Tsukuba Economics Working Papers
No. 2022-003

The impact of access to improved sanitation facilities on child health in Pakistan
byHanif Ammazia
Yuko Nakano
Midori Matsushima
October, 2022

UNIVERSITY OF TSUKUBA
Faculty of Humanities and Social Sciences
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPAN

i

The Impact of Access to Improved Sanitation Facilities on Child
Health in Pakistan

Hanif Ammazia*, Yuko Nakano** & Midori Matsuhima***

Corresponding author: Program in Economic and Public Policy, Graduate School of Humanities

and Social Sciences, University of Tsukuba, Japan. E-mail address: s2230029@u.tsukuba.ac.jp**

Faculty of Humanities and Social Sciences, University of Tsukuba, Japanii

Abstract
Poor sanitation is a major public health issue linked to various significant health outcomes. Several
studies have associated poor sanitation with malnutrition and childhood diarrhoea. Improved
sanitation, however, is determined by household decisions, which may induce endogeneity. Such
endogeneity of household sanitation choices has been insufficiently explored in most of the previous
literature. Using the Pakistan Demographic and Health Survey (PDHS), we examine the impact ofimproved sanitation on children’s height-for-age, weight-for-age, and weight-for-height z scores, aswell as diarrhoea. We address potential endogeneity using an instrumental variable approach. Ourfindings highlight the significance of better domestic sanitation in improving child health in Pakistan:
improved sanitation was found to positively and significantly affect children’s growth, mainly
height-for-age and weight-for-age in those below five years old. In contrast, no significant impact
was identified on weight-for-height and diarrhoea prevalence. The sub-sample analysis showed thatparticularly girls, children older than two years, children with uneducated mothers, and those fromhouseholds with poor economic status are positively and significantly affected by access to improved
sanitation facilities. Our results were robust throughout different model specifications. We suggestthat policies concerning the provision of and enhanced access to improved sanitation are effective in
reducing child malnutrition.

Keywords: Child Health, Malnutrition, Diarrhoea, Sanitation Sources, Endogeneity, Pakistaniii

1. Introduction
Malnutrition is considered a substantial public health concern, especially in developing
countries, and it may impact human capital accumulation and impair cognition throughout life
(Farid et al., 2013; Galler et al., 2012). Globally, 149.2 million children were found to be
stunted (low height-for-age) and over 45.4 million under the age of five were wasted; morethan 50% of all wasted children live in Southern Asia (UNICEF, WHO, & World Bank, 2021).
Ending hunger and malnutrition is one of the United Nations Sustainable Development Goals(SDGs). Various interrelated factors are considered to be associated with malnutrition,
including persistent diarrhoea, insufficient breastfeeding, deficiency of nutritious food, andintestinal infections that weaken a child’s nutritional levels (WHO, UNICEF, & World Bank,
2012).
Pakistan is no exception. Malnutrition and child mortality are serious health concerns
in the country. Stunting (height-for-age z-score (HAZ) <-2) in the country has worsened andremained at the global critical level of 40.2% in 2018 (National Nutrition Survey, 2018).
Therefore, the stunting rate is far higher than the Asia region average of 21.8% (UNICEF,
WHO, & World Bank Group, 2021). The prevalence of wasting (low weight-for-height)
among children under age five is also rising, with 17.7% of children in Pakistan sufferingfrom this (weight-for-height z-score (WHZ) <-2), the highest rate in the country’s history. The
prevalence of underweight (weight-for-age z-score (WAZ) < -2) children is 28.9% (National
Nutrition Survey Pakistan, 2018). Despite the improvements in various socioeconomic
indicators, malnutrition continues to represent a nutrition emergency in the country.
One possible solution to reduce malnutrition and improve child health could be
improved sanitation facilities. This is because studies have found human faeces to be asignificant reservoir for various pathogenic bacteria and soil-transmitted helminths (STHs),
which may cause trachoma, diarrhoea, and environmental enteric disorder (EED) among4

children (Mara, Lane, Scott, & Trouba, 2010) and lead to malnutrition. The transition fromopen defecation (OD) to toilet /flush toilet use can contribute by not only offering non-healthbenefits like saved time (Wang & Shen, 2022) and increased satisfaction but also reducing
child mortality and malnutrition (Dickinson, Patil, Pattanayak, Poulos, & Yang, 2015; Geruso& Spears, 2018; Rahman et al., 2020). Studies have revealed that sanitation investments havepositive impacts on health outcomes by reducing the exposure to faecal pathogens that occurs
due to inappropriate containment of faecal matter (Duflo, Greenstone, Guiteras, & Clasen,
2015; Hammer & Spears, 2013).
Various studies have been conducted to examine the relationship between improved
sanitation, child health, and other outcomes, but the results have been inconsistent. Some haveobserved that improved sanitation can lead to considerable health benefits (Augsburg &Rodríguez-Lesmes, 2018; Bekele, Rahman, & Rawstorne, 2020; Cameron, Olivia, & Shah,2019; Cameron et al, 2021; Dickinson et al., 2015; Fink, Günther & Hill, 2011; Rahman et al.,2020; Spears, 2020; Spears & Lamba, 2016; Spears, Ghosh, & Cumming, 2013; Vyas, Kov,
Smets, & Spears, 2016), educational achievements (Adukia, 2017), and reduced risk ofviolence towards women (Hossain, Mahajan, & Sekhri, 2022). Freeman et al. (2017) andHeadey and Palloni (2019), however, found no significant association between sanitation
facilities and child nutritional outcomes.
In addition to these works, several studies have associated poor sanitation withdiarrhoea prevalence among children, finding that water, sanitation ans hygiene (WASH)
investments are generally considered important for improving early childhood health. Most
diarrhoea prevalence is considered to result from contaminated environments. Poor sanitation
infrastructure and drinking water contaminated with animal and human faeces containpathogens, which enter the body through several faecal-oral passageways and cause diarrhoea(Andrés, Briceño, Chase, & Echenique, 2017; Adane, Mengistie, Kloos, Medhin, & Mulat,
2017; Duflo et al. 2015; Fink et al., 2011; Kumar & Vollmer, 2013; Patil et al., 2014; Santika
5

et al., 2020; Usman, Gerber & Braun, 2019; Wasonga, Okowa, & Kioli, 2016). The major
limitation of the existing research, however, is that most have failed to consider theendogeneity of household choices regarding sanitation facilities.
The few exceptions include Augsburg and Rodríguez-Lesmes (2018), who empiricallyexamined the impact of sanitation coverage on child height-for-age by using the price of the
raw materials needed for toilet construction as an instrumental variable (IV). Geruso andSpears (2018) examined the relationship between neighbourhood sanitation (open defecation)and infant mortality using an IV approach. Other exceptions include the studies based on
randomised controlled trials (RCTs), but their results are also mixed. Meanwhile, Dickinson etal. (2015) and Pickering, Djebbari, Lopez, Coulibaly, and Alzua (2015) identified significant
impacts of sanitation on children’s height-for-age, weight-for-age, stunting, and underweight,
but sanitation was found to have an insignificant impact on diarrhoea. Other RCT-based studies
observed that it had no impact on child health, height, or weight (Cameron et al., 2021; Clasen
et al., 2014; Patil et al., 2014).
The purpose of this study is to examine the impact of access to improved sanitation
facilities on child health, focusing on height-for-age, weight-for-age, weight-for-height, anddiarrhoea, by using cross-sectional data from the Demographic Health Survey (DHS) 2017-18
for Pakistan. In examining the impact of improved sanitation, it was recognised that thecorrelation between the error term and health-related outcomes may be caused byunobservables, including knowledge, beliefs, and attitudes about toilet usage, as well as themother’s health status. Whereas previous studies have largely failed to consider such facts, weaddress the endogeneity of household sanitation choices by employing an IV approach. We
used the slope of the homestead and the average improved sanitation coverage at the cluster
level as the instruments for household sanitation facilities. Height-for-age, weight-for-height,

