ISSN (Print) 0473-453X

Discussion Paper No. 1210

ISSN (Online) 2435-0982

UNINTENDED CONSEQUENCES
OF SANITATION INVESTMENT:NEGATIVE EXTERNALITIESON WATER QUALITYAND HEALTH IN INDIA

Kazuki Motohashi

July 2023

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Unintended Consequences of Sanitation Investment:
Negative Externalities on Water Quality and Healthin India
Kazuki Motohashi∗
Click here for the most recent version
This version: July 10, 2023
Abstract
Developing countries have increased sanitation investment to reduce diarrheal diseases.
However, the direct health benefits of latrine construction can be oﬀset by water pollution externalities due to poor treatment of fecal sludge. I estimate these negative
externalities of a sanitation policy in India that subsidized the construction of over
100 million latrines. Exploiting geographical variation in soil characteristics and thediﬀerential increase in latrine coverage across districts, I find that the policy increases
river pollution by 72%. While it reduces diarrheal mortality overall, this positive healtheﬀect is two-thirds smaller in areas with lower capacities for treatment of fecal sludge
where water pollution externalities are consequently larger.
JEL: I15, O13, Q53, Q56
Keywords: Sanitation, Water Quality, Health, Negative Externalities∗

Hitotsubashi University, Hitotsubashi Institute for Advanced Study. Email: kazuki.motohashi@r.hitu.ac.jp. I am grateful for guidance and advice from my advisors, Ujjayant Chakravorty, Shinsuke Tanaka,
and Marc Jeuland. For helpful comments, I thank Khushboo Aggarwal, Jenny Aker, Antonella Bancalari,
Yuxian Chen, Jishnu Das, Kyle Emerick, Jun Goto, Raymond Guiteras, Cynthia Kinnan, Takashi Kurosaki,
Daniele Lantagne, Leslie Martin, Patricia Ritter, Adam Storeygard, Haruka Takayama, Takanao Tanaka,
Kensuke Teshima, Zachary Wagner, Junichi Yamasaki, Akio Yamazaki, Hidefumi Yokoo, as well as seminar
participants at Hitotsubashi University, Tufts University, Waseda University, JADE/GRIPS, SWELL, Osaka
University, and conference participants at 2020 Young JADE Conference, 2021 SEEPS Camp, 2021 AERE
Summer Conference, 2021 NEUDC Conference, 2022 JEA Spring Meeting, Camp Resources XXVIII, 2022
AMES Tokyo, AMIE 2nd Workshop in Applied Microeconomics, Fourth SANEM-World Bank North America
Discussion Forum. This paper was awarded the 2022 Moriguchi Prize by the Institute of Social and Economic
Research, Osaka University.

11

Introduction

The importance of sanitation investment for improving human health in developing countries
is widely recognized by policymakers and researchers. Poor access to sanitation facilities
and the associated practice of open defecation adversely aﬀect child health by increasing
the occurrence of diarrheal diseases. Worldwide, according to the WHO/UNICEF data,
688 million people practiced open defecation in 2016, which is estimated to have caused
432,000 deaths in that year (Prüss-Ustün et al., 2019). To address these issues, developing
countries such as India and China have provided significant subsidies for the construction of
latrines.1 Their sanitation policies aim to improve child health by reducing open defecationand exposure to fecal matter near human habitation. These direct health benefits of latrine
construction have been well-documented (Hammer and Spears, 2016; Geruso and Spears,
2018; Spears, 2018; Cameron et al., 2019, 2021, 2022).
But little is known about the unintended negative externalities of latrine construction due
to poor treatment of fecal sludge, which can oﬀset the direct health benefits. The constructed
latrines accumulate a large volume of fecal sludge, which must be emptied periodically byvacuum trucks or by hand. The emptied fecal sludge should then be treated by wastewater
treatment plants to disinfect the remaining active pathogens. However, due to insuﬃcient
infrastructure, the emptied fecal sludge is instead, in many cases, dumped into rivers, thus
polluting the rivers. These water pollution externalities may decrease the overall eﬀectivenessof latrine construction in improving human health. In the extreme, latrine construction can,in fact, worsen health outcomes if water pollution externalities oﬀset any direct positiveeﬀects from reduced open defecation.
Therefore, I examine such negative externalities of latrine construction on water quality
and health in the context of India’s nationwide sanitation policy, the Swachh Bharat Mission(SBM). Since its inception in 2014, the SBM has allocated about 6 billion USD to subsidize
the construction of over 100 million latrines at the household level in rural India.2 As thelargest sanitation policy in the world, the SBM’s impacts deserve careful examination. I use
administrative panel datasets on the district-level number of latrines from 2012 to 2019 under
the SBM and the water quality of 1,189 monitoring stations along rivers in 337 districts from2007 to 2019 to examine the negative externality on water quality. I combine these withpanel data on district-level diarrheal mortality estimates to examine the eﬀect on health.
1

Like the Indian government’s Swachh Bharat Mission examined in this paper, the Chinese government’s“Toilet Revolution” built and upgraded over 10 million rural toilets in 2018 to increase sanitary toilets withsealed and covered septic tanks.
2
The types of latrines most commonly used in rural India are pit latrines and latrines with septic tanks.
These are not connected to sewer pipes and accumulate fecal sludge. The disposal of fecal sludge from these
latrines can result in water pollution externalities.

2

My analysis captures the diﬀerential eﬀects of latrine construction relative to open defecation, which can also cause water pollution externalities. Open defecation, practiced before
the latrine construction, generates small amounts of stool in a wide range of locations. Thesestools will be flushed into rivers only if it rains and if open defecation sites are close to rivers.
On the other hand, constructed latrines concentrate a large volume of fecal sludge that may
be dumped directly into rivers. Thus, the volume of fecal sludge that reaches rivers may
increase after latrine construction.
To identify the causal eﬀects of latrine construction under the SBM on water quality
and health, I use two identification strategies. First, I adopt an instrumental variable (IV)design that uses geographical variation in Available Water Capacity (AWC), a proxy forthe soil infiltration rate, interacted with a post-SBM indicator, as an instrument for thenumber of latrines.3 This IV design is conceptually similar to the diﬀerence-in-diﬀerences(DID) design, where the reduced-form regression uses AWC as a treatment variable. Higher
infiltration rates (lower AWC) increase the risk of groundwater contamination from thefecal sludge accumulated in latrines. To address this risk, an oﬃcial technical guideline(CPHEEO, 2013), which became eﬀective since the SBM’s inception in 2014, requires either
greater distances between latrines and wells or the addition of impervious materials inside
latrines in areas with high infiltration rates. So, lower AWC increases the diﬃculty and costof latrine construction after the SBM started in 2014. Indeed, I find that lower AWC is
associated with a smaller increase in latrines during the post-SBM period in the first stage.
Second, I adopt an additional DID design to examine the dynamic evolution of theeﬀects on water quality. In this design, I use a diﬀerential increase in latrine coverage acrossdistricts with diﬀerent levels of baseline coverage. All districts had, according to the SBM
database, achieved almost universal latrine coverage by the target date of 2019, regardlessof their baseline latrine coverage.4 Therefore, districts with lower baseline latrine coverage
have experienced a larger increase in latrine coverage, which may lead to a larger increasein water pollution. The DID event study based on this approach allows me to examine how
eﬀects on water quality evolve over time.
My results show that latrine construction under the SBM degrades river water quality while improving health overall. I find that one additional latrine per square kilometerincreases fecal coliform in rivers by 3% in the IV design. The total eﬀect of the SBM is
estimated to be a 72% increase in river pollution. This negative externality on water quality
3

Soil infiltration rate is the velocity or speed at which water enters the soil. Conversely, AWC is theamount of water that a soil can store that is available for use by plants.
4
Although there are some debates over whether the universal latrine coverage has actually been achieved
under the SBM, a recent survey found that 85% of the rural population used toilets in 2019-2020 (DDWS,
2020).

