Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Do Toilets Save Young Children’s Lives? Evidence from Cambodia
Hiroyuki Yamada, Tien Manh Vu
28 March, 2024
DP2024-007
https://ies.keio.ac.jp/en/publications/23665/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
28 March, 2024

Do Toilets Save Young Children’s Lives? Evidence from Cambodia
Hiroyuki Yamada, Tien Manh Vu
Keio-IES DP2024-007
28 March, 2024
JEL Classification: I15, J13, O18, O53
Keywords: Toilet, Sanitation, Under-five mortality, Census, Cambodia

Abstract
There are 3.5 billion people living without safe toilets worldwide, who often have no
choice but to use unreliable, inadequate toilets or engage in open defecation. The
negative impacts of lacking appropriate toilets are particularly noticeable in infants
and children, including diseases, stunted growth, chronic malnutrition, and in somecases, death. However, evidence showing these negative effects based on nationally
representative data is lacking and thus a thorough analysis is needed. Therefore, in thisstudy, we explore how the extent of toilet coverage is associated with under-five
mortality in Cambodia. We use the censuses conducted in 2008 and 2019 in Cambodia
to create village-level panel data that include information on the extent of toilet
coverage and what kinds of toilets are used. We use the constructed village-level panel
data to perform an instrumental variable regression analysis aimed at elucidating theassociation of toilet coverage with under-five mortality at the village level. We findthat increased toilet coverage in a given village is associated with reduced under-five
mortality in that village. Increased coverage of improved toilets in particular isassociated with lower under-five mortality, suggesting that cleaner toilets save young
children’s lives. This finding is useful to policymakers in developing countries facing
challenges regarding the widespread use of toilets.
Hiroyuki Yamada
Faculty of Economics, Keio University
2–15–45 Mita, Minato–ku, Tokyohyamada@econ.keio.ac.jp
Tien Manh Vu
Faculty of Global Management, Chuo University742-1 Higashinakano, Hachioji, Tokyo
mvu264@g.chuo-u.ac.jp
Acknowledgement: This work was supported by JSPS (Japan Society for the Promotionof Science) KAKENHI Grant Numbers 21K01455, 22K18534, and Keio University
Academic Development Funds.

Do Toilets Save Young Children’s Lives? Evidence from Cambodia
Hiroyuki Yamada* and Tien Manh Vu**
* (Corresponding author) Faculty of Economics, Keio University
2–15–45 Mita, Minato–ku, Tokyo 108–8345, Japan
Tel (Direct): +81-(0)3-5427-1271
E-mail: hyamada@econ.keio.ac.jp
https://orcid.org/0000-0003-3709-1914** Faculty of Global Management, Chuo University742-1 Higashinakano, Hachioji, Tokyo 192-0393, Japan
Email: mvu264@g.chuo-u.ac.jp
https://orcid.org/0000-0002-2251-0795

Acknowledgements: This work was supported by JSPS (Japan Society for the Promotionof Science) KAKENHI Grant Numbers 21K01455, 22K18534, and Keio University
Academic Development Funds.
Conflict of interest: The authors declare no conflicts of interest.
Abstract
There are 3.5 billion people living without safe toilets worldwide, who often have no
choice but to use unreliable, inadequate toilets or engage in open defecation. The negative
impacts of lacking appropriate toilets are particularly noticeable in infants and children,
including diseases, stunted growth, chronic malnutrition, and in some cases, death.
However, evidence showing these negative effects based on nationally representative datais lacking and thus a thorough analysis is needed. Therefore, in this study, we explore
how the extent of toilet coverage is associated with under-five mortality in Cambodia.
We use the censuses conducted in 2008 and 2019 in Cambodia to create village-level
panel data that include information on the extent of toilet coverage and what kinds of
toilets are used. We use the constructed village-level panel data to perform an
instrumental variable regression analysis aimed at elucidating the association of toilet
coverage with under-five mortality at the village level. We find that increased toilet
coverage in a given village is associated with reduced under-five mortality in that village.
Increased coverage of improved toilets in particular is associated with lower under-five
mortality, suggesting that cleaner toilets save young children’s lives. This finding isuseful to policymakers in developing countries facing challenges regarding thewidespread use of toilets.
Keywords: Toilet, Sanitation, Under-five mortality, Census, Cambodia
JEL Classification Codes: I15, J13, O18, O53

1

1. Introduction
Access to toilets may seem like a basic right, and one taken for granted by many.
This issue is strongly related to the sixth goal of the Sustainable Development Goals:“Ensure access to water and sanitation for all.” However, there are 3.5 billion people
living without safe toilets in the world, who often have no choice but to use unreliable,
inadequate toilets or engage in open defecation.1 Without proper toilets and sanitation
systems, untreated human feces can pose a serious threat to public health bycontaminating drinking water sources, rivers, and food crops, thereby spreading deadly
diseases among the wider population. The negative impacts of not having appropriate
toilets are particularly noticeable in infants and children, including diseases, stunted
growth, chronic malnutrition, and in some cases, death.
Policymakers in developing countries have been aware of the importance of toilets
for a long time. A famous example is China’s “toilet revolution.” Although no studies
have focused on the origin of the toilet revolution in China, the term “toilet revolution”
was proposed first by United Nations International Children’s Emergency Fund
(UNICEF) in 1997, when UNICEF and the National Patriotic Health Campaign
Committee worked together to promote toilet retrofitting in China (Cheng et al., 2018).
Another notable example is the Governments of India’s Total Sanitation Campaign (TSC),
a low-subsidy regime that aims to generate household involvement and demand
responsiveness for the building of individual household toilets in households below thepoverty line (Barnard, et al. 2013). A further example is Community-Led Total Sanitation(CLTS), a program implemented in more than 60 countries in Asia, Africa, Latin America,
the Pacific, and the Middle East to address the sanitation burden (da Silva Wells andSijbesma, 2012). CLTS aims to create demand for sanitation by facilitating graphic,
shame-inducing community discussions of the negative health consequences of existing
sanitation practices, rather than through the more traditional approach of providing
sanitation hardware or subsidies (Cameron et al., 2019).
Researchers are keen to investigate the impacts of toilet utilization on various
outcomes. Indeed, previous studies have investigated the impacts of having adequate
toilets on outcomes such as self-rated health (Chen et al. 2022), diarrheal disease(Anteneh and Kumie 2010, Semba et al. 2011), labor supply (Wan and Shen 2022), andgroundwater quality (Graham 2013, Ndoziya et al. 2019). Spears (2020) claims that open
defecation in India might account for Indian children’s lower height compared withchildren in Africa. Using a randomized control trial in the context of CLTS in Laos,
Cameron et al. (2021) show that improved sanitation produced positive health spillovers,in that increased village sanitation coverage decreased the probability of childhoodstunting.
However, when it comes to the impact of toilets on infant and under-five mortality,
the literature remains scarce. Semba et al. (2011) investigate the association between thepresence of an improved toilet in the household and under-five mortality in Indonesia.
Agha (2000) also investigates the correlation between infant mortality and toilet
sanitation. The most closely related paper to ours is Spears (2012), which investigates thecausal impact of rural sanitation on infant mortality in the context of TSC in India.
1