6and weight-for-age z-scores1 among children under five years old were used as child growthindicators. These conventional nutritional indicators are employed as proxies for stunting,underweight, and wasting, respectively (Wang & Chen, 2012).
Having access to improved domestic sanitation facilities was found to result in an
average increase in children’s height-for-age by 0.43 standard deviations and weight-for-age
by 0.32 standard deviations. It had no significant impact, however, on weight-for-height anddiarrhoea prevalence. This implies that child health is also damaged by routes other thanclinical disorders such as diarrhoea. Recent research implies that environmental entericdysfunction (EED), a sub-clinical gut condition which spreads rapidly following exposure tofaecal-contaminated water and soil, as well as poor sanitation access, has severe impacts onchild growth than through diarrhoea, which reduces the absorption of significant nutrients.
Whilst inconclusive, our analysis provides suggestive evidence through its mechanism toaugment the growing view that improved domestic sanitation impacts child health.
In addition, four sub-sample analyses were undertaken that considered the differential
effects of improved sanitation on child health, based on the children’s age and gender, themothers’ education, and the household economic status. This approach revealed that sanitation
matters little for children below two years old who do not use sanitation facilities, while itsignificantly affects children older than two years old. The current findings also suggest thatgirls in particular benefit significantly from improved sanitation in terms of increased weightfor-age and weight-for-height, while such tendencies were not observed for boys. In terms ofheight-for-age, however, we found few differences between girls and boys regarding theeffectiveness of improved sanitation. Moreover, the findings showed that children from poor
households are more likely to be positively affected than those from average and above-1

Z-score= Most commonly used reference standard recommended by the WHO for international use. A zscore below -2 reflects stunting, wasting, or underweight; a z-score below -3 reflects severe malnutrition.

7

average income households. These results imply that vulnerable social groups are more likely
to benefit from improved sanitation. These sub-sample analyses add value to this study byproviding these insights and highlighting the importance of policy interventions, which were
not rigorously examined in previous research.
The remainder of the paper is organised as follows: Section 2 explains the data set anddescriptive analyses. Section 3 discusses the econometric estimation strategy. Section 4
presents the estimation results, while Section 5 provides a discussion. Section 6 concludes thestudy.

2. Data and Variables
2.1. Data
Our study utilised data from the fourth round of the nationally representative Pakistan
Demographic Health Survey (PDHS, 2017-18). During the survey, 561 enumeration blocks(hereafter called clusters) were randomly selected, and interviews were conducted with themembers of 14,540 households (approximately 28 from each cluster) from urban and rural
areas across 143 districts of Pakistan. The data includes information on 12,708 children aged
under 60 months, as well as their mothers or caregivers. For the PDHS 2017-18, one-thirdof the households were selected for anthropometric measurement of the children at the timeof the survey. Therefore, our analysis was based on 4,041 children aged 0-59 months whose
height and weight were measured. In the PDHS, the enumerators collected geospatial
information with a global positioning system (GPS) device to record each cluster’s central
position. Displacements of 0–5 km in urban areas and 0–2 km in rural areas were added
randomly to the GPS information for confidentiality called “geo-scrambled” or “geomasked”.

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2.2. Variable Construction
Child growth is measured by three indicators: height-for-age, weight-for-age, andweight-for-height; these measures were operationalised as z-scores scaled to the WHO Child
Growth Standards (WHO, 2019; de Onis, Onyango, Borghi, Garza, & Yang, 2006). A childis considered stunted if their height-for-age is less than -2, wasted if their weight-for-height isless than -2, and underweight if their weight-for-age is less than -2. Stunting (low height-forage) is generally referred to as a sign of long-term chronic undernutrition, a measure of linear
growth obstruction and cumulative growth failure that illustrates a history of problematic
nutritional and health issues. Wasting (low weight-for-height) is a short-term indicator of
acute malnourishment. Underweight (weight-for-age) is a composite index of height-for-age
and weight-for-height. We also constructed a binary indicator which took a value of one if ahousehold reported diarrhoea prevalence for a child in the two weeks before the survey.
The main explanatory variable is the improved sanitation binary variable, which took
a value of one if the sanitation facility was improved, according to the sanitation
technologies classification devised by the WHO/UNICEF Joint Monitoring Program for
Water Supply, Sanitation and Hygiene (JMP) 2017. Improved sanitation facilities include
flushing/pour flushing to piped sewer systems, septic tanks or pit toilets, compositing toilets,
ventilated improved pit toilets, and pit toilets with slabs. An unimproved sanitation facility
comprises the use of a bush or field, no facility, pit toilets without slabs, open pit toilets,
hanging toilets, bucket toilets, compositing or flush toilets to open drains, and un-sewered
toilets. Our data shows that about 70% of households in Pakistan use improved sanitation
sources, while about 11% still practice OD. The fact that households make their own choices
regarding the use of sanitation facilities may have caused bias in our estimation and led tothe impact of sanitation on child health being overestimated.
To address the possible endogeneity of sanitation sources, the IV approach and two
9instruments were used, including the cluster-level average improved sanitation coverage andthe homestead slope. We measured the improved sanitation variable at the cluster level by
the proportion of households in the cluster, excluding particular households (leave out mean).
Cluster-level GPS data was used to measure the slope of each area. If a village had a certain
infrastructure such as a sewerage and drainage system, it would be easier for local
households to invest in improved sanitation facilities. Thus, we expected to observe a
positive correlation between improved sanitation at both household and village level. The
homestead slope was expected to be correlated with improved sanitation, since a sanitation
system works more effectively when the slope is steeper due to stronger gravity. To protect
personal information, the DHS does not include GPS information about the exact locationsof homesteads, so the slope gradient variable was calculated based on the cluster-level data
provided by the DHS Program. Households could only be located within each cluster.
We used a natural shape file in QGIS containing information about waterways andrivers, along with administrative data about the regions and districts of Pakistan taken from
DIVA-GIS. We used slope raster data for Pakistan provided by the Japan Aerospace
Exploration Agency (JAXA) 2 . This database is a Global Digital Surface (DSM) model
which provides worldwide raster data at resolutions of around 30 metres (basically 1 arcsecond). Figure 1 shows the cluster locations obtained from the PDHS and Pakistan
administrative data. The slope of each cluster was calculated using the QGIS point sampling
tool and based on the elevation rater data and cluster information.
[Figure 1]
In addition, various child, mother, and household characteristics reported in the data
were included in the analyses for controlling the observed differences among children. The
children’s characteristics include their age, birth size, gender, and delivery place, as well as2

https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm

10whether the child was desired or not at the time of pregnancy. Furthermore, a measure of thechildren’s dietary diversity was constructed using food intake information representative of
the children under five years old in the PDHS. Dietary diversity measured how many typesof food were consumed by the child in the 24 hours before the survey. A list of the fooditems consumed by all the children was arranged into eight different categories following
WHO guidelines (2017) and the number of food categories they consumed was counted.3
The mother and household characteristics included the mother’s age, education, working
status and nutrition status (BMI); the household head’s age, gender and education; theinterview month, the household size, the number of children under five years old, the timeneeded by the household to collect water, the water source used by the household, the water
treatment status for drinking water, 4 the livestock ownership, cooking fuel, and thehousehold’s economic status. The community characteristic included an urban or rural
dummy. Appendix Table 1 provides details of the summary statistics of the variables usedin the analyses.