3

is similarly found in the DID design. However, one additional latrine per square kilometer
reduces the diarrheal post-neonatal mortality per 1,000 people by about 0.029, which is a
1.3% reduction in the IV design. The total eﬀect of the SBM is a 36% decrease in diarrheal
mortality. This positive overall health eﬀect suggests that the direct positive health eﬀect
outweighs the negative externality on health due to water pollution.
To identify the mechanism behind these negative externalities, I show that the eﬀects
of latrine construction on water quality vary by the level of complementary treatment offecal sludge. Suﬃcient infrastructure to treat fecal sludge can prevent the dumping of fecal
sludge, which is the main mechanism of the negative externalities. The most common suchinfrastructure takes the form of sewage treatment plants (STPs), which co-treat both urban
sewer and rural fecal sludge in India. So, I compare the eﬀects between areas with higher andlower treatment capacities of STPs than the median in the pre-SBM period. I find that thenegative externality on water quality is eliminated in areas with higher treatment capacities
in both IV and DID designs.5 Conversely, in areas with lower treatment capacities, thenegative externality on water quality is found to be substantial, and this eﬀect has becomelarger over time, which suggests that the dumping of fecal sludge is the mechanism.
The heterogeneous health eﬀects by the treatment capacity of fecal sludge suggest thatwater pollution externalities oﬀset positive health eﬀects. I find that the total eﬀect of the
SBM is a 26% decrease in diarrheal mortality in states with lower treatment capacities, about
two-thirds less than the 71% decrease in states with higher treatment capacities in the IV
design. This smaller positive health eﬀect in areas with lower treatment capacities suggeststhat water pollution externalities reduce the overall eﬀectiveness of latrine construction under
the SBM. The negative externality on health through the water pollution channel is further
supported by the finding that the negative externality on health is disproportionally borne bypeople who are more highly exposed to river pollution. Specifically, I find a smaller positiveeﬀect on diarrheal mortality in districts where a higher proportion of people live near rivers
in the IV design.
I then show that these negative externalities of latrine construction spill over to downstream districts. Dumped fecal sludge from latrines in upstream districts can flow downstream along rivers, causing negative externalities in downstream districts. I test thesespillover eﬀects in the modified IV design, where I estimate the eﬀects of upstream latrine
construction on downstream outcomes by instrumenting the upstream latrine construction
with upstream AWC. I find that upstream latrine construction increases downstream riverpollution, especially when upstream areas have lower treatment capacities. Upstream latrine
5

This null result in areas with higher treatment capacities suggests a low probability that other mechanisms, e.g., the direct seepage of fecal matter from latrines into rivers, aﬀect water quality.

4

construction decreases downstream diarrheal mortality overall because the positive healtheﬀect that comes from the corresponding increase in downstream latrine construction outweighs the water pollution externalities. However, I find that the positive health eﬀect iseliminated in the case of lower upstream treatment capacities, which suggests a large negative
externality on health due to the spillover of water pollution.
A variety of tests corroborate my findings on the negative externalities of latrine construction. First, in an indirect test of the exclusion restriction, I find that AWC has noeﬀects on outcomes prior to the SBM policy (before the technical guideline on infiltration
rates was published). Second, in a falsification test, I do not find eﬀects on unrelated water
quality indicators and malaria mortality. Third, my results are robust to the considerationof spillovers from neighboring districts, influence from urban areas, and a balanced panel.
My findings suggest that an enabling environment that includes eﬀective treatment offecal sludge can make sanitation policies more eﬀective by mitigating water pollution externalities. The back-of-the-envelope cost-benefit analysis shows that net mortality benefits(16.9 million USD) are worth the subsidy cost of the SBM policy (17.4 million USD) at the
district level. But complementing the latrine construction with suﬃcient sewage treatment
plants would increase the mortality benefits even more (by 18.1 million USD) with low additional construction and operating costs of sewage treatment plants (11.5 million USD).
More generally, my findings suggest the importance of promoting private goods together
with complementary public service provisions to prevent potential negative externalities.
This paper makes three contributions. First, I contribute to the literature on the eﬀects
of sanitation interventions by revealing the negative externalities of latrine construction
on the environment and health. Previous studies focused on the direct positive eﬀects ofsanitation interventions on child health and mortality (Clasen et al., 2014; Patil et al., 2014;
Duflo et al., 2015; Hammer and Spears, 2016; Geruso and Spears, 2018; Spears, 2018; Alsan
and Goldin, 2019; Cameron et al., 2019, 2021; Flynn and Marcus, 2021; Cameron et al.,2022).6 I complement these findings by showing that latrine construction additionally causes
unintended water pollution externalities, which oﬀset the direct positive health eﬀects. The
district-level analysis leveraging policy variation across hundreds of districts in India allows
me to examine these externalities that extend beyond villages, which was not captured inmost past studies relying on village-level field experiments. Moreover, this paper provides
6

Past literature also showed the positive eﬀects of such interventions on educational outcomes (Spears
and Lamba, 2016; Adukia, 2017; Orgill-Meyer and Pattanayak, 2020), labor supply (Wang and Shen, 2022),
and violence against women (Hossain et al., 2022). Another strand of literature examined the constraints
behind latrine adoption, including financial constraints, inadequate information concerning the benefits oflatrines and costs of open defecation (Pattanayak et al., 2009; Guiteras et al., 2015; Yishay et al., 2017;
Lipscomb and Schechter, 2018; Orgill-Meyer et al., 2019), and religious and caste beliefs that discourage
latrine use (Spears and Thorat, 2019; Adukia et al., 2021).

5

new evidence on the environmental and health eﬀects of the SBM policy, which is the world’s
largest program of latrine construction.
Second, I contribute to the literature on the causes and eﬀects of water pollution by providing the first causal estimates of the eﬀects of latrine construction on river water quality.7
Previous literature has studied how water quality is aﬀected by water quality regulations(Greenstone and Hanna, 2014; Keiser and Shapiro, 2019) and political boundaries (Kahn
et al., 2015; Lipscomb and Mobarak, 2016; Motohashi and Toya, 2022). Another set of studies has investigated the eﬀects of industrial and agricultural wastewater on health outcomes,
including digestive cancer (Ebenstein, 2012), infant mortality (Brainerd and Menon, 2014;
Do et al., 2018), and birth outcomes (Dias et al., 2019). This paper shows that latrine
construction substantially increases river pollution (by 72% in the case of the SBM, which isa large eﬀect) and that this increased domestic wastewater from latrines can oﬀset positive
health eﬀects.
Third, more broadly, this paper advances the literature on the unintended negative eﬀects
of health policies in developing countries by showing that the negative eﬀects due to the displacement of pollution sources can be minimized with suﬃcient complementary investment.
Past literature documented how health policies unintentionally worsen health outcomes dueto the reduction in complementary health behaviors (Bennett, 2012; Jeuland et al., 2021),
switching to alternative unsafe health behaviors (Buchmann et al., 2019), and abandonmentof and delays in project completion (Bancalari, 2020). This paper shows the unintended
negative eﬀects of health policies can also be caused by the displacement of pollution sourcesfrom open defecation sites to rivers where fecal sludge is dumped. I then show that these effects can be eliminated with suﬃcient complementary infrastructure for controlling pollution
sources.
The rest of the paper is organized as follows. Section 2 describes the SBM and its potential
eﬀects on water quality and health. Section 3 lays out a simple conceptual framework to
guide the subsequent empirical analysis. Sections 4 and 5 describe the data and empiricalstrategies. Section 6 presents the baseline results of the eﬀects on water quality and health.
Section 7 presents the heterogeneous eﬀects of latrine construction. Section 8 concludes.
7

Public health literature has examined the association between pit latrines and groundwater quality
based on a limited sample of a few hundred latrines (Graham and Polizzotto (2013) reviewed these studies).
This paper estimates the causal eﬀects of latrines on river water quality based on nationwide administrative
data.

