.64][0.48,0.66]

school typesex

child age

[0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000]-0.066*-0.053*-0.062*-0.089**-0.075**-0.083**-0.078**-0.064**-0.073**[-0.12,-0.01][-0.10,-0.01][-0.11,-0.01][-0.15,-0.03][-0.13,-0.02][-0.14,-0.03][-0.13,-0.03][-0.11,-0.02][-0.12,-0.03][0.012][0.030][0.014][0.003][0.005][0.003][0.002][0.004][0.002]0.088***

0.090***

0.086***

0.039**

0.041**

0.036**

0.073***

0.076***

0.072***[0.07,0.11][0.07,0.11][0.07,0.10][0.01,0.06][0.02,0.07][0.01,0.06][0.06,0.09][0.06,0.09][0.05,0.09][0.000][0.000][0.000][0.003][0.002][0.007][0.000][0.000][0.000]0.035***

0.037***

0.040***[0.03,0.04][0.03,0.05][0.03,0.05][0.000][0.000][0.000]

wealth index

0.101***

0.108***

0.100***[0.09,0.12][0.09,0.12][0.09,0.11][0.000][0.000][0.000]parents' literacy

NHE-treatment

HESP- treatment

16102

16171

0.056***

0.075***

0.060***[0.04,0.07][0.06,0.09][0.05,0.07][0.000][0.000][0.000]

16181

16104

16173

16183

(P6) knowledge(P6E) knowledge + extra (endline)

β-coefficient [95%CI] [p-value]

β-coefficient [95%CI] [p-value]

0.395***

0.398***

0.396***

0.220***

0.220***

0.219***[0.24,0.55][0.25,0.55][0.24,0.55][0.14,0.30][0.14,0.30][0.14,0.30][0.000][0.000][0.000][0.000][0.000][0.000]0.401***

0.408***

0.402***

0.219***

0.224***

0.219***[0.25,0.56][0.25,0.56][0.25,0.56][0.14,0.30][0.14,0.31][0.14,0.30][0.000][0.000][0.000][0.000][0.000][0.000]

16084

16153

16163

SP-treatment

HE-group

HESP-group

SP-group

periodschool typesex

child age

wealth index-0.07-0.069-0.071-0.070+-0.071+-0.073+[-0.22,0.08][-0.22,0.08][-0.22,0.08][-0.15,0.01][-0.15,0.01][-0.15,0.00][0.366][0.376][0.357][0.071][0.070][0.061]-0.051-0.043-0.047-0.025-0.014-0.018[-0.19,0.09][-0.18,0.09][-0.19,0.09][-0.08,0.03][-0.07,0.04][-0.07,0.04][0.479][0.541][0.510][0.402][0.614][0.514]-0.085

-0.08

-0.08

-0.038-0.03-0.032[-0.22,0.05][-0.21,0.06][-0.22,0.06][-0.09,0.02][-0.08,0.02][-0.09,0.02][0.230][0.247][0.251][0.192][0.263][0.250]

0.016

0.028

0.026

0.001

0.014

0.011

[-0.12,0.16][-0.11,0.17][-0.11,0.16][-0.06,0.06][-0.04,0.07][-0.04,0.07][0.819][0.693][0.718][0.973][0.611][0.695]0.781***

0.817***

0.829***

0.758***

0.795***

0.808***[0.68,0.89][0.71,0.92][0.72,0.93][0.70,0.81][0.74,0.85][0.75,0.86][0.000][0.000][0.000][0.000][0.000][0.000]-0.021-0.016-0.021-0.034-0.027-0.033[-0.08,0.04][-0.08,0.04][-0.08,0.04][-0.08,0.01][-0.07,0.01][-0.07,0.01][0.514][0.594][0.501][0.127][0.175][0.102]0.022+

0.026*

0.023*

0.021**

0.025***

0.023**[-0.00,0.04][0.00,0.05][0.00,0.04][0.01,0.04][0.01,0.04][0.01,0.04][0.050][0.020][0.038][0.003][0.000][0.001]

0.056***

0.055***[0.05,0.06][0.05,0.06][0.000][0.000]0.059***

0.066***[0.04,0.07][0.05,0.08]

[0.000]
parents' literacy

N

16104

16173

[0.000]0.039***

0.028***[0.02,0.05][0.02,0.04][0.000][0.000]