3. Methodology
To estimate how the household use of improved sanitation impacted child health, a
two-stage least square (2SLS) estimation strategy was used to control for the possible
endogeneity caused by the household’s selection of sanitation facilities. A correlation

3

The eight categories include: 1) Bread, grain, and tubers, 2) Lentils, peas, and nut products, 3) Dairy food(cheese, milk, and yoghurt), 4) Meat products, 5) Eggs, 6) Vitamin-A-rich vegetables and fruit, 7) Green
leafy vegetables and other fruits, 8) Breastmilk. Binary variables were created for all the food groups. A
value of “0” indicates that the food consumed by the child is different from our list of eight categories. Thismeasure has been validated and accepted for constructing dietary diversity and food adequacy (WHO, 2017;
Darapheak, Takano, Kizuki, Nakamura, & Seino et al., 2013; Hirvonen, 2016).

Drinking water sources were categorised into improved and unimproved. The variable takes “1” if thewater source is improved (by the nature of its construction or if it is protected from external contamination).
For the water treatment status, we created a dummy variable which takes “1” if the household used any
water treatment method to treat water for drinking purposes, including boiling, chlorination, filtration, solar
disinfection, or straining through a cloth.
411

between the error term and sanitation facilities could have been caused by unobservables
including knowledge, beliefs and attitudes about toilet usage, as well as the mother’s healthstatus. This might have affected both the type of sanitation facility and child health.
The following regression equations were estimated. Equation 1 is the first stage of theregression and equation 2 is the second.

Sanitationjk = π0 + π1 Cluster_sanitationk + π2 Slopek + øHjk + γNk + δ𝑖 + vjk(1)

Yijk = β0 + β1 Sanitationjk + γHijk + θNk + δ𝑖 + εijk

(2)

Where Yijk is the outcome variable indicating the health of child i in household j incommunity k, including height-for-age, weight-for-age, and weight-for-height as continuous
variables and diarrhoea prevalence as a dichotomous variable. Sanitationjk is a
discontinuous endogenous variable of having improved sanitation at the household level.
We included district fixed effects (δ) and used clustered standard errors at the Primary
Sampling Unit (PSU) or cluster level. 𝐻𝑖𝑗𝑘 is a vector for the basic child, mother, andhousehold characteristics, as explained above. 𝑁𝑘 is a community characteristic and includes
the dummy variable, which takes the value of one if it is a rural location. εijk is an error term.
As discussed, the instruments used included the average improved sanitation
coverage of the cluster and the homestead slope. Village- (or cluster) level improved
sanitation coverage would positively affect a local household by enabling access to improved
sanitation facilities. This is because when a village has a certain infrastructure, such as asewerage and drainage system, households find it easier to invest in improved sanitation
facilities. In addition, the average improved sanitation coverage cannot be determined byeach household, suggesting that the variable can satisfy exclusion restrictions. The slope
variable could also be correlated with improved sanitation. If a slope is steeper, the sanitation
system works more effectively due to better gravity flow, which may incentivise a household
to invest in improved sanitation. Moreover, the homestead slope cannot be changed unless
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a household moves, which is not easy, at least in the short term. Therefore, these wereconsidered suitable instruments for this study. Reduced-form estimates are presented laterin the results section to illustrate the links between these instruments and child health.
Various robustness checks were also conducted to examine the validity of the instruments.

4. Results
4.1. Descriptive Analyses
Table 1 compares the outcome variables of children with and without improved
household sanitation facilities. The data shows that the average height-for-age, weight-forage, and weight-for-height are lower for households using unimproved sanitation facilities
than for those using such improvements. On average, height-for-age is -2.028 for children
from households using unimproved sanitation and -1.364 for those using improved sanitation.
The mean height-for-age variable is -1.559 (for the total sample), indicating that, on average,these children were shorter for their age compared to the WHO reference population of thesame sex and age. A similar situation was observed for the other malnutrition indicators, i.e.,weight-for-age and weight-for-height. On average, the diarrhoeal prevalence was 0.209
among children from households using unimproved sanitation and 0.188 for those whose
homes had improved sanitation. The mean comparison test between the households using
each type of sanitation also showed statistically significant differences between them for thevariables of height-for-age, weight-for-age, weight-for-height and diarrhoea.
[Table 1]
4.2 Main Results
The estimation results concerning the impact of improved sanitation on child heightfor-age, weight-for-age, and weight-for-height, as well as diarrhoea prevalence, are13

presented in Table 2. The first-stage estimates are shown in Panel A. The cluster-levelaverage improved sanitation and homestead slope values have strong positive correlations
with improved sanitation. The first-stage F-statistics for the set of excluded instruments
remained over 10, suggesting that weak instruments did not bias our estimates. Furthermore,
an over-identification test of the instruments was conducted, which mainly led to the null
hypothesis not being rejected, suggesting that at least one of the instruments is exogenous.
To explain the correlation between these instruments and the height-for-age, weightfor-age, weight-for-height, and diarrhoea prevalence outcome variables, the reduced-form
estimates are presented in Appendix Table 2. The results show that a correlation exists
between our instruments and child health, which may have arisen through household
improved sanitation. Appendix Figure 1 presents the Frisch Waugh Lovell (FWL) theorem
results, where the residuals of the first-stage estimation are plotted on the horizontal axes,
while the residuals of reduced-form estimation and the outcome variables are plotted on thevertical axes. The results suggest that these instruments are valid and that our model islogically defined.
In Panels B and C of Table 2, the authors use OLS and 2SLS to report the estimatedcoefficient of interest β1 on the impact of improved sanitation facilities on the children’sheight-for-age, weight-for-age, weight-for-height, and diarrhoea prevalence. The OLS
results, shown in Panel B, demonstrate the positive and statistically significant relationships
between improved sanitation and both height-for-age and weight-for-age. Conversely, thecoefficients of improved sanitation on weight-for-height and diarrhoea are statisticallyinsignificant.
Panel C reports the 2SLS estimation results. While accounting for endogeneity by
instrumenting with the cluster-level improved sanitation coverage and homestead slope, thecoefficients for the children’s height-for-age and weight-for-age were found to be significant,14