6

2

Background2.1

Latrine Construction under Swachh Bharat Mission in India

To eliminate open defecation, the Indian government has subsidized the construction of over
100 million latrines in rural India under the Swachh Bharat Mission (SBM), the largest
sanitation policy in the world.
In India, a large number of people have historically practiced open defecation, which
adversely aﬀects child health by increasing the occurrence of diarrheal diseases. About 470
million people in India practiced open defecation in 2013, according to the WHO/UNICEF
Joint Monitoring Programme. As such, India had the highest number of people practicing
open defecation in the world, more than 10 times that of the country with the second-highest
number, Nigeria, in 2013 (Appendix Figure A1).
To eliminate open defecation and improve human health, the Indian government has
implemented a nationwide sanitation policy that subsidizes latrine construction, the SBM.8
Since its inception in 2014, the SBM has set an ambitious goal of achieving universal latrine
coverage by October 2nd, 2019, the 150th anniversary of Mahatma Gandhi’s birth. To
achieve this goal, the SBM significantly increased the amount of subsidy to about 150 USD(12,000 INR) per household. This subsidy covers most of the initial cost of basic latrines inrural India.
With this big push to construct latrines, the SBM has become the largest sanitation
policy in the world, building over 100 million latrines in rural India by spending about 6
billion USD.9 Latrine coverage dramatically increased from 39.2% in 2013 to almost 100% in
2019, according to the administrative database of the SBM (Figure 1). A recent governmentcommissioned survey also found that 85% of the rural population used toilets in 2019-2020,
which suggests almost universal latrine coverage had been achieved under the SBM as shownin the administrative database (DDWS, 2020). As the largest sanitation policy in the world,
the SBM’s impacts on water quality and health deserve careful examination.
8

Swachh Bharat Mission (SBM) is the most recent policy out of four consecutive sanitation policies atthe central government level. Although state governments have primary responsibility for public health andsanitation, these central government-level policies were meant to influence the state-level sanitation policies
through policy guidance and budget allocation.
9
According to the actual expenditure shown in the annual budgets of the Indian government, the central
government has spent about 6.37 billion USD (497 billion INR) from 2014 to 2019. The data source on thenumber of built latrines is the SBM website (https://swachhbharatmission.gov.in/SBMCMS/about-us.h
tm).

7

2.2

Negative Externality of Latrine Construction on Water Quality

The large-scale latrine construction under the SBM may cause a unintended negative externality on river water quality due to poor treatment of fecal sludge emptied from latrines.
Dumping fecal sludge emptied from latrines can cause river pollution. Latrines accumulate a large volume of fecal sludge, which must be emptied either by vacuum trucks
or manually. The emptied fecal sludge should then be transported to and treated at thewastewater treatment plants to disinfect the remaining active pathogens.10 However, due toinsuﬃcient infrastructure, the emptied fecal sludge is instead frequently dumped into rivers,
which can cause water pollution in rivers.11
My analysis captures the diﬀerential eﬀects of latrine construction relative to open defecation, which can also cause the water pollution externality. Open defecation, practiced
before the latrine construction, generates small amounts of stool in a wide range of locations. These stools decompose in sunlight and will be flushed into rivers only if it rains andif open defecation sites are close to rivers. On the other hand, latrines accumulate a large
volume of fecal sludge that may be emptied and dumped directly into rivers. Thus, thevolume of fecal sludge that reaches rivers may increase after latrine construction.
This paper argues that the water pollution externality of latrine construction is unintended, as evidenced by the absence of policy targets addressing the treatment of fecal
sludge. Under the SBM, a village is declared and verified to be open defecation free basedon a checklist of indicators including access to toilet facilities, 100% usage, fly-proofing, andsafe septage disposal as per the SBM guideline (MDWS, 2018). In the safe septage disposal
section, while the checklist stipulates that toilets should be connected to pits or septic tanks,
it lacks specific guidance on how the emptied fecal sludge should be properly treated.
2.3

Negative Externality of Latrine Construction on Health

The latrine construction under the SBM may also result in a negative externality on healththrough increasing river pollution. Exposure to polluted water increases the risks of diarrheal
diseases and mortality for people who uses river water in their daily lives.
These water pollution externalities of latrine construction may oﬀset direct positive healtheﬀects from reduced open defecation. Latrine construction has direct positive eﬀects on
health by reducing open defecation and exposure to fecal matter near human habitation,
10

Although the fecal sludge contained in pits degrades to some degree with time, pathogens can bepresent even after long-term storage. The primary objective of pit latrines is fecal containment rather thanpathogen reduction (Orner et al., 2018).
11
An ethnographic study on 32 truck operators that clean latrines showed that these operators practiceillegal dumping, although this study focuses on urban areas in Bangalore, Karnataka (Prasad and Ray,2019). Illegal dumping and associated water pollution have also been pointed out by news media.

8while at the same time, latrine construction may indirectly cause negative externality on
health due to increased river pollution. The magnitudes of both the direct positive healtheﬀects and the indirect negative externality determine the sign of the overall health eﬀect.
My analysis investigates this overall health eﬀect in terms of diarrheal mortality.
2.4

Complementary Treatment of Fecal Sludge

The magnitude of negative externalities of latrine construction on water quality and healthvaries by the level of complementary treatment of fecal sludge. The adequate treatment of
emptied fecal sludge prevents the dumping of fecal sludge, which is the main mechanism of
the negative externalities of latrine construction.
In India, local governments are tasked with developing infrastructure that treats emptied
fecal sludge, i.e., sewage treatment plants (STPs) and fecal sludge treatment plants (FSTPs).
STPs are large-scale facilities that have been available in India for a long time. India had
about 500 STPs in operation in 2015 (CPCB, 2015). STPs are typically designed to treat
urban sewage, but they are also increasingly used to co-treat fecal sludge due to the underutilization of STP capacities in India.12 On the other hand, FSTPs are newly developed
small-scale facilities for treating fecal sludge. FSTPs started operating in 2014, and there
were only about only 30 FSTPs in operation at the end of 2019 (Rao et al., 2020).
I use geographical variation in STP capacity in the pre-SBM period to examine theheterogeneous eﬀects on water quality and health.13 The negative externality on water
quality is expected to be substantial in areas with lower treatment capacities. Thus, in theseareas, the negative externality on health is also expected to be larger, which suggests a smaller
overall positive health eﬀect. Conversely, I expect to find smaller negative externalities inareas with high treatment capacities. In the following sections, I first construct a conceptual
framework to derive these predictions and then test them in empirical analysis.

3

Conceptual Framework on Negative Externalities of Latrine
Construction

I present a simple conceptual framework to show how latrine construction under the SBM
causes negative externalities that oﬀset direct health benefits. A decrease in a latrine priceunder the subsidy increases the number of constructed latrines, which increases the marginal
12

Although the data on the actual prevalence of co-treatment is not available, case studies are available
for STPs in Panaji (Goa), Kanpur (Uttar Pradesh), and Chennai (Tamil Nadu). Also, policies and guidelines
mentioning the co-treatments at STPs are available in multiple states, such as Punjab, Madhya Pradesh,
Jharkhand, and Rajasthan (Gupta et al., 2018).
13
I do not consider FSTP capacities because there were no FSTPs in the pre-SBM period.