https://www.un.org/en/observances/toilet-day/background (las accessed on March 5, 2024)

2

However, as we explain in detail in a later section, the empirical identification approachof Spears (2012) (difference-in-differences) differs from our own.
To fill this gap in the literature, we investigate the impact of toilets on under-five
mortality in Cambodia by using village-level panel data. It is no exaggeration to say that
under-five mortality rate is one of the most important indicators reflecting the sanitaryenvironment of a country. In addition, the survival of children is extremely important forthe subsequent accumulation of human capital and, ultimately, for the development of thecountry. Cambodia is an ideal country to test the hypothesis that toilets have an impact
on under-five mortality because the country experienced rapid economic growth between
2008 and 2019 (above 6% annually on average), which led to a dramatic increase in toilet
coverage from 35% to 83%.
Our study makes four contributions to the literature concerning the relationshipbetween toilets and under-five mortality. First, we utilize two censuses conducted in 2008
and 2019 in order to construct the village-level panel dataset, which is quite unique in
research on the impact of toilets. Because they are censuses, not a sampled survey dataset,they are free from the sample selection problem.
Second, as mentioned above, few studies have investigated the impact of toilets onunder-five mortality. This is partly because there are few reliable data on under-five
mortality in developing countries. For example, respondents might be inclined tounderreport the death of infants and young children because it is a sensitive issue.
Utilizing two censuses in Cambodia enables us to avoid this potential problem because
they include a death module asking about family members who passed away in the 12months prior to “census night”—the specific point in time that the collected census
information refers to—as well as the causes of death.
Third, we use a novel instrumental variable for the variables regarding toilets to obtaina causal relationship between the availability of toilets and the under-five mortality in thevillage, if any. Many previous studies merely performed correlation analyses between
toilets and health outcomes. Although some studies utilized policy interventions to draw
a causal relationship between toilets and health outcomes, none studied the impact of
toilets on under-five mortality.
Fourth, in our analysis, both the positive and negative externalities of toilet issues
within a village are fully taken into account because we utilize the full censuses and use“village” as the sample unit (i.e., village-level panel data). It is widely known that ahousehold having a toilet can have a positive impact on neighboring households, whichis indeed a positive externality. Conversely, open defecation because a household does
not have a toilet can lead to negative health outcomes for neighboring households (i.e., anegative externality). Using survey data from a small sample, we cannot tell anything
about such positive and negative externalities. However, our village-level panel datausing full censuses allows us to consider such externalities.2
The rest of this paper is organized as follows. The next section explains the
background information and country context of Cambodia. Section 3 explores the data.
Section 4 shows the empirical models and the estimation results. In Section 5, we discuss
2 Cameron et al. (2021) show positive health externality (or spillover) within a village of improved

sanitation, but they use a sample of 160 villages. Furthermore, their outcome variable is childhoodstunting, not under-five mortality. Geruso and Spears (2018) also examine the importance ofneighborhoods by finding that Muslim children are substantially more likely than Hindu children to
survive to their first birthday.

3the obtained results as well as the policy implications. Section 6 presents the conclusions.

2. Background and context
Cambodia, officially the Kingdom of Cambodia, is a country in Mainland
Southeast Asia. The population was about 16.2 million and the population density was
91.8 people per square kilometer of land area in 2019. The population is relatively young,with persons aged 0–14 years accounting for about 30% of the population in 2019. Thisis largely because the early 1970s were a time of escalating civil war, and in the late 70s
during the Khmer Rouge period, a large number of people were killed in Cambodia(Ministry of Planning of Cambodia 2020).
Between the 2008 and 2019 censuses, Cambodia recorded a high mean annual
GDP growth rate of more than 6%, even taking into consideration the zero growth rate in2009 due to the global financial crisis. Hence, Cambodia experienced both economic andsocial changes during this period.
According to the General Population Census of Cambodia (GPCC) of 2019, which
is one of our main data sources for the analysis below, about 83% of households in
Cambodia had at least one toilet at home, a great improvement compared with just 35%
of households in 2008. Despite this progress, the sanitation issue in Cambodia remains a
concern. Indeed, the under-five mortality rate in Cambodia (26.80 per 1,000 live births)
in 2019 was much higher than that of neighboring countries such as Vietnam (21.10),
Thailand (9.00), and Malaysia (7.90); only Laos had a higher rate (45.80). Furthermore,
the mortality rate attributed to unsafe water, unsafe sanitation, and lack of hygiene in
Cambodia (17.10 per 100,000 population) is also higher compared with neighboring
countries (6.90 in Vietnam, 11.80 in Thailand, and 14.40 in Malaysia).3
Although this is a very worrisome situation, solid evidence on the impact of the
toilet installation on mortality remains scarce. Therefore, in this paper, we focus inparticular on the impact of having a toilet at home on under-five mortality.

3. Data
3.1. Outcome variables
We use the following data sources for the empirical analyses. The first data
source is the GPCC of 2008 and 2019, which are full censuses that contain information
identifying each village and include a death module asking about household family
members who passed away in the 12 months prior to the census night. The death module
enables us to count how many children under the age of 5 years passed away during those
12 months. We obtained the under-five mortality rate for each village in 2008 and 2019by dividing the number of under-five deaths by the number of under-five children who
were alive on the census night, multiplied by 100. We did not include under-five deaths
unrelated to sanitation, such as those caused by road accidents, land mines, and drowning.
Note that although we use the term “under-five mortality” in this paper, we mean underfive mortality in the 12 months prior to the census night. This definition differs from the
3 The data for population and GDP are taken from World Development Indicators(https://databank.worldbank.org/source/world-development-indicators) (last accessed on March 10,2024).