16183

16104

16173

16183

Notes: Each column represents a separate regression on a family of outcomes applying seemingly unrelated regressions (SUR), estimated by a feasible generalised leastsquares (FGLS) estimator with cluster-robust standard errors (CRSE) except for (H2). β-coefficient is the mean-standardised effect from SUR applying the difference-indifferences (DID) model, controlling for school type, child sex and additional covariates, namely, child age, wealth index, and parent literacy. Wealth index is created throughiterated principal factor, reflecting house structure materials, roof materials, number of rooms, latrine structure and materials, possession of electronic appliances, mobile
phones and bikes. Parents’ literacy reflects whether the child mother and father can read. For each outcome family, estimates are provided for all sample children and children
present in both baseline and endline. Selected outcome families are those for which treatment effect was found to be statistically significant in HE-treatment analysis given
in S3 Table above; (P1) handwashing practice: handwashing habits (washing frequency in each occasion (before eating, after defecation, after playing); used substances(soap, ash, mud and/or water only); washing with soap in each occasion; wash with running water; correct washing procedure); (P2) dental care practice: dentalcare (frequency
and use of brush/branch, toothpaste, etc.), type of materials used; (P3) overall hygiene practice: shoes/footwear wearing at school (frequency), shoes/footwear wearing at
home (frequency in latrine and in courtyard), + P1 & P2; (P6) health/hygiene knowledge: handwashing procedure, breakfast significance; (P6E) health/hygiene knowledge +extra: additional knowledge measured only in the endline, i.e., putting water in latrine before defecating, oral rehydration solution (ORS) making, food pyramid. Significancelevel: + p<0.1, * p<0.05, ** p<0.01, ***p<0.001; 95% confidence intervals and p-value in brackets.

A-Table 5. HE-treatment effects on each child level outcome (selected outcomes
HE-Treatment Effects on Each Outcome at Individual-Level (Selected Outcomes)

Outcome(4) ANCOVA(5) ConstrainedBasein bothallin bothallin both-0.032*-0.033*-0.031-0.029-0.032**-0.030*[0.094][0.094][0.088][0.208][0.123][0.047][0.069]

-0.02

-0.02

-0.020-0.020-0.014-0.016-0.017*[0.179][0.313][0.313][0.191][0.190][0.178][0.163]-0.006-0.008*-0.011*-0.011*-0.008*-0.011**-0.006[0.078][0.132][0.078][0.067][0.067][0.090][0.040]-0.005-0.009-0.005-0.009-0.023**-0.023**-0.018**[0.467][0.255][0.466][0.254][0.043][0.043]-0.009-0.013-0.009-0.013-0.03[0.593][0.432][0.591][0.441]-0.007-0.008-0.007[0.372][0.330]-0.005(1) Pooled(1E) Endline(2) FE(3) DIDallin bothallin bothallin bothall-0.0200.016-0.0200.016-0.033*-0.033*[0.648][0.732][0.648][0.730][0.094]-0.014-0.016-0.0139-0.015[0.200][0.176][0.197]-0.006-0.008*[0.132](6) Logit
allin both-0.017

0.854

0.847

[0.088][0.137][0.216][0.212]-0.007*-0.006**-0.008**0.631

0.522*[0.121][0.083][0.044][0.020][0.176][0.095]-0.023**-0.006

-0.01

-0.01

-0.014*0.761

0.698**[0.043][0.020][0.4051][0.1945][0.1203][0.0618][0.108][0.040]-0.03-0.034-0.029-0.01-0.012-0.023*-0.0210.86

0.87[0.269][0.269][0.132][0.192][0.563][0.464][0.070][0.137][0.212][0.261]-0.008-0.014-0.014-0.012-0.013-0.007-0.008-0.009*-0.010*

0.68

0.641

[0.372][0.331][0.167][0.167][0.154][0.136][0.359][0.323][0.070][0.072][0.214][0.177]-0.005-0.005-0.005-0.011*-0.011*-0.010**-0.011**-0.005-0.006-0.007**-0.008*0.584*

0.564*[0.196][0.222][0.196][0.222][0.091][0.091][0.032][0.033][0.157][0.1741][0.047][0.065][0.094][0.086]-0.005-0.005-0.005-0.005-0.037-0.037-0.041**-0.037*-0.006-0.006-0.023*

-0.02

0.846

0.86

[0.749][0.777][0.749][0.786][0.174][0.174][0.050][0.093][0.662][0.740][0.070][0.167][0.118][0.181]-0.007-0.021**-0.007-0.021**-0.021-0.021-0.0127-0.021*-0.001-0.021**-0.008-0.021**0.899

0.804*[0.488][0.032][0.487][0.032][0.103][0.103][0.285][0.082][0.9071][0.0313][0.3032][0.0207][0.348][0.064]handwashingindex

0.372**

0.365**

0.372**

0.364**

0.518**

0.518**

0.512**

0.520**

0.392***

0.371**

0.487***[0.013][0.015][0.013][0.016][0.033][0.033][0.024][0.022][0.008][0.012]

handwashingindex before
eating*0.195**

0.198**

0.195**

0.1980**0.292**

0.292**

0.297***

0.295**

0.210***[0.013][0.015][0.013][0.016][0.018][0.018][0.009][0.011][0.006]weight z-score(net of clothes)coughbreathingdifficulty
fever

cough in 2wks
breathingdifficulty in
2wks
sore throat in
2wks
running nose in
2wks
diarrhea in
2wks(7) Ologit
allin both