9damage (negative externalities), which oﬀsets the marginal benefit (health benefits). The
magnitude of these negative externalities depends on the treatment capacity of fecal sludge.
I consider a district that has N households which can decide whether or not to constructlatrines. I suppose that a given household can build a latrine by paying a fixed price (ppre ).14
I denote the maximum number of latrines that can be built in this district as Qmax = N .
The fecal sludge emptied from latrines in this district is treated by sewage treatment
plants (STPs). I give the treatment capacity of fecal sludge as Qstp ∈ [0, Qmax ] where Qstp
can be interpreted as the number of latrines whose fecal sludge can be treated by STPs.
Thus, when the number of latrines (Q) exceeds Qstp , Q − Qstp amount of fecal sludge isdumped into rivers, which causes negative externalities on water quality and health. In thismax
conceptual framework, I analyze two cases: (i) low treatment capacity (Qstp ≤ Q 2 ) andmax(ii) high treatment capacity (Qstp > Q 2 ).
Figure 2 shows the marginal benefit (M B), marginal cost (M C), marginal damage (M D),
and social marginal cost (SM C) of latrine construction for low treatment capacity case(Panel A) and high treatment capacity case (Panel B).
Both panels show the same M B and M C curves. The M B curve represents direct
health benefits that come from reduced open defecation and exposure to fecal matter nearhuman habitation.15 This curve is downward-sloping because some households benefit morethan other households — for instance, if they have more infants who are vulnerable todiarrhea. As for M C, the pre-SBM curves are constant at the constant price of latrines(M Cpre = ppre ). The M C curves are shifted down by subsidy under the SBM. Households
receive a subsidy of about 150 USD for latrine construction, so the post-SBM eﬀective priceof latrines (M Cpost = ppost ) becomes significantly lower than ppre .
The main diﬀerence between Panels A and B is SM C. If the treatment capacity (Qstp )is low (Panel A), the M D, i.e., the negative externality on health due to river pollution,
becomes non-zero, starting from the lower number of latrines. On the other hand, if thetreatment capacity (Qstp ) is high (Panel B), the M D occurs only at a larger number of
latrines. Here I assume the non-linear dose-response relationship: the larger the volumeof dumped fecal sludge (Q − Qstp ), the larger the marginal negative externality on health(M D).16 . The SM C curves reflect the diﬀerences in M D curves because SM C = M C+M D.
14

The latrine price can include both the initial construction cost of a latrine and the present value ofmarginal costs for emptying fecal sludge periodically.
15
In this conceptual framework, MB is assumed to only represent direct health benefits, i.e., reduction in the risks of diarrhea and diarrheal mortality, although there could be other benefits, including an
improvement in educational outcomes and reduction in violence against women.
16
The non-linear relationship is suggested by a classic epidemiological study (Moe et al., 1991), whichshows the evidence of threshold eﬀects where significantly higher rates of diarrheal disease are observed once
the fecal contamination level in drinking water reaches a certain threshold.

10Based on this conceptual framework, I examine the welfare eﬀects of latrine construction
under the SBM in Figure 2. If the treatment capacity is low (Panel A), pre-SBM market
equilibrium quantity is Qepre at the intersection of M B and M Cpre , and pre-SBM optimalquantity is Q∗pre at the intersection of M B and SM Cpre . The wedge between Qepre and Q∗pre ,caused by M D (negative externality), generates deadweight loss (DW Lpre ). Then, the eﬀectof the SBM is to decrease the marginal cost from M Cpre to M Cpost through subsidy. Thus,
the number of latrines increases significantly from Qepre to Qepost . This increase in latrines
causes a large increase in negative externality due to low treatment capacity. Deadweightloss significantly increases from DW Lpre to DW Lpost . On the other hand, if the treatment
capacity is high (Panel B), the increase in deadweight loss due to latrine construction islimited because the negative externality only occurs at a large number of latrines. The
comparison of Panels A and B suggests that subsidies under the SBM adversely impact
welfare more significantly in the case of low treatment capacity.
Moreover, I examine the eﬀects of latrine construction under the SBM on water quality
and health in Figure 3, which is based on the welfare analysis in Figure 2. In Figure 3, thetotal benefit represents the total direct health eﬀects, while the total damage represents thetotal negative externality on health due to water pollution.17 The diﬀerence in the total
benefit and total damage (net benefit) is examined as a health outcome in the empiricalanalysis.18 The total damage can be interpreted as a degree of water pollution, whichcorresponds to the water quality outcome in the empirical analysis.
The subsequent empirical analysis estimates the eﬀects of the increase in the numberof latrines at market equilibrium from Qepre to Qepost on water quality and health under the
SBM. As shown in Figure 3, there are three testable hypotheses in the empirical analysis.
The first hypothesis is tested in the baseline analysis (Section 6), and the second and third
hypotheses are tested in the heterogeneity analysis (Section 7).
1. The SBM improves health overall (increase in net benefit) and increases water pollution(increase in total damage) regardless of treatment capacity.19
2. The magnitude of positive health eﬀects is smaller in the case of low treatment capacity.
3. The magnitude of negative eﬀects on water quality (increased water pollution) is larger
in the case of low treatment capacity.
17

The total benefit in Figure 3 is the area under the M B curves of Figure 2. The total damage in Figure
3 is the area bounded by the SM C and M C curves of Figure 2.
18
I assume that the total benefit is larger than the total damage. In this case, the net benefit is positive,
which means that latrines are health-improving. This is consistent with the empirical results of this paper.
19
While theoretically, net benefit may decrease, Figure 3 demonstrates a case where the total benefit
increases more substantially than the total damage (increase in net benefit), which is consistent with theempirical results of this paper.

11

4

Data

I combine administrative datasets on the water quality of rivers and latrines across India to
examine the negative externality of latrine construction on water quality. I use district-level
diarrheal mortality estimates as an additional outcome to examine the negative externality
on health. In the IV design, I use Available Water Capacity (AWC) as an instrument
for latrines. Moreover, I control for other district characteristics that might aﬀect latrine
construction and outcomes. All of these data are spatially matched based on the 2011
district-level boundary data.
4.1

Water Quality

This paper adopts two outcome variables: water quality and health. As a first outcome, I use
detailed water quality data from 1,189 monitoring stations along rivers in India from 2007 to2019 (Figure 4). The data is based on the National Water Quality Monitoring Programme(NWMP), managed by the Central Pollution Control Board (CPCB).20
Among multiple water quality indicators, I use fecal coliform as a main indicator because
fecal coliform is a direct measurement of the fecal contamination caused by the fecal sludge
emptied from latrines.21 A higher value of fecal coliform means a higher level of fecal contamination. In the analysis, I use the average of maximum and minimum values of fecal
coliform because mean values are only available up to 2014.22 Lastly, since the distributionof fecal coliform is approximately log normal, I use the log of fecal coliform as a water quality
outcome in the analysis.
4.2

HealthAnother outcome variable is health. I use the diarrheal mortality estimates (per 1,000 people)
from 2000 to 2019, provided as 5-kilometer raster data by the Institute for Health Metrics
and Evaluation (IHME, 2020b). This dataset includes estimates of diarrheal mortality infive age groups, i.e., early-neonatal (0-6 days), late-neonatal (7-27 days), post-neonatal (28days - 1 year), ages 1-4, and under age 5. These estimates are constructed based on thedatasets of multiple household surveys, including the India Demographic and Health Survey,
20
I additionally identify the basin of each monitoring station by using the GPS coordinates of monitoring
stations and the “Watershed Map of India” of the ML Infomap.
21
I do not use other common water quality indicators such as Biological Oxygen Demand (BOD) andDissolved Oxygen (DO) because they capture the overall level of water contamination from various pollution
sources, including agricultural and industrial wastewater.
22
The correlation between mean values and average values of fecal coliform is 0.9973, which suggests that
average values are good proxies of mean values.