4

one used by the World Health Organization (WHO): “the probability that a child born ina specific year or period will die before reaching the age of 5 years, subject to the agespecific mortality rates of that period”. 4 Hence, our computed under-five mortality issmaller than that of the WHO.
We calculate the under-five mortality rate of boys and girls in a similar manner.
These three constructed variables—total under-five mortality rate ( 𝑅𝑑𝑒𝑎𝑡ℎ𝑢𝑛𝑑𝑒𝑟5),
under-five mortality of boys ( 𝑅𝑑𝑒𝑎𝑡ℎ𝑢𝑛𝑑𝑒𝑟5𝐵 ), and under-five mortality of girls(𝑅𝑑𝑒𝑎𝑡ℎ𝑢𝑛𝑑𝑒𝑟5𝐺)—are our outcome variables.
We omit the top 0.7% of villages in terms of the highest 𝑅𝑑𝑒𝑎𝑡ℎ𝑢𝑛𝑑𝑒𝑟5 (over15 in value) to overcome the outlier problem.

3.2. Variables of interest
The variables for toilets at the village level also come from the GPCC, in which
each household is asked about the type of toilet in the house. First, the percentage ofhouseholds having no toilet (𝑅𝑛𝑜𝑡𝑜𝑖𝑙𝑒𝑡) in each village is obtained by dividing thenumber of households that have no toilet at home by the total number of households inthe village. Second, the percentage of households having toilets regardless of type
(𝑅𝑡𝑜𝑖𝑙𝑒𝑡) in each village is obtained by dividing the number of households that have a
toilet at home by the total number of households in the village.5 Third, we consider thetype of a toilet. We define (a) a pour flush (or flush) toilet connected to sewage and (b) apour flush (or flush) toilet with a septic tank as an “improved” toilet. The percentage ofhouseholds having an improved toilet (𝑅𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡) in each village is obtained by
dividing the number of households that have an improved toilet at home by the total
number of households in the village. Then, we define the percentage of households having
a “non-improved” toilet (𝑅𝑛𝑜𝑡𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡) as the number of households having a
toilet other than (a) and (b) above divided by the total number of households in the village.
A typical non-improved toilet is a pit latrine with or without a slab, or a pour flush (orflush) toilet connected to something other than sewage or a septic tank.

3.3. Instrumental variable
As we discuss in detail in a later section, we suspect that toilet conditions invillages may be endogenous. To overcome this endogeneity, we apply an instrumental
variable (IV) approach. The instrumental variable used is information on how steep theslope within the village is. The steepness of the land within the village may be associatedwith the difficulty or suitability for the construction of a given type of toilet. For instance,in the construction of a toilet with a septic tank, it is generally recommended to slope the
4 See the following page for details of the WHO’s definition of the mortality rate of children under

the age of 5:
https://www.who.int/data/nutrition/nlis/info/under-five-mortalityrate#:~:text=How%20is%20it%20defined%3F,mortality%20rates%20of%20that%20period (lastaccessed on March 15, 2024)
5 Some households did not answer the question regarding toilets. Hence, 𝑅𝑛𝑜𝑡𝑜𝑖𝑙𝑒𝑡 and 𝑅𝑡𝑜𝑖𝑙𝑒𝑡 do
not necessarily add up to 1 in all villages. This is the why we use both variables as variables ofinterest in this paper.

5

pipe 1/4 inch per foot (1/8 inch per foot minimum) toward the tank.6 The slope is notdirectly related to the under-five mortality in the village. To measure the slope of a village,
we use the following procedure. First, we have the location of each village as a point on
a map in the shape file obtained from the National Institute of Cambodia. Figure 1 shows
the location distribution of villages. Unfortunately, the shape file of the villages is notavailable, so we cannot identify the exact topographic information of each village. Hence,
we assume that each village is small, with a radius of 300 m. By observing the point
location of each village, this approximation would not be a bad one, although it apparently
contains some noise. Second, by overlapping the circle of each village with the
topographic information from the Digital Elevation Model created by the Shuttle Radar
Topography Mission in 2000 and the ASTER global digital elevation model in 2011, we
find the highest and the lowest elevations and take the difference of the two. This
procedure is possible because the resolution is 1 arc-second (approximately 30 m) for thetwo models above. We interpret the difference in elevation between the highest and thelowest elevations as the slope of the village. We use the constructed slope variable using
the data from 2000 as an IV for the toilet variable in 2008 and that using the data from2011 as an IV for the toilet variable in 2019.

3.4. Control variables
All control variables in the main specifications below were obtained from
Commune Village Database (CVD) for 2007 and 2018 collected by the National Instituteof Statistics (NIS) of Cambodia, except for population in a village, which was obtained
from the GPCC 2008 and GPCC 2019. In each year since 2006, NIS has collected various
information at the village and/or commune level as the CVD. Village leaders and key
informants of the government development commune committee collect the data after
training, and monitoring and evaluation are carried out by the Ministry of Planning andthe Ministry of Interior. We use the CVD for 2007 and 2018, one year before the census
years (i.e., 2008 and 2019, respectively). We construct a variable for the percentage ofhouseholds whose head is female and the percentage of households whose main water
source is from a well, pond, or river, respectively. The illiteracy rates of people aged 15–
17, 18–24, 25–35, and 36–45 years are obtained for the male and female populations
separately. For housing conditions, we construct a variable for the percentage ofhouseholds having zinc or fibro roof houses with electricity and those having tiled roof
houses with electricity. Variables for the percentage of households using chemicalfertilizers and those using pesticides are also included because agriculture remains themain economic activity in many village economies. In addition, the average number ofvehicles per household and the distance to the nearest health center from the village arealso included.7

6 For instance, see the following website https://extension.missouri.edu/publications/eq401 (lastaccessed on March 15, 2024)
7 For population and the distance to the nearest health center, we apply the inverse hyperbolic sinetransformation. The inverse hyperbolic sine transformation of 𝑥 is denoted by the following
equation: 𝑙𝑛 𝑥 + √𝑥 + 1 .