0.483***

1.309*

1.299[0.000][0.000][0.098][0.102]0.207**

0.279***

0.272***

1.448*

1.414*[0.011][0.000][0.000][0.085][0.092]

handwashingindex after
defecation*
handwashingindex after
playing*

0.080**

0.077**

0.080**

0.077**

0.166**

0.166**

0.147**

0.158***

0.085**

0.076**

0.126***

0.135***

1.391**

1.420**[0.028][0.035][0.027][0.036][0.017][0.017][0.018][0.009][0.018][0.040][0.000][0.000][0.026][0.012]0.097

0.0886

0.097

0.088

0.061

0.061

0.06870.0689

0.094

0.086

0.075*

0.066

1.068

1.014

1.027

1

[0.278][0.324][0.278][0.327][0.626][0.626][0.552][0.549][0.292][0.335][0.086][0.183][0.799][0.957][0.878][0.999]handwashingwith soap
before eating

0.067**

0.068**

0.067**

0.068**

0.114***

0.114***

0.114***

0.113***

0.070***

0.069**

0.103***

0.101***

1.879***

1.860***[0.012][0.015][0.012][0.015][0.001][0.001][0.000][0.001][0.010][0.016][0.000][0.000][0.001][0.002]

handwashingwith soap after
defecation

0.032***

0.033***

0.032***

0.033***

0.064**

0.064**

0.056**

0.063***

0.033***

0.033***

0.047***

0.053***

1.560***

1.711***[0.008][0.007][0.008][0.007][0.018][0.018][0.016][0.008][0.0057][0.008][0.000][0.000][0.006][0.003]

handwashingwith soap after
playing

0.025

0.034

0.025

0.034

0.062**

0.062**

0.0480**0.061**

0.031

0.038*

0.042***

0.053***

1.554**

1.767**[0.251][0.139][0.250][0.140][0.016][0.016][0.038][0.012][0.160][0.095][0.000][0.000][0.041][0.010]

0.012

0.005

0.012

0.0054

0.023

0.023

0.025*

0.024

0.017

0.011

0.021***

0.017**

2.559

9.978**

1.455*

1.472[0.414][0.703][0.413][0.695][0.154][0.154][0.091][0.105][0.2404][0.4374][0.0052][0.0395][0.140][0.040][0.084][0.122]

handwashingprocedures

0.774***

0.821***

0.774***

0.820***

0.850***

0.850***

0.807***

0.838***

0.782***

0.815***

0.801***

0.843***

2.758***

2.933***

3.532***

3.796***[0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.003][0.004][0.000][0.000]

tooth brushingfrequency

0.071***

0.074***

0.071***

0.074***

0.094***

0.094***

0.091***

0.0236

0.069***

0.065**

0.087***

0.087***

2.007***

1.992***[0.006][0.008][0.006][0.008][0.005][0.005][0.002][0.105][0.007][0.018][0.000][0.000][0.007][0.008]dental-care
index

0.124***

0.113***

0.124***

0.113***

0.148***

0.148***

0.151***

0.087***

0.131***

0.115***

0.143***

0.134***

1.524***

1.493**

1.717***

1.713***[0.000][0.000][0.000][0.000][0.001][0.001][0.000][0.005][0.000][0.000][0.000][0.000][0.002][0.013][0.000][0.000]wearing
footwear atschool-0.0010.001-0.0010.001

0.033*

0.033*

0.02380.031**

0.003

0.005

0.017**

0.024***

1.2

1.367

[0.898][0.843][0.897][0.861][0.075][0.075][0.115][0.043][0.6685][0.3400][0.0226][0.0046][0.409][0.184]wearing
footwear at
home

0.105***

0.085**

0.105***

0.084**

0.164**

0.164**

0.155***

0.155***

0.105***

0.075**

0.137***

0.129***

3.032***

2.780**

1.324**

1.352**[0.005][0.020][0.005][0.020][0.014][0.014][0.007][0.006][0.005][0.038][0.000][0.000][0.010][0.042][0.035][0.025]water latrine
beforedefecate (a)0.069*

0.080*

0.069*

0.080*

0.069*

0.07

1.13

1.189*

1.168*

1.196*[0.087][0.055][0.087][0.055][0.089][0.100][0.166][0.073][0.085][0.055]0.238***

0.259***

0.238***

0.259***

0.238***

0.252***

1.726***

1.740***

1.713***

1.728***[0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000]handwashingwith runningwater