12the India District Level Household Survey, and the India Human Development Survey. For
the analysis, I compute the district-level mean of these estimates based on this raster data
and district boundary data.
4.3

Latrines

The treatment variable is the number of latrines. I use the administrative data on the districtlevel number of household latrines from 2012 to 2019 in rural India, which are scraped fromthe database available on the SBM website. Based on this dataset, I compute the numberof latrines per square kilometer for the IV design. Moreover, I compute the 2013 latrine
coverage by dividing the number of household latrines in 2013 by the total number of recordedhouseholds in each district for the DID design.23
One concern about this dataset is that the number of latrines may be systematicallyoverestimated because the data are collected by the Indian government under the SBM policy,
whose aim is to achieve universal latrine coverage. I deal with this potential measurement
error by adopting an IV design. Furthermore, it should be noted that the DID design,
relying on the variation in baseline latrine coverages, could yield lower-bound estimates
because actual latrine coverages could be lower than those in the administrative data.
4.4

Available Water Capacity

In the IV design, I use Available Water Capacity (AWC) as an instrument for the number of
latrines. AWC is the amount of water that a soil can store that is available for use by plants.
AWC represents a soil infiltration rate, i.e., the velocity or speed at which water enters
the soil. Higher AWC is associated with a lower soil infiltration rate. The AWC data isavailable in the Harmonized World Soil Database v1.2 provided by the Food and Agriculture
Organization of the United Nations (FAO). This database provides 30 arc-second raster dataof AWC across the globe. I compute the district-level mean of AWC based on this rasterdata and district boundary data.
4.5

Other District Characteristics

I supplement the above information with further data to account for district characteristics
that might aﬀect water quality and health.
First, I use 0.25-degree raster data of precipitation from 2007 to 2019, provided by the
India Meteorological Department (Pai et al., 2014). I aggregate daily raw data into annual
23

I do not consider whether constructed latrines are used due to a lack of district-level panel data on
latrine usage. Because the usage rate of constructed latrines can be lower than 100% in India, my estimates
represent the lower bound of the eﬀect of the increase in latrine usage.

13

data. Then, I construct the district-level mean of precipitation based on this raster data anddistrict boundary data.
Second, I use 15 arc second (<500m at the Equator) raster data of nighttime light toaccount for the size of the economy at the district level. Specifically, I use the V.2 annualcomposites of Visible and Infrared Imaging Suite (VIIRS) Day Night Band (Elvidge et al.,2021).24 I compute the district-level mean of nighttime luminosity in the pre-SBM period
based on the annual composite of 2013.
Lastly, I use data on district-level socio-demographic characteristics, including population, the proportions of Scheduled Caste and Scheduled Tribe members, and literacy rates
in rural India, in the 2011 Census of India.
4.6

Data Matching and Sample Construction

To match water quality data with other data, I first use the 2011 district-level boundary dataof the ML Infomap and the GPS coordinates of monitoring stations to identify the districts
where monitoring stations are located. Then, I match water quality data with latrine data
based on district names.25 All other data are similarly matched to water quality and latrine
data by following the 2011 district boundary.
After the data matching, I construct an unbalanced panel data of 1,189 water quality
monitoring stations in 337 districts from 2007 to 2019.26 In the IV design, I use data of
1,189 stations from 2012 because latrine data is only available from that year. In the DID
specification, which relies only on latrine coverage in 2013 as a treatment, I use longer panel
data of the same stations from 2007 to 2019. When I examine the health eﬀects, I focus
on the same 337 districts used in the analysis of water quality. Specifically, I construct abalanced panel of 337 districts from 2012 to 2019.
Table 1 shows the summary statistics of all variables used in the analysis. Time-varying
variables are shown separately for both pre-SBM and post-SBM periods.
24

I use the values of masked average radiance that represents stable lights from which background noises,
biomass burning, and aurora are removed.
25
I deal with the changes in the district boundary by ensuring that all data are organized according to
the 2011 boundary. Latrine data based on the 2019 boundary are aggregated to follow the 2011 boundary
by considering the district splits from 2011 to 2019.
26
In the main specification, I use an unbalanced panel data of water quality to cover as many districts
as possible to enhance the external validity. As a robustness check, I run the same analysis on a balanced
panel in Section 6.4.

14

5

Empirical Strategy

I empirically examine the eﬀects of latrine construction under the SBM on river water quality
and health. Estimates of ordinary least squares (OLS) might be biased due to reverse
causality and omitted variables. For example, increased water pollution may impede latrine
construction to prevent further water pollution, leading to reverse causality. Moreover,spurious correlations may be caused by unobservables that aﬀect both latrine construction
and water quality. For example, unobserved persistent belief in open defecation may reduce
the probability of latrine construction and aﬀect water quality due to the associated practiceof open defecation.
Therefore, I use two identification strategies to estimate the causal eﬀects of latrine
construction. First, I adopt an IV design, which uses geographical variation in Available
Water Capacity (AWC) interacted with a post-SBM indicator as an instrument for thenumber of latrines. Second, I additionally adopt a DID design to examine the dynamic
evolution of the eﬀects, where I use a diﬀerential increase in latrine coverage across districts
with diﬀerent levels of baseline coverage. In both designs, I include monitoring station fixedeﬀects in the case of water quality and district fixed eﬀects in the case of health to control
for time-invariant unobservables.
5.1

Instrumental Variable Design

In the IV design, I use geographical variation in Available Water Capacity (AWC), a proxy forthe soil infiltration rate, interacted with a post-SBM indicator, as an instrument for latrine
construction to examine the eﬀects of latrine construction on water quality and health.
Higher soil infiltration rates (lower AWC) increase the risk of groundwater contaminationfrom the fecal sludge accumulated in pit latrines, the widely adopted type in rural India.
Pit latrines consist of a hole, called a pit, that accumulates fecal sludge without a completely sealed wall. So, pathogens inside the fecal sludge can percolate into soils, potentiallycausing fecal contamination of groundwater sources such as wells.27 The degree of this fecal
contamination depends on the soil infiltration rates.
To address the risk of groundwater contamination, an oﬃcial technical guideline (CPHEEO,
2013), which became eﬀective since the SBM’s inception in 2014, requires additional precautionary measures for latrine construction in areas with high infiltration rates (lower AWC).
So, lower AWC increases the diﬃculty and cost of latrine construction after the SBM started
27

This fecal contamination of groundwater sources is diﬀerent from the river pollution that is caused by
the dumping of fecal sludge emptied from latrines. The former is considered to motivate the IV design, while
the latter is the eﬀect I investigate in this paper.

15

in 2014. If the eﬀective size (ES) of the soil is 0.2 mm or less, i.e., a lower infiltration rate
(higher AWC), pits can be located at a minimum distance of 3 meters from water sources
such as wells. However, for coarser soils with ES greater than 0.2 mm, i.e., a higher infiltration rate (lower AWC), the minimum distance must be greater than 3 meters. In cases where
the requirement for minimum distance cannot be met, additional investments are mandated
for latrine construction. Specifically, the bottom of the pits must be sealed oﬀ with impervious materials such as puddle clay and plastic sheeting, and a 500 mm thick envelope offine sand of 0.2 mm eﬀective size must surround the pit.28 In short, higher infiltration rates(lower AWC) make it more diﬃcult to find the space for latrines or increase the cost of theirconstruction due to these additional investments after the SBM started in 2014.29
Therefore, I use AWC interacted with a post-SBM indicator as an instrument for thenumber of latrines. Figure 5 shows substantial variation in AWC across districts in India.
In the first stage, an area with lower AWC, i.e., a higher infiltration rate, is expected toexperience a smaller increase in the number of latrines. As expected, Column 2 of Tables 2
and 3 show that one mm/m decrease in AWC is associated with a smaller increase in thenumber of latrines per square kilometer by 0.283 and 0.244, respectively. The F-statistics ofthe first stage regressions are relatively high (29.954 and 33.374).
In the analysis of eﬀects on water quality, I adopt the following two-stage least squares
regressions, where equations 1 and 2 are second stage and first stage regressions, respectively.
This IV design is conceptually similar to the DID design, where the reduced-form regression
in this IV design uses AWC as a continuous treatment variable.
Yi,d,t = α + βIV Latrined,t + γ1 P recipd,t + δi + θt + εi,t(1)Latrined,t = π1 + π2 AW Cd · P ostt + π3 P recipd,t + δi + θt + νi,t(2)

where Yi,d,t is a water quality indicator, represented by the logarithm of fecal coliform, atmonitoring station i inside district d in year t. Latrined,t is the number of latrines per
square kilometer at district d of monitoring station i in year t. P recipd,t is precipitation
at district d in year t, which is added to control for the rainfall and associated floods that
may aﬀect both water quality and latrine construction. A time-variant instrument for thepanel data analysis is constructed by interacting time-invariant AWC at district d with a28