6

3.5. Descriptive statistics
Table 1 shows the descriptive statistics of the variables to be used in the analyses
below. Total sample size is 24,178 and covers 12,089 of the roughly 14,000 villages in
Cambodia, indicating that we have well-balanced panel data. The discrepancy between
the number of villages in the sample and the actual number of villages in Cambodia isdue mainly to three reasons. First, there are some villages with missing data and/or withoutliers (e.g., the number of under-five children and deaths), which we dropped from oursample. Second, some villages in 2008 were no longer villages in 2019 and vice versa,
and thus we could not make village-level panel data for these villages. Third, between thetwo censuses conducted in 2008 and 2019, Kampong Cham Province was split into
Kampong Cham and Tboung Khmum provinces. It was difficult to match the villages in
Kampong Cham Province in the 2008 census with those in Tboung Khmum Province inthe 2019 census because their province, district, and commune codes had changed. Thus,
we did not include these villages (about 870 villages) in our sample.
The mean total under-five mortality rate was 0.66%, and the under-five mortality
rate for girls (0.79%) was higher than that for boys (0.54%). In a typical village, 53% ofhouseholds had toilets and 46% of households did not. In addition, 46% of households
had improved toilets, while 7% had non-improved toilets. The mean 𝑠𝑙𝑜𝑝𝑒 was 12.8 m,
with a large standard deviation. The descriptive statistics of the controls are shown in
Table 1.

4. Empirical model and results
4.1 Empirical model
We apply a standard two-period panel specification with fixed effects. The sample
unit is “village.” Specifically, we estimate the following empirical model:
𝑚𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦= 𝛽 + 𝛽 𝑡𝑜𝑖𝑙𝑒𝑡 + 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝛽 + 𝜑 + 𝜋 + 𝜀 , (1)where 𝑚𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦 is one of the three outcome variables, namely, 𝑅𝑑𝑒𝑎𝑡ℎ𝑢𝑛𝑑𝑒𝑟5 ,𝑅𝑑𝑒𝑎𝑡ℎ𝑢𝑛𝑑𝑒𝑟5𝐵, or 𝑅𝑑𝑒𝑎𝑡ℎ𝑢𝑛𝑑𝑒𝑟5𝐺 in village 𝑣 in year 𝑡 (=2008 or 2019). 𝑡𝑜𝑖𝑙𝑒𝑡
is our main variable of interest. We have 𝑅𝑛𝑜𝑡𝑜𝑖𝑙𝑒𝑡, 𝑅𝑡𝑜𝑖𝑙𝑒𝑡, 𝑅𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡, and𝑅𝑛𝑜𝑡𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡 in village 𝑣 in year 𝑡 (=2008 or 2019) as a candidate of 𝑡𝑜𝑖𝑙𝑒𝑡 .
In the specification, we test these variables one by one. 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 is a vector of control
variables in village 𝑣 in year 𝑡 (=2008 or 2019). 𝜑 is a village fixed effect and 𝜋 is atime dummy taking 1 if the year is 2019 and 0 otherwise. 𝜀 is the error term.
However, as briefly mentioned in the previous section, the toilet variable may beendogenous. That is, villages with a certain percentage of households with toilets or no
toilets may be non-randomly located in a certain area of Cambodia. Or, simply, theallocation of toilets may be correlated with unobservable cofounders in the error term in
the main specification. Hence, a simple estimation of (1) using ordinary least squares(OLS) may yield biased estimates.
To overcome this endogeneity problem, we apply the instrumental variableapproach. As explained in the previous section, the IV is the slope of the village. We
assume that the slope within a village is related to the difficulty or suitability for theconstruction of a given type of toilet. Specifically, the slope could ensure the smooth flow7

of running water within the village or help to flush excrement into septic tanks. However,the slope variable does not imply a direct cause of higher or lower under-five mortality.
Note that in computing under-five mortality, we consider only non-accident causes.
Hence, the slope variable is an ideal candidate for the toilet variables. Specifically, we
have the following first-stage specification:
𝑡𝑜𝑖𝑙𝑒𝑡= 𝛾 + 𝛾 𝑠𝑙𝑜𝑝𝑒 + 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝛾 + 𝜔 + 𝜏 + 𝜇 , (2)where 𝑠𝑙𝑜𝑝𝑒 is the slope variable in village 𝑣 in year 𝑡 (=2008 or 2019), as explainedin the previous section. 𝜔 is a village fixed effect and 𝜏 is a time dummy taking 1 if theyear is 2019 and 0 otherwise. 𝜇 is the error term. Armed with this first-stage
specification (2), we estimate the second-stage specification (1) in which the toilet
variable is replaced with the fitted value from (2). Because the sample unit is “village,”
not an individual within the village, robust standard errors are used.