at 10%. The estimated coefficients from the IV regressions, shown in columns 1 and 2,
suggest that having improved sanitation facilities resulted in the children’s height-for-age
being higher by 0.43 standard deviations and their weight-for-age being higher by 0.32
standard deviations. In contrast, columns 3 and 4 indicate the insignificant effects ofimproved sanitation on a child’s weight-for-height and diarrhoea. Generally, the 2SLS
estimates are consistent with the OLS estimates, suggesting the robustness of our results (seeAppendix Table 3 for full results).
[Table 2]
4.3 Heterogeneous Impacts of Improved Sanitation
Within the empirical analyses, the authors examined whether the effects of sanitation
were heterogeneous across different child age groups and households of different
socioeconomic status. More specifically, the impact of improved sanitation on child healthwas compared by the children’s age and gender, the mothers’ education, and the economic
status of the household.
First, we focus on the age of the children. Heterogeneity may exist based on theirage because sanitation sources do not affect all children in the same manner. Table 3 presents
the results of the sub-sample analyses based on each child’s age. The estimated significant
coefficients of 0.60 and 0.46 (in columns 5 and 6, respectively) from the 2SLS regressions
for children older than two years suggest that having improved sanitation facilities resulted
in significantly higher children’s height-for-age and weight-for-age. In contrast, columns 1
and 2 indicate the insignificant effects of improved sanitation on children’s height-for-age
and weight-for-age for those younger than two years of age. Similar to our main results, theeffects of improved sanitation on weight-for-height and diarrhoea were found to be
insignificant for both child age groups.
[Table 3]

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Secondly, we focus on the gender of the children. Since families in Pakistan generally
prefer boys to girls, improved sanitation facilities may have different impacts on boys andgirls. The results of the gender-based sub-sample analysis are presented in Table 4. In PanelA, the OLS and 2SLS estimates are reported for female children. Columns 1 and 2 for the
OLS estimates show the positive and significant impacts of access to improved sanitation
facilities on girls’ height-for-age and weight-for-age. The 2SLS estimates (shown in columns
2 and 3) indicate the positive and significant impacts on girls’ weight-for-age and weightfor-height. In Panel B, the OLS estimates for height-for-age are significant at 5% for malechildren. The coefficient became insignificant, however, for the 2SLS estimates. Columns 3
and 4 show the insignificant effects of improved sanitation on weight-for-height anddiarrhoea prevalence for boys, which is consistent with our full sample analysis.
[Table 4]
What potential reasons could explain the different impacts of improved sanitation onmale and female child health? The major share of the gender inequalities in South Asia canbe attributed to nutritional imbalances. Boys are believed to receive more vitamin supplements
and childcare time, and they are more likely to be breastfed and vaccinated (Augsburg &Rodríguez-Lesmes, 2018). Previous studies also show that boys are more likely to receive
immunisation and better access to medical professionals (Hazarika, 2000). In our data set,boys and girls were also compared regarding access to healthcare facilities and nutrition(Appendix Table 4). The results indicate that boys are more likely than girls to be immunised,have better vaccination status, receive better medical services in cases of diarrhoea and fever,and be given diverse types of dairy food. Furthermore, as shown in Appendix Table 5, OLS
regression was run to examine the association between the child’s gender and parental
investment behaviour regarding child healthcare and nutrition. The estimated results highlight
how girls are less likely than boys to receive diverse types of food (particularly dairy), medical
16

treatment, or any treatment given for diarrhoea and fever.
Table 5 displays the heterogeneous effects based on the wealth status of the households.
The wealth quantiles reported in the PDHS were categorised into below-average (the poorestand poor) and average or above-average (the middle, richer, and richest) households. Panel A
shows the results of the OLS and 2SLS estimates for the poor households. These results
illustrate that, generally, the height-for-age and weight-for-age of children from householdsof below-average economic status were significantly affected by improved sanitation facilities.
Meanwhile, column 3 shows that our results remained insignificant in regard to weight-forheight. Column 4 in Panel A shows the estimated coefficient of improved sanitation on
diarrhoea for children. Improved sanitation was observed to be positively associated with thediarrhoea prevalence of children from poor households, for which no satisfactory explanations
can be given. Panel B presents the results of the households of average and above-average(rich) economic status. We found the effects of improved sanitation on non-poor households
to be insignificant. Overall, the results are consistent with our main analyses in showing that
improved sanitation positively affects height-for-age and weight-for-age and these effects canmainly be identified in poor households.
[Table 5]
The authors also tested whether the mother’s education level influenced the impact ofsanitation facilities on child health. The mother’s education was defined as a dummy variablewhich took a value of one if the mother had completed education at primary level or above.
Educated mothers could be expected to have the capacity to protect their children from diseaseenvironments and health issues by having better awareness and knowledge of handwashingand sanitation-related hygiene practices (Olubukola, 2014). Columns 1 and 2 of Table 6 show
the estimated coefficients of 1.20 and 0.48 from the 2SLS regressions, suggesting that having
improved sanitation facilities resulted in significantly higher height-for-age and weight-forage for households where the children had uneducated mothers. In contrast, columns 5 and 6
17

indicate the insignificant effects of improved sanitation on the height-for-age and weight-forage of children of educated mothers. Similar to our main results, neither educated nor
uneducated mothers appeared to significantly affect weight-for-height or diarrhoea.
[Table 6]
4.4 Robustness Checks and Sensitivity Analysis
Several checks were conducted to examine the robustness of the main results. First,
the authors examined the sensitivity of the coefficients to the control variables selected. The
main results included as controls were the characteristics of the child, the household head,the mother, and the household. For the robustness check, certain control variables were
excluded. The results, shown in Appendix Table 6, demonstrate that the size of thecoefficients and the statistical significance were stable across different estimations,suggesting the robustness of our results.
The second robustness check was related to the omitted-variable bias and theexclusion restriction of the IVs. One of the potentially most important omitted variables inour estimation was the drinking water quality, since this may directly affect child health.
Nevertheless, poor sanitation facilities may affect the quality of the drinking water becauseof the discharge of microbial and chemical contaminants from pit toilets into groundwater,
as well as the dumping of faecal sludge into rivers and other open places, can contribute towater pollution. Therefore, if access to clean water had been included in the error term, thismight have caused omitted-variable bias in the estimates. To determine the extent to which
our IV estimates were sensitive to these controls, both regressions were run with and without
the household drinking water source and water treatment-related covariates. The coefficients
reported in columns 5-8 in Appendix Table 7 show that the results with and without theaccess to drinking water related control variables are almost identical in terms of thesignificance and size of the coefficients, suggesting the robustness of our results.
Thirdly, the authors examined the mechanism through which improved sanitation18

affects child health. It was assumed that improved sanitation would positively impact childhealth through reductions in bowel infections and gut disorders. However, improved
sanitation was found to affect child health possibly in other ways. To examine suchpossibilities, coughing, the common cold, fever, and breathing issues were constructed asdummy variables, based on the PDHS. Appendix Table 8 illustrates the impact of improved
sanitation on these other child health issues. The results in columns 1-4 show that improved
sanitation has insignificant effects on these health issues, which could mediate child growth.