Noncompliance with these requirements in the technical guideline can weaken the first-stage relationship. I discuss the F-statistics of the first-stage relationship below, as well as show the confidence intervalof the Anderson and Rubin (1949) test that is robust to the weak instrument in Section 6.1.
29
This relationship applies not only to pit latrines but also to latrines with septic tanks, which are usuallyequipped with soak pits that treat septic tank eﬄuent. Soak pits are subject to similar requirements that
depend on soil infiltration rates to prevent groundwater contamination (CPHEEO, 2013).

16

post-SBM indicator that takes the value one after 2014 when SBM started. δi is monitoring
station fixed eﬀects, and θt is year fixed eﬀects. Standard errors are clustered at the districtlevel because the variation in the number of latrines is observed at the district level. The
coeﬃcient of interest is βIV , and I expect it to be positive.
In the analysis of health eﬀects, I adopt similar regressions, where the outcome variable,
Yd,t , is changed to district-level diarrheal mortality. As an outcome, I focus on post-neonatal
mortality because it is the closest available measure to infant mortality, which is often used
as an outcome in the context of sanitation and water pollution (Do et al., 2018; Geruso
and Spears, 2018).30 I use district fixed eﬀects instead of monitoring station fixed eﬀects.
Standard errors are similarly clustered at the district level. In this health analysis, the signof the coeﬃcient of interest, βIV , is determined by the magnitudes of both positive healtheﬀects and negative externality.
5.2

Validity of Exclusion Restriction

The IV design builds on a key assumption of exclusion restriction. The instrument, AW Cd ·P ostt , must aﬀect water quality and health only through the channel of latrine construction
after controlling for precipitation, monitoring station (district) fixed eﬀects, and year fixedeﬀects.
When water quality is an outcome, one potential concern is that AWC aﬀects the agricultural yield of crops, which in turn aﬀects the volume of agricultural runoﬀ, leading toa change in the water quality. To address this concern, I choose fecal coliform as a water
quality indicator because fecal coliform is unrelated to the production of crops.
There are also legitimate concerns that AWC can aﬀect health outcomes through otherchannels. For instance, AWC might aﬀect the agricultural yield of crops, which in turn
determines a household’s income, which in turn aﬀects the level of health investments, leading
to changes in health conditions.31 Moreover, AWC might aﬀect the level of groundwaterpollution resulting from open defecation, which in turn aﬀects health outcomes.
As a formal test of the validity of the exclusion restriction, I run reduced-form regressionsof the outcomes of water quality and health on the interaction of AWC and year dummies.
The exclusion restriction suggests that AWC should not aﬀect the outcomes prior to the
SBM policy. During the pre-SBM period, AWC is unlikely to aﬀect latrine construction
because the oﬃcial technical guideline that requires the consideration of soil infiltrations
30
Post-neonatal mortality and infant mortality refer to the probabilities of a child dying between 28 days
after birth and the age of one year and dying between the birth and the age of one year, respectively.
31
Although district fixed eﬀects control for the time-invariant agricultural productivity across districts,
diﬀerential growth in the agricultural yield caused by diﬀerent levels of AWC might be present, leading topotential diﬀerential increases in income and health investment.

17

had not been published until 2013, just before the start of the SBM. Thus, the associationof AWC and outcomes during the pre-SBM period captures the causal pathways other than
through latrines. Conversely, AWC is expected to have strong relationships with outcomes
after the SBM started to incentivize latrine construction in 2014. Indeed, Figure 7 shows
no diﬀerential eﬀects of AWC on both outcomes of water quality and health up to 2013. In
contrast, starting from 2014, larger AWC leads to an increase in fecal coliform and a decreasein diarrheal post-neonatal mortality.32
As a second test of the validity of the exclusion restriction, I conduct falsification tests
that examine the eﬀects on other water quality and health indicators unrelated to fecal contamination. Specifically, I examine the eﬀects of latrine construction on water temperature,
pH, and malaria mortality for ages 0-4.33 The exclusion restriction suggests that latrine
construction should not aﬀect these irrelevant outcomes. As expected, I do not find eﬀects
on water temperature, pH, and malaria mortality in either IV or DID designs (Columns 1-4
and 11 of Appendix Table B4).34
5.3

Upstream-Downstream Specification in Instrumental Variable Design

The negative externalities on water and health may spill over to downstream districts becausedumped fecal sludge can flow downstream along rivers. Thus, I additionally examine theeﬀects of upstream latrine construction on downstream water quality and health in themodified IV specification.
This upstream-downstream specification addresses the concern of exclusion restriction in
the baseline specification of the IV design. In this specification, I use upstream AWC as an
instrument for upstream latrine construction and examine the impacts on downstream outcomes. The upstream AWC is not expected to aﬀect downstream health outcomes througha change in income because the upstream AWC is unlikely to be associated with the downstream agricultural output. Thus, using upstream AWC unrelated to downstream healthoutcomes as an instrument enhances the validity of the exclusion restriction.35
32

The lagged eﬀect on water quality, which become statistically significant starting from 2017, can be
explained by the fact that it typically takes between 1.5 to 3 years for latrines to fill up with fecal sludge
and subsequently be emptied, as per the requirement stated in the technical guideline (CPHEEO, 2013).
33
This analysis is based on the water quality data of the NWMP and the malaria mortality estimates of
IHME (2020a).
34
As an additional analysis, I adopt the BOD, DO, and Nitrate-Nitrite, which measure water contamination from various pollution sources, as outcomes. I do not find eﬀects on these variables in most cases(Columns 5-10 of Appendix Table B4). These results suggest that there are no other substantial sources ofwater contamination due to other programs implemented at the same time as the SBM.
35
To test the validity of the exclusion restriction, I run reduced-form regressions of outcomes on upstream
AWC as in Section 5.2. Appendix Figure A2 encouragingly shows no eﬀects of upstream AWC on both water
quality and health outcomes during the pre-SBM period.