4.2 Results
4.2.1. OLS results
The upper half of Table 2 presents the estimation results of the availability of
toilets, using specification (1) with OLS (i.e., without IV). It shows the estimation results
using total under-five mortality, under-five mortality for boys, and under-five mortality
for girls, both with and without control variables.
The percentage of households without toilets in a village is associated with higher
total and boys’ under-five mortality in the village but not with girls’ under-five mortality(Columns (1), (2), and (3), respectively). The coefficient of 𝑅𝑛𝑜𝑡𝑜𝑖𝑙𝑒𝑡 is positive andstatistically significant for total and boys’ under-five mortality. The coefficient of
𝑅𝑛𝑜𝑡𝑜𝑖𝑙𝑒𝑡 for girls’ under-five mortality is positive but is not statistically significant.
These results hold even if the control variables are included (Columns (4), (5), and (6)).
The percentage of households with toilets in a village is associated with lower
total and boys’ under-five mortality in the village but not with girls’ under-five mortality(Columns (7), (8), and (9), respectively). The coefficient of 𝑅𝑡𝑜𝑖𝑙𝑒𝑡 is negative andstatistically significant for total and boys’ under-five mortality. The coefficient of 𝑅𝑡𝑜𝑖𝑙𝑒𝑡
for girls’ under-five mortality is negative but is not statistically significant. These results
hold even if the control variables are included (Columns (10), (11), and (12)).
In the bottom half of Table 2, we decompose 𝑅𝑡𝑜𝑖𝑙𝑒𝑡 into two variables:
𝑅𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡 and 𝑅𝑛𝑜𝑡𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡 . The former is the percentage ofhouseholds having improved toilets in a village, while the latter is the percentage ofhouseholds having non-improved toilets in a village; both were defined in the previous
section.
We find that the percentage of households with improved toilets in a village isnegatively associated with total, boys’, and girls’ under-five mortality in the village(Columns (13)-(18)). For the under-five mortality of boys, the coefficient of
𝑅𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡 is negative but statistically insignificant in Column (14). However,when the control variables are included, the coefficient of 𝑅𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡 becomes
negative and statistically significant. Total and girls’ under-five mortality are negativeand statistically significant both with and without the control variables.
8However, the percentage of households with non-improved toilets in a village is
not associated with under-five mortality. The coefficient of 𝑅𝑛𝑜𝑡𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡 does
not have any statistical significance except Column (24), which implies that thepercentage of households having non-improved toilets in a village is positively associatedwith under-five mortality for girls, implying non-improved toilets are harmful to thesurvival of young girls.
The results in Table 2 reveal that the popularization of toilets in a given village isnegatively associated with lower under-five mortality in that village, but that associationis entirely driven by the popularization of improved toilets in the village.
4.2.2. IV results
The results obtained from the OLS are insightful, but they may suffer from theendogeneity of the toilet variables, our main variables of our interest, as discussed earlier.
Hence, we perform an IV estimation for specification (1), and its first stage is (2) in 4.1.
The IV is the slope variable constructed from the highest and lowest altitude within avillage. The slope could ensure the smooth flow of running within the village or help toflush excrement into septic tanks. Or, conversely, the slope might make it difficult toconstruct a toilet. Whether a steeper slope helps or hinders the construction of toilets is
an empirical question; however, the slope itself is not directly related to under-five
mortality. The latter condition is an exclusion restriction of the IV that must be satisfied.
Ultimately, there is no established test that definitively investigates the validity of
instruments in the context of exclusion restrictions (Kiviet, 2020). However, thiscondition seems to hold in our context because we do not include under-five deaths
unrelated to sanitation, such as those caused by road accidents, land mines, and drowning,when computing under-five mortality. Therefore, the slope variable of each village is agood candidate IV for the toilet variables.
Table 3 presents the estimation results of the availability of toilets using the IV
estimations. From Panel B of Columns (1)–(6), we find that the IV is strongly andnegatively statistically correlated with the percentage of households without toilets in avillage. The coefficient of 𝑠𝑙𝑜𝑝𝑒 is negative and statistically significant at the 1% level.
The value of the F test of the excluded instrument is large enough (52.97 for Columns(1)–(3) and 93.19 for Columns (4)–(6)), rejecting the hypothesis that the IV is weak.
Therefore, we can proceed to the interpretation of the second-stage results for Columns(1)–(6).
From Panel A of Columns (1)–(3), we find that the percentage of households
without toilets in a village has a negative impact on the survival of young children, in
terms of total, boys’, and girls’ under-five mortality, but its impacts are not statisticallysignificant. However, when the control variables are appropriately included in Columns(4)–(6), the percentage of households without toilets in a village has a negative impact on
total, boys’, and girls’ under-five mortality, and the estimated impacts are statisticallysignificant at the 5% level for total and boys’ under-five mortality and at the 10% levelfor girls’ under-five mortality. The estimated impact is large. For instance, a 10percentage point increase in households without toilets in a village increases the total
under-five mortality rate by 0.221 percentage points. The negative impact on under-five
mortality is larger for boys than for girls.
Conversely, from Columns (7)–(12), we find that the percentage of households
having toilets in a village reduces total, boys’ and girls’ under-five mortality. From Panel9B of Columns (7)–(12), the IV is strongly and positively statistically correlated with thepercentage of households having toilets in a village. The value of the F test of the excluded
instrument is large enough to reject the hypothesis that the IV is weak. From panel A,
although the coefficient of 𝑅𝑡𝑜𝑖𝑙𝑒𝑡 is not statistically significant without the control
variables in Columns (7)–(9), it becomes statistically significant with the control variables
in Columns (10)–(12). Again, the estimated impact is large. For instance, a 10-percentage
point increase in households having toilets in a village decreases the total under-five
mortality rate by 0.218 percentage points. The positive impact of having a toilet in avillage on under-five mortality is larger for boys than for girls.
In Table 4, we decompose 𝑅𝑡𝑜𝑖𝑙𝑒𝑡 into two variables, 𝑅𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡 and𝑅𝑛𝑜𝑡𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡, and conduct IV estimations separately for 𝑅𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡 and𝑅𝑛𝑜𝑡𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡.
Columns (1)–(6) of Table 4 present the IV estimation results using
𝑅𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡 as the main variable of interest. From Panel A, the IV is strongly andpositively statistically correlated with the percentage of households having improved
toilets in a village. The value of the F test of the excluded instrument is large enough toreject the hypothesis that the IV is weak. Panel B shows the second-stage estimation
results. Although the coefficient of 𝑅𝑡𝑜𝑖𝑙𝑒𝑡 is not statistically significant without thecontrol variables in Columns (7)–(9), it becomes statistically significant with the control
variables in Columns (10)–(12). The estimated magnitude of the impacts is quite similar
to that obtained using 𝑅𝑡𝑜𝑖𝑙𝑒𝑡 in Table 3. For instance, a 10-percentage point increase in
the percentage of households with improved toilets in a village decreases the total underfive mortality rate by 0.227 percentage points. The positive impact of having improved
toilets in a village on under-five mortality is larger for boys than for girls.
Meanwhile, 𝑠𝑙𝑜𝑝𝑒 is not a valid IV for the percentage of households with nonimproved toilets. From Panel B of Columns (7)–(12), 𝑠𝑙𝑜𝑝𝑒 is not statistically correlatedwith 𝑅𝑛𝑜𝑡𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡 at all and, from the value of the F test of the excluded
instrument (1.12 in Columns (7)–(9) and 0.22 in Columns (10)–(12)), we cannot reject
the hypothesis that the IV is weak. Therefore, we cannot make any causal inference
regarding the impact of the percentage of households having non-improved toilets in avillage on under-five mortality. In other words, we would need a good IV for nonimproved toilets, but we leave this issue for a future study.
Our IV estimation results provide similar implications as the ones obtained fromthe OLS results. Combining the results in Tables 3 and 4, the popularization of toilets ina given village reduces total, boys’, and girls’ under-five mortality in that village, but thispositive impact of reducing under-five mortality comes entirely from the increase in theavailability of improved toilets in the village. This claim is supported not only by thesimilarity between the magnitude of the positive impact of the percentage of households
having toilets and the percentage of households having improved toilets but also by theplausible lack of impact of the percentage of households having non-improved toilets.
4.2.3. Robustness checks and a fallacy test
We conduct three robustness checks. First, in the main analyses, we dropped
likely outlier villages where the total under-five mortality was above 15. However, thisthreshold may be too restrictive. Therefore, we modify this threshold to 20 and re-estimate
the IV models. This includes 61 additional households in the sample. The results are
qualitatively and quantitatively very similar to the main results.
10Second, our main sample includes the villages in Phnom Penh, the capital city of
Cambodia. Its population is about 2.3 million, which accounts for about 15% of the total
population of Cambodia. The capital city differs substantially from other places in
Cambodia in many aspects. Therefore, we exclude the sample of villages in Phnom Penh,
and re-estimate the IV models. The results are qualitatively and quantitatively very similar
to the main results, with greater statistical significance in the case of 𝑅𝑛𝑜𝑡𝑜𝑖𝑙𝑒𝑡, 𝑅𝑡𝑜𝑖𝑙𝑒𝑡,
and 𝑅𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡.
Third, we apply the inverse hyperbolic sine transformation to the slope variable
and use it as the IV.8 This modified IV is still strongly correlated with 𝑅𝑛𝑜𝑡𝑜𝑖𝑙𝑒𝑡, 𝑅𝑡𝑜𝑖𝑙𝑒𝑡,
and 𝑅𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡; the coefficient of the modified IV is statistically significant at the
1% level in the specification including the control variables, although the values of the F
test of the excluded instrument are around 10. The second-stage results are qualitatively
similar to the main results, although the coefficients of 𝑅𝑛𝑜𝑡𝑜𝑖𝑙𝑒𝑡 , 𝑅𝑡𝑜𝑖𝑙𝑒𝑡 , and𝑅𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑𝑡𝑜𝑖𝑙𝑒𝑡 for girls’ under-five mortality become marginally insignificant (zvalues are around 1.45).
Finally, as a fallacy test, we use mortality rate of children aged 6–10 years (total,boys and girls) as the outcome variables. However, we do not get any statisticallysignificant coefficients of the toilet variables in the specification using the instrumental
variable estimations.
Therefore, our main results hold robustly and improved toilets are considered tobe effective in reducing mortality under the age of 5 years, but not above. These additional
results facilitate the discussion in the next section. 9