ORS making
knowledge(a)-0.006-0.004-0.006-0.0042

0.037

0.037

0.043*

0.035

0.135***-0.0040.026***

0.023**

0.891

0.813

[0.173][0.189][0.171][0.182][0.172][0.172][0.079][0.136][0.000][0.1928][0.0060][0.0450][0.648][0.506]breakfast eaten
today-0.00120.003-0.00120.0027-0.026-0.026-0.0302-0.0251

0.098***

0.004

-0.01

-0.0050.523**

0.442***

0.906

0.929

[0.937][0.869][0.937][0.870][0.615][0.615][0.115][0.229][0.0000][0.8328][0.4249][0.7170][0.028][0.009][0.256][0.432]cleaned school
latrine0.095***

0.109***

0.095***

0.109***

0.194

0.194

0.104***

0.111***-0.0010.108***

0.101***

0.113***

1.725***

1.743***[0.000][0.000][0.000][0.000][0.307][0.307][0.000][0.000][0.930][0.000][0.000][0.000][0.000][0.000]seen otherpupils clean
school latrine

0.136***

0.118***

0.136***

0.117***

0.109***

0.109***

0.131***

0.222***

0.003

0.116***

0.132***

0.111***

1.833***

1.685***[0.000][0.000][0.000][0.000][0.002][0.002][0.000][0.001][0.6103][0.000][0.000][0.000][0.000][0.001]

swimming
frequency-0.059-0.042-0.059-0.042-0.024-0.024-0.039-0.026-0.046-0.032-0.043*-0.026

0.937

0.99

0.912

0.958

[0.271][0.451][0.271][0.445][0.606][0.606][0.363][0.574][0.278][0.474][0.073][0.331][0.563][0.932][0.351][0.680]breakfastsignificant

Note: HE-treatment effects are given for each outcome shown in the first column for separate regression models (1) ~ (6), with significance level * p<0.10, ** p<0.05, *** p<0.01,
and p-value in brackets. Models (1) pooled OLS, (1E) endline OLS, (2) child-level fixed effects (FE); (3) DID estimation with multilevel random-effects (RE) , (6) logit and (7)ordered-logit (ologit) apply cluster-robust standard-error (CRSE).; models (4) Analysis of Covariance (ANCOVA) and (5) Constrained Baseline Analysis (CBA) control for
intracluster correlation by adjusting for baseline cluster-mean, and (4) in both and (5) additionally adjust for individual-mean (for variable available only in endline, only clustermean adjustment is applied). Estimates for logit model and ordered-logit (ologit) are estimated in DID form and results shown are exponentiated (odds-ratio) ; logit results are also
shown for ordered variables for valid estimates with sufficient 0 observations. The number of observations shown is general number and may vary by specific outcome variables.
(a) ORS making knowledge is available only in endline. For ANCOVA all estimation, the presented results are adjusted for baseline cluster mean as well as individual baseline
value.
Model Specification: Estimations are conducted for (1) pooled and (1e) endline only data by OLS estimator, (2) child-level FE and (3) DID estimation with child-level RE using
maximum-likelihood (ML) estimator. For binary or ordered response variables, (6) logit and (7) ordered-logit (ologit) estimations by maximum likelihood estimator are also applied.
While models (1)~(3), (5) and (6) apply CRSE, as pointed out by Angrist and Pischke (2009), Cameron and Miller (2015), and others, CRSE is not a panacea. McKenzie (2012) points
to the fact that with a single baseline and follow-up, it would require twice the sample size in DID to get the same power as obtained with ANCOVA. The benefits and procedures ofapplying ANCOVA as well as baseline-constrained model are highlighted in a simulation by Hooper et al. (2018). As detailed in McKenzie (2012), instead of DID which attempts tofully control for the non-random differences in baseline that will likely jeopardize predictive power, we apply Analysis of Covariance (ANCOVA) estimation which is reported to be
more efficient that DID or post estimator which uses only the endline data (Frison and Pocock 1992).
(1𝐴) 𝑌!"# = 𝛿 ∙ 𝑡 + 𝛽 ∙ 𝑇!"# + 𝜑 ∙ 𝑌/"+ 𝜐 ∙ 𝑌! #$% + 𝜀!"# ,#$%This method can adjust for the baseline cluster mean 𝑌/" #$% or baseline individual value of the outcome variable𝑌! #$% , or both baseline cluster mean and baseline individual value formore precise results (Klar and Darlington 2004; McKenzie 2012; Hooper et al. 2018). As an alternative to ANCOVA, Constrained Baseline Analysis (CBA) is also applied whichallows for random effects of cluster as well as individual nested within cluster. CBA can be applied to all pupils at the endline regardless of having their data taken at the baseline(Hooper et al. 2018). In order to discern possible effects of attrition and replacement, as well as additional class 1 inclusion, we also present the results of estimation over children of
closed cohort who were present in both the baseline and endline surveys (in-both).