18

I identify upstream-downstream relationships among monitoring stations and districts
using the elevation data along 43 major rivers.36 Thus, this specification focuses on a subset
of districts (stations) located along major rivers that have further upstream districts.37 As
shown in Appendix Figure A3, the upstream districts of a given district (station) are selected
as the districts that intersect with river segments whose elevations are higher than theelevation of the given district (station).
The definition of upstream districts, i.e., how far upstream I should search for districts,
matters because the pollution decays as it flows downstream. Because the decay rates dependon the temperature and other environmental factors of rivers, I adopt a variety of distancesfrom a given district (station) for identifying upstream districts. Specifically, for a given
district (station), the upstream districts are selected from districts that fall within a range of[X, Y ] kilometers from the given district (station), where X ∈ {0, 50, 100}, Y ∈ {100, 150},
and X < Y . I use a range of [0, 150] kilometers as the baseline specification, and I conductrobust checks which use either alternative buﬀer sizes or all upstream districts without
buﬀers.38
In this analysis, I use the following regressions 3 and 4 modified from the baseline specification. The independent variable is changed to the upstream number of latrines per square
kilometer, and the instrument is changed to the upstream AWC.39 I also control for AWC
in the reference district because the instrument (upstream AWC) can be spatially correlatedwith AWC in the reference district, which can also aﬀect outcomes. The coeﬃcient of interU
est, βIV, captures the eﬀect of upstream latrine construction, which is the composite of two
underlying eﬀects: (i) the direct eﬀect of upstream latrine construction on outcomes and(ii) the indirect eﬀect of upstream latrine construction on outcomes via latrine constructionin the reference district. To examine the latter channel, I also test the eﬀect of upstream
36

I focus on major rivers included in the Version 4.1.0 GIS polygons of rivers provided by the Natural
Earth. Upstream-downstream relationships along major rivers are less susceptible to measurement errors
because the river systems are simpler than those that include hundreds of rivers. I use 90-meter raster digital
elevation data, called the Shuttle Radar Topography Mission (SRTM) data Version 4.1 (Reuter et al., 2007).
37
The focus on major rivers results in a sample of 365 stations in 154 districts in the water quality
analysis. In the district-level health analysis, I further drop districts where more than one major rivers flow
due to the complexity of determining the upstream-downstream relationships, resulting in a sample of 103
districts.
38
The same procedure is repeated to identify downstream districts for a placebo test. These are selected
as the districts that intersect with river segments whose elevations are lower than the elevation of the given
district (station).
39
If there are multiple upstream districts, I compute the aggregated values of latrines and AWC for
upstream districts by taking the average of their values.

19latrine construction on the latrine construction in the reference district.40
UYi,d,t = α + βIV
U pstream Latrined,t + γ1 P recipd,t + γ2 AW Cd · P ostt + δi + θt + εi,tU pstream Latrined,t = π1 + π2 U pstream AW Cd · P ostt + π3 P recipd,t+ π4 AW Cd · P ostt + δi + θt + νi,t
5.4

(3)
(4)

Diﬀerence-in-Diﬀerences Design

I additionally adopt the DID design to examine the dynamic evolution of the eﬀects of latrine
construction on water quality.
In the DID design, I exploit the fact that all districts had achieved almost universal latrine
coverage by the target date of 2019, regardless of their baseline latrine coverage. Thus,
districts with lower baseline latrine coverage have experienced a larger increase in latrine
coverage. This fact allows me to adopt a DID design that uses baseline latrine coverage
as a continuous treatment.41 As shown in Figure 6, there are substantial diﬀerences in thebaseline latrine coverage across districts in 2013, which suggests a diﬀerential increase in
the number of latrines by 2019. This first-stage relationship is empirically shown in the
Appendix Figure A4. Then, I expect that districts with higher latrine non-coverage in 2013
experienced a higher increase in water pollution due to a larger increase in latrine coverage.
In this DID design, I adopt the following baseline regression.42
Yi,d,t = α + βDID (1 − Latrinepred ) · P ostt + γXd,t + δi + θb,t + εi,t(5)where Yi,d,t is a water quality indicator, represented by the logarithm of fecal coliform, atmonitoring station i inside district d in year t. Latrinepre
is a latrine coverage in district dd
in 2013, which was one year before the SBM started. P ostt is an indicator that takes thevalue one after 2014 when SBM started. Xd,t are a set of control variables, which are timevarying precipitation and time-invariant district characteristics, including VIIRS nighttime
luminosity in 2013, population, the proportions of Scheduled Caste and Scheduled Tribemembers, and literacy rates in 2011. Time-invariant variables are added as control variables
40

Another approach would be to regress outcomes on upstream latrine construction and the latrine
construction in a reference district and instrument them with AWC in each area. I do not adopt thisapproach due to the issue of a weak instrument in the first stage.
41
This DID design that uses variation in baseline degree of policy implementation is in the same vein as
Duflo (2001) and Bleakley (2007).
42
Recent literature shows that the DID design with a continuous treatment is subject to biased estimates
unless alternative (typically stronger) assumptions, other than the parallel trends assumption, are satisfied(Callaway et al., 2021). Therefore, I conduct a robustness check by adopting an alternative design with abinary treatment that compares districts whose baseline latrine coverage is lower than the national medianwith districts whose baseline coverage is higher than the national median.

20

after being interacted with year dummies. Lastly, monitoring station fixed eﬀects, δi , andbasin-year fixed eﬀects, θb,t , are included. Standard errors are clustered at the district level
since the baseline latrine coverage varies across districts. The coeﬃcient of interest is βDID ,and I expect it to be positive.
To examine pre-trends and the dynamic evolution of the treatment eﬀects, I also adopt
the following event-study specification.
Yi,d,t = α +2019󰁛l=2007βl (1 − Latrinepred ) · Tl + γXi,t + δi + θb,t + εi,t(6)where the baseline year is 2013, and Tl is a year dummy variable. Standard errors are
similarly clustered at the district level. The coeﬃcients of interest are the βl ’s, that measure
the treatment eﬀects on water quality in each year relative to 2013. The βl ’s of 2007-2012 areexamined to test the assumption of parallel pre-trends, while the βl ’s of 2014-2019 capture
the dynamic evolution of the treatment eﬀects. Based on the test of parallel pre-trends, I
use this DID design only for the water quality outcome.

6

Results

6.1

Eﬀects on Water Quality

I find that latrine construction under the SBM degrades river water quality in both IV and
DID designs.
In the IV design, Table 2 shows that one additional latrine per square kilometer increases
fecal coliform by 3% on average (Column 3). This estimate of the eﬀect on water quality
in the IV design is substantially larger than in the OLS regression, which is about 0.6%
(Column 1). This diﬀerence comes from the downward bias from the endogeneity in the
OLS regression, possibly due to measurement errors in the number of latrines and omitted
variables, including persistent belief in open defecation that increases water pollution but
slows down latrine construction.
The result of the first stage shows the positive association between AWC and the numberof latrines as expected (Column 2 of Table 2). Although the F-statistics of the first stage
are not low (29.954), I also compute the 95% confidence interval of the Anderson and Rubin(1949) test that is robust to the weak instrument. Reassuringly, the positive left and rightends of the 95% confidence interval ([0.15, 0.49]) show that the result is robust to thisAnderson and Rubin (1949) specification.43
43

The Anderson and Rubin (1949) confidence intervals are shown for all following IV results.

21

A total eﬀect of the SBM (hereinafter called “average policy eﬀect”) is a 72% increase infecal coliform, which shows a substantial negative externality on the water quality (Column 3
of Table 2). The average policy eﬀect is calculated by multiplying the estimated coeﬃcient by
the diﬀerence between the mean number of latrines per square kilometer during the pre-SBM(2012-2013) period and during the post-SBM period (2014-2019).44 The 72% increase in fecal
coliform due to latrine construction exceeds the estimates of most past studies conductedin diﬀerent settings, due to the substantial more than doubling of latrine coverage during
the SBM period. It is significantly larger than the eﬀect of each additional border crossing
induced by a border change on water pollution level (3% increase) in Brazil (Lipscomb andMobarak, 2016) and the eﬀect of each additional Clean Water Act’s grant to municipal
wastewater treatment plants on fecal coliform (3.6 % decrease) in the United States (Keiser
and Shapiro, 2019).
I find a similar result in the DID specification. The positive coeﬃcient in Column 4of Table 2 suggests that latrine construction increases water pollution, although the eﬀect
becomes imprecise. In this DID design, the water pollution eﬀects become larger and more
precise when I focus on areas with lower STP treatment capacities in the heterogeneity
analysis in Section 7.1. On the other hand, I find a null eﬀect in areas with higher treatment
capacities, which renders the overall eﬀect in Table 2 imprecise.
These results are consistent with my theoretical prediction that the SBM increases waterpollution due to the dumping of fecal sludge from increased latrines.
6.2