5. Discussion
The authors examined the impact of access to improved sanitation facilities on childhealth by addressing the endogeneity of household access to sanitation. The results suggestthat children are less likely to suffer from malnutrition issues if they have access to improved
sanitation facilities, which was mainly reflected in the height-for-age and weight-for-age. In
contrast, improved sanitation was found to have insignificant effects on weight-for-heightand diarrhoea prevalence. The results were confirmed as robust in different model
specifications.
One unexplained aspect of our results is that improved sanitation affects child healthwithout diarrhoea prevalence. A possible explanation for this is that increased household
access to improved sanitation might reduce the prevalence of intestinal worm infections,which can cause malnutrition among children. An alternative explanation is that lessenvironmental faecal contamination might contribute to lower environmental enteropathy
among children (a sub-clinical disorder), resulting in better nutrient absorption in the gut andimproved child growth. In fact, Humphrey (2009) and Mbuya and Humphrey (2016) foundthat environmental enteric dysfunction (EED) may hamper a child’s growth more severely
than diarrhoea. One limitation, however, is that the diarrhoea variable captured diarrhoea
incidence only in the two weeks preceding the survey. Moreover, diarrhoeal illness was19measured for this survey mainly during the dry season, and improved sanitation may havebeen found to affect diarrhoea risk differently if the rainy season had been included or if
long-term diarrhoea prevalence had been measured. Therefore, the mechanism throughwhich improved sanitation facilities enhance child growth remained uncertain.
Overall, our results corroborate the findings in the existing literature. In terms ofmagnitude, the estimated impacts identified in this study are comparable to the findings ofDickinson et al. (2015) in India, where improved sanitation was found to increase heightfor-age by 0.37–0.52 standard deviations and weight-for-age by 0.26–0.31 standard
deviations, while diarrhoeal illness was not reduced significantly. Hammer and Spears(2013) found that an increase in toilet ownership of 8.2 percentage points increased heightfor-age by 0.3–0.4 standard deviations for children aged four years old in Madhya Pradesh,
India. Moreover, the findings of Pickering et al. (2015) also suggest that improved accessto sanitation does not significantly prevent child diarrhoea. The results of our study are
consistent with those of previous researchers, in that a poor household sanitation
environment was found to pose a risk to child growth failure, but it had no statisticallysignificant impact on diarrhoea prevalence.
While examining the heterogeneous effects of improved sanitation with regard togender, different child age groups, and varying household socioeconomic status, it was
found that children older than two years benefitted more from improved sanitation. This may
have been because children under two do not use the toilet and are less likely to move aroundoutside their dwelling. In addition, children are usually breastfed at this younger age, so they
benefit from the antibodies in breastmilk.
Our findings for the gender-based heterogeneous effects reveal that the positive
impacts of improved sanitation on weight-for-age and weight-for-height were mainly driven
by the effects on girls. Potential reasons were sought for the heterogeneous impacts ofimproved sanitation on male children compared to females, and the results indicate that girls
20

are less likely than boys to receive diverse types of food (particularly dairy food), medical
treatment, or any treatment for diarrhoea or fever. Such preferential nutrition and healthinvestments in boys might explain why improved sanitation matters more for girls. Since
male children receive better healthcare and nutrition treatment, their growth may not be
substantially hindered, even if the household is not equipped with improved sanitation
facilities.
The heterogeneous effects in regard to rich and poor households imply that improved
sanitation has insignificant effects on non-poor households. One reason could be that over
90% of the above-average economic status households were already using improved
sanitation and their sanitation use did not vary. Therefore, improved sanitation facilities were
found to have no strong impact on child health among rich households. On the other hand,
improved sanitation was found to positively affect the height-for-age and weight-for-age forchildren from poor households, which is consistent with our main results.
Similarly, we found a significant and positive impact of improved sanitation on childhealth for those with uneducated mothers, while no such effects were found for children of
educated mothers. This might have been due to the minimal variation among households
containing educated mothers, with more than 88% of such households already using
improved sanitation. When sanitation improves, the children of uneducated mothers appear
to benefit more than those with educated mothers.

6. Conclusion
This study analysed the impacts of household sanitation facilities on children’s heightfor-age, weight-for-age, weight-for-height, and diarrhoea in Pakistan using the IV approach.
The key finding is that access to improved sanitation significantly affects a child’s heightfor-age and weight-for-age , although it does not significantly affect diarrhoeal prevalence21and weight-for-height among children. These findings were obtained after controlling for
endogeneity problems such as unobserved mothers’ behaviour towards child health or
sanitation as a whole.
This study also revealed differential impacts of improved sanitation facilities on childhealth, depending on the child’s age and sex, the mother’s education, and the economic
status of the household. Firstly, children above two years of age seem to benefit more from
improved sanitation than younger children because the latter do not use the toilet. Secondly,
girls benefit more from improved sanitation than boys, probably because the former are
likely to be discriminated against in terms of child healthcare and nutrition. Thirdly,
households where the children have uneducated mothers are more affected by improved
sanitation than those with educated mothers. The authors also found that children from poor
households are likely to benefit more from improved sanitation than those from richhouseholds, possibly because the latter were generally already using improved sanitationand their sanitation use varied minimally. If sanitation were improved through policies or
projects, children living in unfavourable conditions may gain greater benefits.
Certain policy recommendations can be highlighted following this study. Firstly,
improved sanitation facilities may significantly reduce the risk of child malnutrition, which
could positively influence their overall development and economic opportunities later in life.
Moreover, the potential benefits from access to improved sanitation facilities may influence
non-health-related outcomes, primarily household time use. Secondly, compared to theiropposites/counterparts, girls, children of poor households, and those with uneducatedmothers are more likely to be affected by improved sanitation. Considering the government’s
financial limitations, public funding allocations should be diverted towards such groups toreduce the disparity in growth between children living in favourable conditions and thoseliving in unfavourable conditions in the country.

22

Acknowledgements
The authors would like to acknowledge the contributions of the DHS Program for providing
open access to the dataset. The authors also sincerely appreciate the constructive commentsfrom Hisahiro Naito, Kenichi Kashiwagi, Mari Minowa, Yu Zhengfei, Moges Abu Girma,
Mohammad Abdul Malek and Yoshinori Kurokawa.

Disclosure statement
No potential conflict of interest was reported by the authors.

Data availability statement
The

data

used

in

this

study

for

analysis

came

from

the

DHS

Program(https://dhsprogram.com/Data/) and is publicly archived and available to download on request.

23

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Table 1. Mean comparison tests for the child health with and without improved sanitation
facilities in the household

Variables
Height-for-age
Weight-for-age
Weight-for-height
Diarrhoea

Unimproved-2.028-1.470-0.4020.209
1,190

No. of Observations

Sanitation sources
Improved
Total-1.364-1.559-0.919-1.081-0.173-0.240
0.188
0.194
2,851

t-test0.66***
0.55***
0.23***
0.02*

4,041

Source: Authors’ calculations. *** indicates significant at 1%, ** significant at 5% and * significant at 10%
in mean comparison test between households using unimproved and improved sanitation facilities.