5. Discussion
According to our estimation results, the popularization of toilets in a given
village reduces total, boys’, and girls’ under-five mortality in that village, and this positive
impact of reducing under-five mortality comes entirely from the increased availability of
improved toilets in the village. Conversely, the increase in the percentage of households
without toilets increases total, boys’, and girls’ under-five mortality in the village. The
family members of those households are thought to engage in open defecation. This
practice contaminates drinking water sources, rivers, and food crops, leading to diseases,
stunted growth, chronic malnutrition, and in some cases, death. Our estimation results
clearly identify the worst-case scenario: under-five mortality. We have no clue whether
the increased availability of non-improve toilets within a village affects the under-five
mortality of the village or not.
Furthermore, it should be noted that in our analysis, positive and negative
externalities regarding toilet issues within a village are fully taken into account because
the sample unit is “village.” Therefore, when a household newly constructs an improved
toilet, it may positively impact the health condition of neighboring households and viceversa. Or, if a family member of a household without a toilet engages in open defecation,
it may cause health problems for neighboring households and vice versa. Our analyses
address these positive and negative externalities at the village level.
These findings have at least two policy implications. First, the policymakers must
8 See Footnote 7 for inverse hyperbolic sine transformation.
9 The estimation results for all the robustness checks and of the fallacy test are available uponrequest from the authors.

11

seriously consider the negative impacts of open defecation on under-five mortality inorder to save young children’s lives. A family member of a household without a toilet ishighly likely to engage in open defecation. Accordingly, preventing open defecation must
be a priority to save young children’s lives.
Second, one of the recommended policy measures to save young children’s lives
and to prevent open defecation at the same time is to construct toilets in households.
However, given the estimation results from our analyses, these need to be improved toilets.
Furthermore, such policy measures would be more effective if implemented following a“big push” approach (Rosenstein-Rodan 1943). In other words, targeting the whole
village, rather than individual households, would yield better outcomes. This is becausesuch a large-scale intervention can leverage the positive externalities of constructing
improved toilets. However, this requires a large amount of resources (i.e., money), andresource constraints are often a big challenge for policymakers in developing countries.

6. Conclusion
Using village-level balanced panel data of Cambodia, we investigate how theavailability of toilets in a given village impacts under-five mortality in that village. The
findings have at least two implications. First, an increase in the percentage of households
without toilets increases total, boys’, and girls’ under-five mortality in the village. Second,
only improved toilets appear to reduce under-five mortality, and whether non-improved
toilets can reduce under-five mortality or not is inconclusive. We emphasize that thepositive impacts of improved toilets are likely not the exclusive domain of under-five
mortality, but might also include the reduction of diarrhea, stunted growth, and so on.
Unfortunately, we were unable to investigate the impact of improved toilets on suchoutcomes due to a lack of data. However, such improvements are expected to yield better
outcomes beyond under-five mortality.
Finally, we point out some limitations of this study. First, we utilized the datafrom two censuses in order to create the village-level panel data (2008 and 2019). The
censuses were conducted 11 years apart, while the information on deaths covers only the
12 months prior to the census night, ignoring the deaths that occurred between 2009 and2017. If we had had more detailed information for each year, we could have performed
deeper analyses. However, in many developing countries, a census is conducted only
every 10 years or so, and thus this limitation may be unavoidable.
Another important limitation is the external validity of the results obtained in thecontext of Cambodia. We believe the general pattern found in our study is interpretable,
but a different setting with a different culture at a different stage of economic
development may yield a different set of results. Therefore, similar analyses in different
contexts are warranted.