29

Table 2. Effects of improved sanitation on child health - OLS & 2SLS

Variables

Dependent Variable: Sanitation facilities (1=improved)
(1)
(2)
(3)
(4)

Panel A. First-stage estimates
Cluster mean improved sanitation (except self)
Slope (degree)
Controls
District Fixed EffectChild characteristics
Household characteristics
R-squared
Kleibergen-Paap F-statistic

Variables
Panel B. OLS estimates
Sanitation source (1=Improved, otherwise 0)
R-squared
Panel C. 2SLS estimates
Sanitation source (1=Improved, otherwise 0)
R-squared
Controls (all)Basmann over-identification (p-value)
Observations
No. of households

0.832***(0.0148)0.00051**(0.0002)0.438***(0.0357)0.0019**(0.0008)

0.436***(0.0358)
0.0019**(0.0008)0.441***(0.037)0.0020**(0.0008)

No
No
No
0.351
86.13
Heightfor-age
(1)

Yes
No
No
0.438
86.13
Weightfor-age
(2)

Yes
Yes
No
0.447
86.13
Weight-forheight
(3)

Yes
Yes
Yes
0.452
86.13
Diarrhoea(4)0.211***(0.07)
0.247

0.105**(0.05)
0.275-0.016(0.05)
0.188-0.004(0.01)
0.097

0.437*(0.25)
0.115
Yes
0.537
4,041
2,464

0.329*(0.18)
0.119
Yes
0.906
4,041
2,464

0.101
(0.19)
0.046
Yes
0.585
4,041
2,464

0.033
(0.06)
0.052
Yes
0.090
4,041
2,464

Clustered standard errors at PSU level in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Source: Authors’calculations.

30

Table 3. Differential effects of sanitation on child health by age group
Younger than 2 years of age (2SLS)

Older than 2 years of age (2SLS)HeightforAge

Weightfor-Age

WeightforHeight

Diarrhea

HeightforAge

Weightfor-Age

WeightforHeight

Diarrhea

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.259

0.172

-0.0110.01

0.601*

0.469**

0.171

0.043

-0.341-0.263-0.284-0.113-0.334-0.229-0.23-0.073R-squared

0.126

0.096

0.036

0.02

0.073

0.114

0.052

0.042

Observations
Over-identification
test(p-value)
Controls

1,662

1,662

1,662

1,662

2,379

2,379

2,379

2,379

0.828

0.971

0.986

0.684

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Variables
Sanitation source(1=Improved,

otherwise 0)

F-Statistics
64.35
54.7
Source: Authors’ calculations. Clustered standard errors at PSU level in parentheses, *** p<0.01, ** p<0.05,* p<0.1

31

Table 4. Differential effects of sanitation on child health by gender
Heightfor-age(1)

Weight-forage(2)

Weight-forheight(3)

Diarrhoea(4)0.166*(0.092)0.286

0.140**(0.065)
0.317

0.061
(0.077)
0.230

0.009
(0.026)
0.144

0.454
(0.330)
0.16
1,997
43.78
0.021

0.719***(0.253)
0.10
1,997
43.78
0.381

0.589**(0.259)
0.03
1,997
43.78
0.665

0.043
(0.089)
0.07
1,997
43.78
0.053

0.273**(0.107)0.27300.089(0.080)0.3023-0.072(0.083)0.2371-0.015(0.027)0.12990.404-0.059-0.368(0.334)(0.232)(0.266)R-squared
0.12
0.13
0.04
Observations
2,044
2,044
2,044
F-statistic
52.17
52.17
52.17
Basmann over-identification (p-value)
0.024
0.442
0.242
Controls
Yes
Yes
Yes
Clustered standard errors at PSU level in parentheses, *** p<0.01, ** p<0.05, * p<0.1

0.034(0.095)
0.03
2,044
52.17
0.560
Yes

Variables
Panel A.
Female Children (OLS)
Sanitation source (1=Improved, otherwise 0)
R-squared
Female Children (2SLS)
Sanitation source (1=Improved)
R-squared
Observations
F-statistic
Basmann over-identification (p-value)
Panel B.
Male Children (OLS)
Sanitation source (1=Improved)
R-squared
Male Children (2SLS)
Sanitation source (1=Improved, otherwise 0)

Source: Authors’ calculations. 2SLS results report correlations between sanitation and gender-based childhealth variables.

32

Table 5. Differential effects of sanitation on child health by household’s wealth status

Variables
Panel A.
Below-average households (OLS)
Sanitation source (1=Improved, otherwise 0)
R-squared
Below-average households (2SLS)
Sanitation source (1=Improved)
R-squared
Observations
F-statistic
Basmann over-identification (p-value)
Panel B.
Above-average households (OLS)
Sanitation source (1=Improved)
R-squared
Above-average households (2SLS)
Sanitation source (1=Improved, otherwise 0)
R-squared
Observations
F-statistic
Basmann over-identification (p-value)
Controls

Heightfor-age(1)

Weight-forage(2)

Weight-forheight(3)

Diarrhoea(4)0.228**(0.102)0.26

0.139*(0.070)0.28

0.026(0.083)0.25-0.012(0.026)0.14

0.771*(0.418)
0.10
1,860
21.65
0.584

0.539*(0.280)
0.06
1,860
21.65
0.267

0.278
(0.331)
0.02
1,860
21.65
0.407

0.179**(0.080)
0.01
1,860
21.65
0.017

0.122
(0.100)0.22-0.001(0.080)0.25-0.121(0.092)0.18-0.009(0.033)0.12-0.027(0.518)
0.09
2,181
25.96
0.263
Yes

-0.000(0.350)
0.13
2,181
25.96
0.711
Yes

-0.112(0.374)
0.04
2,181
25.96
0.228
Yes

-0.106(0.157)
0.04
2,181
25.96
0.816
Yes

Source: Authors’ calculations. Clustered standard errors at PSU level in parentheses, *** p<0.01, **p<0.05, * p<0.1

33

Table 6. Differential effects of sanitation on child health by woman literacy
Mother uneducatedVariables
Sanitation source(1=Improved)

Heightfor-age

Weightfor-age

Weightforheight

(1)

(2)

1.200***

Mother educatedDiarrhoea

Heightfor-age

Weightfor-age

Weightforheight

Diarrhoea

(3)

(4)

(5)

(6)

(7)

(8)

0.476*-0.242-0.001-0.280

0.205

0.426

0.080

(0.419)(0.282)(0.282)(0.074)(0.395)(0.269)(0.302)(0.117)R-squared

0.058

0.077

0.037

0.047

0.108

0.122

0.044

0.054

Observations
Over-identification
test (p-value)
Controls

2,060

2,060

2,060

2,060

1,981

1,981

1,981

1,981

0.691
Yes

0.344
Yes

0.490
Yes

0.091
Yes

0.905
Yes

0.258
Yes

0.344
Yes

0.666
Yes

F-Statistics
27.43
33.30
Source: Authors’ calculations. Clustered standard errors at PSU level in parentheses, *** p<0.01, ** p<0.05, *
p<0.1