References
Agha, Sohail. "The determinants of infant mortality in Pakistan." Social Science& Medicine 51.2 (2000): 199-208.
Anteneh, Andualem, and Abera Kumie. "Assessment of the impact of latrine
utilization on diarrhoeal diseases in the rural community of Hulet Ejju Enessie Woreda,
East Gojjam Zone, Amhara Region." Ethiopian Journal of Health Development 24.2
12

(2010).
Barnard, Sharmani, et al. "Impact of Indian Total Sanitation Campaign on latrine
coverage and use: a cross-sectional study in Orissa three years following programme
implementation." PloS one 8.8 (2013): e71438.
Cameron, Lisa, Susan Olivia, and Manisha Shah. "Scaling up sanitation: evidence
from an RCT in Indonesia." Journal of Development Economics 138 (2019): 1-16.
Cameron, Lisa, et al. "Sanitation, financial incentives and health spillovers: acluster randomised trial." Journal of Health Economics 77 (2021): 102456.
Chen, Baoqi, Fulei Jin, and Yaxin Zhu. "The impact of access to sanitary toilets
on rural adult residents' health: Evidence from the China family panel survey." Frontiersin Public Health 10 (2022): 1026714.
Cheng, Shikun, et al. "Toilet revolution in China." Journal of environmental
management 216 (2018): 347-356.
da Silva Wells, Carmen, and Christine Sijbesma. "Practical innovations forstrengthening Community-Led Total Sanitation: selected experience from
Asia." Development in Practice 22.3 (2012): 417-426.
Geruso, Michael, and Dean Spears. "Neighborhood sanitation and infantmortality." American Economic Journal: Applied Economics 10.2 (2018): 125-162.
Graham, Jay P., and Matthew L. Polizzotto. "Pit latrines and their impacts on
groundwater quality: a systematic review." Environmental health perspectives 121.5(2013): 521-530.
Kiviet, Jan F. "Testing the impossible: Identifying exclusion restrictions." Journalof Econometrics 218.2 (2020): 294-316.
Ministry of Planning of Cambodia 2020 “General Population Census of theKingdom of Cambodia 2019: National Report on FINAL CENSUS RESULTS” available
athttps://www.nis.gov.kh/nis/Census2019/Final%20General%20Population%20Census%
202019-English.pdf (last accessed on March 10, 2024)
Ndoziya, Alfonse Tapera, Zvikomborero Hoko, and Webster Gumindoga.
"Assessment of the impact of pit latrines on groundwater contamination in Hopley
Settlement, Harare, Zimbabwe." Journal of Water, Sanitation and Hygiene forDevelopment 9.3 (2019): 464-476.
Rosenstein-Rodan, Paul N. "Problems of industrialisation of eastern and southeastern Europe." The economic journal 53.210-211 (1943): 202-211.

13

Semba, Richard D., et al. "Relationship of the presence of a household improved
latrine with diarrhea and under-five child mortality in Indonesia." The American journalof tropical medicine and hygiene 84.3 (2011): 443.
Spears, Dean. "Effects of rural sanitation on infant mortality and human capital:
Evidence from India’s Total Sanitation Campaign." Washington, DC: Princeton
University (2012).
Spears, Dean. "Exposure to open defecation can account for the Indian enigma ofchild height." Journal of Development Economics 146 (2020): 102277.
Wang, Dongqin, and Yanni Shen. "Sanitation and work time: Evidence from thetoilet revolution in rural China." World Development 158 (2022): 105992.

14Figure 1: Location distribution of villages in Cambodia

Each dot is the location of villages.
Source: The National Institute of Statistics of Cambodia

15

Table 1: Descriptive statistics
Variable
Explanation

Summary

Rdeathunder5
Rdeathunder5M
Rdeathunder5F
Rnotoilet
RtoiletRimprovedtoilet
Rnotimprovedtoilet
slope
RF_HHHlNpop
RWat_well
RWat_pond
RWat_river
RM_ILT15_17
RF_ILT15_17
RM_ILT18_24
RF_ILT18_24
RM_ILT25_35
RF_ILT25_35
RM_ILT36_45
RF_ILT36_45
RZ_Fib_R_Elec
RTil_R_Elec
RNUM_FERTRNUM_PEST
RFamily_Car
lkmhe

0.656 (1.580)
0.544 (1.855)
0.793 (2.072)
46.411 (37.083)
53.352 (37.124)
46.350 (36.534)
7.002 (15.344)
12.843 (13.391)
15.995 (10.423)
7.341 (0.650)
41.704 (39.600)
13.039 (26.574)
11.905 (27.107)
4.806 (12.488)
4.841 (12.724)
7.624 (16.263)
7.972 (16.945)
10.715 (18.773)
11.559 (19.854)
14.526 (20.918)
15.675 (22.226)
20.195 (26.166)
14.466 (21.960)
58.286 (38.170)
38.376 (38.093)
0.031 (0.086)
1.775 (0.974)

Total mortality of children under five years old (per 100 children)
Mortality of boys under five years old (per 100 children)
Mortality of girls under five years old (per 100 children)
The percentage of household having a toilet at home
The percentage of household having no toilet at home
The percentage of household having a non-improved toilet at home
The percentage of household having an improved toilet at home
The difference between the highest and lowest altitudes in the village (in meters)
The percentage of households whose head is female
Population of the village (after IHS transformation)
The percentage of households whose main water source is from a well
The percentage of households whose main water source is from a pond
The percentage of households whose main water source is from a river
The percentage of illiterate males among aged 15–17 years
The percentage of illiterate females among aged 15–17 years
The percentage of illiterate males among aged 18–24 years
The percentage of illiterate females among aged 18–24 years
The percentage of illiterate males among age 25–35 years
The percentage of illiterate females among age 25–35 years
The percentage of illiterate males among age 36–45 years
The percentage of illiterate females among age 36–45 years
The percentage of households having a zinc or fibro roof house with electricity
The percentage of households having a tiled roof house with electricity
The percentage of households using chemical fertilizer
The percentage of households using pesticide
The number of family cars per household
Distance (km) from the village center to the health center (after IHS transformation)

Sample size
Mean (standard deviation)
IHS stands for “inverse hyperbolic sine.” See Footnote 7 in the text for more information.