34

Appendix
Table 1. Summary Statistics
Variables

Obs

Mean

Std. Dev. Min

Max

4,041
4,041
4,041
4,041
4,041

0.66
-1.56
-1.08
-0.24
0.19

0.48
1.67
1.25
1.28
0.39

0
-5.99
-5.90
-4.94
0

1
5.81
3.90
4.99
1

4,041
4,041
4,041
4,041
4,041
4,041
4,041
4,041
4,041

2.01
29.32
0.49
1.90
0.99
0.82
0.27
0.37
0.31

1.41
17.35
0.50
0.48
0.86
0.37
0.44
0.48
0.46

0
0
0
1
0
0
0
0
0

4
59
1
3
6
1
1
1
1

HH Head’s & Mother’s Characteristics
Age of HH head (years)
HH head education (1=yes)
HH head gender (1=male)Age of mother (years)
Mother’s education (1=yes)
Mother’s working status (1=yes)
Mother’s BMI (mean)

4,041
4,041
4,041
4,041
4,041
4,041
4,041

46.56
0.002
0.89
29.11
0.49
0.11
25.01

15.18
0.04
0.30
6.08
0.49
0.31
5.30

16
0
0
15
0
0
12.93

95
1
1
49
1
1
52.48

HH Characteristics
Number of children under 5 (number)Month of Interview (mean)
Wealth quantile index (1=poorest,…, 5=richest)
Water source (1=improved, 0=otherwise)
HH uses treated water (1= yes)
Distance to water source (minutes)
Cooking fuel clean (1= yes)
HH owns livestock (1= yes, 0=otherwise)
HH size (no. of rooms)

4,041
4,041
4,041
4,041
4,041
4,041
4,041
4,041
4,041

2.46
4.56
2.84
0.91
0.10
1.39
0.42
0.50
2.43

1.51
4.56
1.38
0.28
0.29
0.69
0.49
0.50
1.41

0
1
1
0
0
1
0
0
0

11
12
5
1
1
3
1
1
12

Explanatory Variables
Sanitation source (1=improved, 0=otherwise)
Height-for-age z-score
Weight-for-age z-score
Weight-for-height z-score
Diarrhoea (1=yes)
ChildrenAge in years
Age in months
Female (1=yes)
Birth size (1=small, 2=average, 3= large)
Dietary diversity (0=no group…,6=six groups)
Child wanted at pregnancy (1=yes)
Delivery at government hospital (1=yes)
Delivery at private hospital (1=yes)
Delivery at home (1=yes)

Community Variables
Cluster mean improved sanitation (except self)
4,041
25.40 90.24
0Area of residence (1=rural, 0=urban)
4,041
0.54
0.49
0
Slope (degree)
4,041
3.21
4.93
0
Total Households
2,464
Source: Authors’ calculations. Pakistan Demographic and Health Survey (PDHS), 2017-18.

35

480
1
23

Table 2. Reduced-form estimates (N=4,041)

Variables
Cluster mean improved sanitation
Slope (degree)

Dependent Variable
WeightWeight-forfor-age
height(2)(3)
0.147*
0.0549(0.0833)(0.0911)0.000333-0.00154(0.00283)(0.00346)

Height-forage
(1)
0.178
(0.114)0.00339(0.00384)

Diarrhoea(4)0.0252(0.0307)-0.00170*(0.000871)

Controls
District Fixed Effect
Yes
Yes
Yes
Yes
Child and Household characteristics
Yes
Yes
Yes
Yes
4,041
4,041
4,041
Observations
4,041
R-squared
0.246
0.275
0.189
0.098
Source: Authors’ calculations. Clustered standard errors at PSU level in parentheses, *** p<0.01, ** p<0.05, *
p<0.1

36

Table 3. 2SLS estimates of the impact of sanitation on child health: Full Sample (N=4,041)
Variables
Improved sanitation (1=yes)
Age in months (0-59)
Birth size (average)
Birth size (large)
Female child (1=yes)
Dietary diversity (1 food group)
Dietary diversity (2 food groups )Dietary diversity (3 food groups)
Dietary diversity (4 food groups )Dietary diversity (5 food groups)
Dietary diversity (6 food groups)
Children under age 5 (No.)
Child wanted at pregnancy time (1=yes)
Delivery in government hospital (1=yes)
Delivery in private hospital (1=yes)
Delivery at home (1=yes)
Interview monthAge of HH head (years)
HH head gender (1=male)
HH head education (1=yes)
Age of mother (years)
Mother’s education (1=yes)
Mother’s working status (1=yes)
Mother’s BMI (mean)
Household size (no. of rooms)
Cooking fuel clean (1= yes)
Water source (1= improved)

Heightfor-age(1)
0.4373*(0.2561)-0.0254***(0.0016)0.3301***(0.0693)0.5804***(0.0977)0.0450(0.0449)
0.0342(0.0569)-0.0196(0.0766)
0.0835(0.1514)0.2891(0.2518)
0.2575(0.3398)
1.2603***(0.2309)-0.0018(0.0211)0.0377(0.0687)
0.0260(0.1276)0.1709(0.1228)-0.0251(0.1252)-0.0138(0.0087)-0.0039*(0.0020)-0.0326(0.0860)
0.9454**(0.4285)0.0156***(0.0048)0.2936***(0.0709)-0.0311(0.0856)
0.0198***(0.0053)-0.0321(0.0267)-0.0639(0.0878)-0.1403(0.1098)37

WeightWeight-forfor-age
height(2)(3)
0.3290*0.1012(0.1846)(0.1901)-0.0121***0.0033**(0.0011)(0.0013)0.4065***0.2608***(0.0512)(0.0536)0.7534***0.5419***(0.0732)(0.0798)
0.01030.0229(0.0341)(0.0383)
0.0554-0.0002(0.0444)(0.0473)
0.0328-0.0284(0.0559)(0.0589)0.0873-0.0674(0.0998)(0.0989)
0.2999*0.0497(0.1642)(0.1413)0.5341***0.3863(0.1771)(0.3116)
1.2872***0.5899(0.3547)(0.4608)-0.0069-0.0142(0.0170)(0.0180)0.0097-0.0094(0.0507)(0.0503)-0.0265-0.0576(0.1112)(0.1030)
0.09350.0121(0.1072)(0.1012)-0.1359-0.1599(0.1084)(0.1002)-0.0034
0.0071(0.0071)(0.0067)-0.00240.0001(0.0016)(0.0017)-0.0485-0.0509(0.0641)(0.0758)
0.5984*0.0510(0.3356)(0.3566)
0.0049-0.0062*(0.0034)(0.0037)
0.1628***-0.0248(0.0536)(0.0534)0.02010.0792(0.0636)(0.0594)
0.0261***
0.0214***(0.0042)(0.0043)-0.0274-0.0157(0.0189)(0.0253)-0.1110*-0.1183*(0.0664)(0.0654)-0.04280.0702(0.0834)(0.0961)

Diarrhoea(4)0.0336(0.0660)-0.0034***(0.0004)-0.0802***(0.0183)-0.0280(0.0283)-0.0087(0.0120)0.0322**(0.0146)
0.0285(0.0189)
0.0938**(0.0421)0.0339(0.0694)-0.2102***(0.0282)-0.2676***(0.0804)-0.0089*(0.0053)-0.0428**(0.0191)
0.0133(0.0366)
0.0130(0.0357)
0.0171(0.0344)0.0008(0.0029)-0.0007(0.0005)-0.0208(0.0242)0.0066(0.0424)-0.0015(0.0011)0.0452***(0.0167)-0.0145(0.0179)-0.0008(0.0013)-0.0008(0.0064)0.0276(0.0219)-0.0081(0.0277)