24,1781

Table 2: OLS estimation results on the relationship between toilet availability and under-five mortality
(1)
(2)
(3)
(4)
(5)
(6)
(7)
VARIABLES
Total
Boy
Girl
Total
Boy
Girl
Total
Rnotoilet

0.0016*(0.0008)

0.0023***(0.0009)

0.0013(0.0011)0.0017*(0.0009)

0.0032***(0.0010)

(8)
Boy

(9)
Girl

(10)
Total

(11)
Boy

(12)
Girl

0.0023***(0.0009)
Yes
Yes
No
24,1780.061-0.0012(0.0011)
Yes
Yes
No
24,1780.004-0.0017*(0.0009)
Yes
Yes
Yes
24,1780.020

0.0032***(0.0010)
Yes
Yes
Yes
24,1780.066-0.0005(0.0012)
Yes
Yes
Yes
24,1780.009

0.0006(0.0012)Village fixed effects
Year dummyControls
Observations
R-squared

Yes
Yes
No
24,178
0.013

Yes
Yes
No
24,178
0.061

Yes
Yes
No
24,178
0.004

Yes
Yes
Yes
24,178
0.020

Yes
Yes
Yes
24,178
0.066

Yes
Yes
Yes
24,1780.009

0.0015*(0.0008)
Yes
Yes
No
24,1780.013

VARIABLES

(13)
Total

(14)
Boy

(15)
Girl

(16)
Total

(17)
Boy

(18)
Girl

(19)
Total

(20)
Boy

(21)
Girl

(22)
Total

(23)
Boy

(24)
Girl

0.0018**(0.0008)-0.0014(0.0009)

0.0025**(0.0011)0.0026***(0.0009)

0.0028***(0.0010)0.0028**(0.0012)0.0003(0.0011)
Yes
Yes
No
24,1780.013-0.0019(0.0013)
Yes
Yes
No
24,1780.061

0.0022(0.0014)
Yes
Yes
No
24,1780.004

0.0012(0.0011)
Yes
Yes
Yes
24,1780.020-0.0010(0.0013)
Yes
Yes
Yes
24,1780.065

0.0032**(0.0015)
Yes
Yes
Yes
24,1780.009

RtoiletRimprovedtoilet
Rnotimprovedtoilet

Village fixed effects
Yes
Yes
Yes
Year dummy
Yes
Yes
Yes
Controls
No
No
No
Observations
24,43624,43424,434R-squared
0.013
0.061
0.004
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Total: total mortality rate of children under five years old
Boy: mortality of boys under five years old
Girl: mortality of girls under five years old

Yes
Yes
Yes
24,319
0.020

Yes
Yes
Yes
24,317
0.066

Yes
Yes
Yes
24,3170.010

2

Table 3: IV estimation results on the relationship between toilet availability and under-five mortality
(1)
(2)
(3)
(4)
(5)
(6)
VARIABLES
Total
Boy
Girl
Total
Boy
Girl
Panel A: Second stage
Rnotoilet

0.01350.01730.0102(0.0115) (0.0132) (0.0139)

0.0221**(0.0097)

0.0237**(0.0111)

(7)
Total

(8)
Boy

(9)
Girl

(10)
Total

(11)
Boy

(12)
Girl

0.0214*(0.0117)

Rtoilet-0.0132 -0.0169 -0.0099(0.0112) (0.0129) (0.0136)

0.0218** 0.0234** 0.0211*(0.0096) (0.0110) (0.0115)

Panel B: First stageslope

F test of excluded instrument

Rnotoilet-0.2383***(0.0327)

Rnotoilet-0.2927***(0.0303)

Rtoilet0.2442***(0.0325)

Rtoilet0.2962***(0.0302)

53.00

93.17

56.41

96.05

Village fixed effects
Yes
Yes
Yes
Year dummy
Yes
Yes
Yes
Controls
No
No
No
Observations
24,17824,17824,178Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Total: total mortality rate of children under five years old
Boy: mortality of boys under five years old
Girl: mortality of girls under five years old

Yes
Yes
Yes
24,178Yes
Yes
Yes
24,178Yes
Yes
Yes
24,178Yes
Yes
No
24,178Yes
Yes
No
24,178Yes
Yes
No
24,178Yes
Yes
Yes
24,178Yes
Yes
Yes
24,178

3

Yes
Yes
Yes
24,178

Table 4: IV estimation results on the relationship between toilet availability by type and under-five mortality
(1)
(2)
(3)
(4)
(5)
(6)
(7)
VARIABLES
Total
Boy
Girl
Total
Boy
Girl
Total
Panel A: Second stage

Rimprovedtoilet-0.0119 -0.0152 -0.0090(0.0101) (0.0116) (0.0122)-0.0227**(0.0100)-0.0244**(0.0114)

(8)
Boy

(9)
Girl

(10)
Total

(11)
Boy

(12)
Girl

0.0220*(0.0120)

Rnotimprovedtoilet

0.12170.1557
0.0918(0.1543) (0.1890) (0.1516)-0.5475 -0.5873 -0.5303(1.1858) (1.2736) (1.1627)

Panel B: First stageslope

F test of excluded instrument

Rimprovedtoilet
0.2707***(0.0314)

Rimprovedtoilet
0.2844***(0.0286)

Rnotimprovedtoilet-0.0264(0.0249)

Rnotimprovedtoilet
0.0118(0.0251)

74.34

98.40

1.13

0.22

Village fixed effects
Yes
Yes
Yes
Year dummy
Yes
Yes
Yes
Controls
No
No
No
Observations
24,17824,17824,178Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Total: total mortality rate of children under five years old
Boy: mortality of boys under five years old
Girl: mortality of girls under five years old

Yes
Yes
Yes
24,178Yes
Yes
Yes
24,178Yes
Yes
Yes
24,178Yes
Yes
No
24,178Yes
Yes
No
24,178Yes
Yes
No
24,178Yes
Yes
Yes
24,178Yes
Yes
Yes
24,178

4

Yes
Yes
Yes
24,178