HH uses treated water (1= yes)
Distance to water source (minutes)
HH owns livestock (1= yes, 0=otherwise)Household area of residence (1=rural, 0=urban)
Wealth index (poorer)
Wealth index (middle)
Wealth index (richer)
Wealth index (richest)

0.1535*(0.0891)-0.0174(0.0565)
0.0518(0.0642)0.0383(0.0680)
0.1823(0.1255)
0.1584(0.1577)0.3173*(0.1895)
0.5135**(0.2145)0.0771(0.0667)0.0016(0.0350)
0.0658(0.0477)
0.0312(0.0502)0.1008(0.0873)0.2075*(0.1134)0.3072**(0.1360)
0.5538***(0.1550)

0.0024(0.0631)0.0238(0.0340)0.0311(0.0499)
0.0115(0.0524)-0.0161(0.0876)0.1458(0.1102)
0.1926(0.1380)
0.3962**(0.1605)

Clustered standard errors at PSU level in parentheses, *** p<0.01, ** p<0.05, * p<0.1

380.0024(0.0228)-0.0074(0.0111)-0.0053(0.0165)
0.0067(0.0204)-0.0294(0.0313)-0.0407(0.0375)-0.0567(0.0447)-0.1115**(0.0533)

Table 4. Mean comparison of healthcare and nutrition status by gender

Immunisation status of child(1=yes)Pneumococcal Vaccine (1=yes)
Postnatal checkup (1=yes)
Wealth status of household
Dietary diversity (1,…..8)
Breastfeeding ever (1=yes)
Fruit & Vegetables
Dairy products
Medical treatment for fever
Visited private hospital for fever
Any treatment for fever
Visited private hospital for
diarrhoea
Medical treatment for diarrhoea
Any treatment for diarrhoea

Observations
4,041
4,041
2,671
4,041
4,041
2,595
4,041
2,546
1,646
1,646
1,646
787
787
787

Source: Authors’ calculations.

39

Male

Femalet-test

0.175
0.278
0.442
2.892
1.014
0.345
0.381
0.043
0.737
0.378
0.801

0.157
0.252
0.424
2.806
0.976
0.315
0.362
0.027
0.687
0.352
0.741

0.017*
0.025*
0.018
0.085**
0.038*
0.030**
0.019*
0.015**
0.050**
0.026
0.060***

0.287
0.618
0.702

0.263
0.553
0.605

0.023
0.065**
0.097***

Table 5. OLS results on the association between gender and healthcare and nutrition status
Food
DiversityFemale Child (1=yes)

Dairy
Food

Meat, fruit
Bread,
Breastfeeding Pneumococcal Immunisa&
noodles &vaccinetion
vegetables
grain

(1)

(2)

(3)

(4)

(5)

(6)

(7)

-0.017-0.015**-0.016-0.001-0.024-0.007-0.011(0.025)(0.006)(0.011)(0.016)(0.017)(0.010)(0.009)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Controls
District Fixed Effects
Household & Childcharacteristics
Observations

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4,041

2,546

4,041

2,546

2,595

4,041

4,041

R-squared

0.20

0.12

0.45

0.22

0.30

0.60

0.31

MedicalTreatment
Fever

PrivateHospital
Fever

Any
Treatment
Fever

Postnatal
care

Medical Private
Any
Treatment Hospital Treatmen
Diarrhoea DiarrhoetaDiarrhoea

(8)

(9)

(10)

(11)

(12)

(13)

(14)

-0.077*-0.041-0.101**-0.050**-0.032-0.060***-0.025(0.041)(0.037)(0.042)(0.022)(0.024)(0.022)(0.016)

District Fixed Effects
Household & Childcharacteristics
Observations

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

787

787

787

1,646

1,646

1,646

2,671

R-squared

0.24

0.26

0.23

0.21

0.21

0.18

0.17

Female Child (1=yes)
Controls

Clustered standard errors at PSU level in parentheses, *** p<0.01, ** p<0.05, * p<0.1

40

Table 6. 2LSL Regression results for the robustness check on the impact of improved sanitation
on child health (N=4,041)
Variables
Sanitation
source(1=Improved)
Controls
Child characteristics
Household head’s andMother’s characteristics
Household characteristics
F-Statistics
Variables
Sanitation
source(1=Improved)
Controls
Child characteristics
Household head’s andMother’s characteristics
Household characteristics(1)

Height-for-age
(2)

(3)

(4)

Weight-for-age
(5)

(6)

0.914***(0.17)

0.511***(0.18)

0.437*(0.25)

0.785***(0.12)

0.416***(0.13)

0.329*(0.18)

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No
251

Yes
86

No
251

(9)

(10)

No
176
Diarrhoea
(11)

Yes
86

(7)

No
176
Weight-for-height
(8)

(12)

0.352***(0.12)

0.177(0.14)

0.101(0.19)

0.026(0.04)

0.007(0.04)

0.033(0.06)

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

No

Yes

No

No

Yes

F-Statistics
251
176
86
251
176
Source: Authors. Clustered standard errors at PSU level in parentheses, *** p<0.01, ** p<0.05, * p<0.1

4186

Table 7. 2SLS estimates on the impact of sanitation on child health with and without controlling
drinking water quality variables

Variables
Sanitation source(1=Improved)

2SLS(with drinking water quality variables)
HAZ
WAZ
WHZ
Diarrhoea
(1)
(2)
(3)
(4)

2SLS
(without drinking water quality variables)
HAZ
WAZ
WHZ
Diarrhoea
(5)
(6)
(7)
(8)

0.437*(0.25)
Yes

0.441*(0.256)
Yes

0.329*(0.18)
Yes

0.101(0.19)
Yes

0.033(0.06)
Yes

0.330*(0.185)Yes

0.098(0.190)Yes

0.034(0.066)
Yes

Controls
Over-identification
0.537
0.906
0.585
0.090
0.437
0.974
0.546
0.096
test
F-Statistics
86.13
85.60
Observations
4,041
4,041
4,041
4,041
4,041
4,041
4,041
4,041
Source: Authors. Clustered standard errors at PSU level in parentheses, *** p<0.01, ** p<0.05, * p<0.1

42

Table 8. 2SLS estimates of the impact of sanitation on alternative child health issues (N=4,041)
Variables
Cough

Dependent Variable: Other Health Issues
Common cold
Fever
Breathing issues

(1)
(2)
(3)
(4)
Sanitation source (1=Improved)
0.121
0.066
0.028
0.058
(0.092)(0.072)(0.084)(0.073)Controls
Yes
Yes
Yes
Yes
Over-identification test (p-value)
0.630
0.421
0.890
0.931
Source: Authors. Clustered standard errors at PSU level in parentheses, *** p<0.01, ** p<0.05, * p<0.1

43Figure 1. Location and slope of clusters / enumeration blocks
Source: PDHS and Pakistan administrative and slope data from DIVA-GIS and ALOS-JAXA

44