Eﬀects on HealthI find that latrine construction under the SBM improves health overall in the IV design,
which suggests that the direct positive health eﬀect outweighs the negative externality on
health due to increased water pollution.
Table 3 reports the positive net health eﬀects of latrine construction. One additional
latrine per square kilometer reduces the diarrheal post-neonatal mortality per 1,000 people
by about 0.029 on average in the IV design, which is a 1.3% reduction from the pre-SBM
period.45 The average policy eﬀect of the SBM is calculated to be a 0.827 reduction indiarrheal post-neonatal mortality per 1,000 people, which amounts to a 36% reduction fromthe pre-SBM period (Column 3). This 36% reduction in diarrheal mortality under the SBM
44This average policy eﬀect is an upper-bound estimate because some constructed latrines may not be
attributed to the SBM subsidy. Moreover, when I calculate average policy eﬀects for other results, I use thediﬀerence in the mean number of latrines per square kilometer in each sample.
45
The magnitude of the eﬀect on health in the IV design is substantially larger than in the OLS regression.
This diﬀerence comes from the endogeneity in the OLS regression, possibly due to measurement errors in
the number of latrines and reverse causality when an increase in diarrheal mortality encourages the latrine
construction to address this health issue.

22

is smaller than the eﬀect of a 60 percentage point reduction in the fraction of neighbors
defecating in the open (a similar magnitude to the SBM policy) on infant mortality rate
(48% reduction as estimated in Geruso and Spears (2018)). This discrepancy may be due tothe incorporation of water pollution externalities in the district-level analysis of this paper.
Lastly, this positive health eﬀect result is robust to the adoption of diarrheal mortality inother age groups (Appendix Table B3).
These results capture the overall health eﬀect, which is the diﬀerence between the direct
positive health eﬀect and the negative externality on health due to water pollution. Thus,
the overall positive health eﬀect in my analysis suggests that the direct positive health eﬀect
outweighs the negative externality on health, as shown in the conceptual framework.
6.3

Eﬀects of Upstream Latrine Construction on Downstream Outcomes

I find that upstream latrine construction degrades downstream water quality while improving
downstream health overall in Table 4 in the IV design.46
First, I find that upstream latrine construction increases fecal contamination of rivers
in the reference district only when upstream areas have lower treatment capacities (examined more in detail in Section 7.1), although the eﬀect becomes imprecise when I use allobservations (Columns 1 and 3-6 of Panel A). This heterogeneity in the eﬀects shows waterpollution externalities of latrine construction that extend to downstream districts. On theother hand, I find a null eﬀect of downstream latrine construction on water quality in thereference district, as there should be no water pollution externalities from downstream to
upstream districts (Column 2 of Panel A).
Second, I find that upstream latrine construction has reduced diarrheal mortality in thereference district overall (Column 1 of Panel B). Although upstream latrine construction
causes water pollution externalities to the reference district, it improves health outcomesin the reference district overall due to separate benefits arising from increased latrine construction in the reference district (Column 1 of Panel C). The positive net health eﬀect thatcomes from increased latrine construction outweighs the water pollution externalities. The
positive spillover eﬀect of latrine construction from upstream to reference districts can be
explained by the fact that these districts are usually located in the same state, given that the
buﬀer sizes for identifying upstream districts are less than or equal to 150 kilometers. Because states play a central role in implementing sanitation policies in India, districts within
the same state are likely to undertake a similar level of latrine construction.47 On the other46

Table 4 reports results when upstream and downstream districts are defined as those within the rangeof [0, 150] kilometers from a reference station (district). The results are robust to alternative buﬀer sizes asshown in Appendix Table B2.
47
To directly test this claim, I estimate the intra-cluster correlation coeﬃcient, which measures the23hand, I find a null eﬀect of downstream latrine construction on health in the reference district(Column 2 of Panel B).
6.4

Robustness Checks

The results are robust to the consideration of spillovers from neighboring districts, influence
from urban areas, and a balanced panel.48
Spillovers from Neighboring Districts.—The analysis of the baseline specification assumes
that water quality in a given monitoring station is aﬀected only by the latrine constructionin the district where that monitoring station is located. However, monitoring stations can besituated on rivers that flow along the border of several districts. In this case, water quality
in those stations is likely to be aﬀected by several neighboring districts.
I conduct an additional analysis that incorporates spillover eﬀects from neighboring districts. For the monitoring stations that are located within 2 kilometers of more than one
district, I compute the weighted average of variables of neighboring districts by using district
areas as weights. The data of other monitoring stations remain unchanged. Then, I run the
regressions 1, 2 of the IV design and the regression 5 of the DID design.
As shown in Column 1 of Appendix Table B5, I find results that are similar to the baseline
specification. I find a negative eﬀect on water quality, and the estimated coeﬃcients are very
similar to those in the baseline specification.
Influence from Urban Areas.—While my focus is on the eﬀects of latrine construction inrural India, it is possible that the baseline specification results are partly driven by latrine
construction in urban areas. Therefore, I conduct a robustness check that estimates theeﬀects of the SBM after excluding monitoring stations and districts that are close to urban
areas from the sample. Specifically, I drop monitoring stations and districts that are within
50/100/150 kilometers of cities with a population of 1 million and above, according to the2011 Census.
As shown in Appendix Table B6, the results are robust to excluding urban areas regardlessof distance in both IV and DID designs. As in the baseline specification, I find a negativeeﬀect on water quality and an overall positive health eﬀect in the IV design.
proportion of the overall variance that is explained by within-state variance in the number of latrines per
square kilometer between 2013 and 2019. The coeﬃcient is estimated to be 0.704. This high coeﬃcient
suggests that districts within the same state behave similarly in terms of latrine construction.
48
I mainly test the robustness of the baseline specification that serves as the basis for the heterogeneity
analysis in Section 7, although I include results of heterogeneous eﬀects for the robustness checks on thespillovers from neighboring districts and a balanced panel.

24Balanced Panel.—The baseline specification uses unbalanced panel data on water quality,
so I conduct a robustness check using a balanced panel. Using the balanced panel mitigatesthe concern that monitoring stations may have been endogenously installed in less polluted
locations over the sample periods. As shown in Column 1 of Appendix Table B7, I similarlyfind a negative eﬀect on water quality in both IV and DID designs.

7

Heterogenous Eﬀects of Latrine Construction

To explore the mechanism behind the negative externalities, I test the heterogeneous eﬀects
of latrine construction. Specifically, I show that the main mechanism is the water pollution
from poor treatment (dumping) of fecal sludge because the negative externalities are larger
in areas with lower capacities for treatment of fecal sludge and in areas with higher exposure
to river pollution.
7.1

Heterogenous Eﬀects by Treatment Capacity of Fecal Sludge

As in the theoretical hypotheses regarding the diﬀerential eﬀects of latrine construction
under the SBM, I show that negative externalities on water quality and health vary by thelevel of complementary treatment of fecal sludge. The negative externalities are found to belarger in areas with lower treatment capacities, where dumping of fecal sludge is more likely
to happen. This result suggests that poor treatment (dumping) of fecal sludge is the main
mechanism of the negative externalities.
In this analysis, I use geographical variation in the treatment capacities of STPs. Based on
the inventory of STPs compiled by the CPCB (CPCB, 2015), I calculate the STP capacities
at both state and district levels in 2013, one year before the SBM started.49 In the baseline
specification, I compare eﬀects in states/districts that have higher treatment capacities thanthe median in the sample with those in states/districts with lower treatment capacities.50
In the upstream-downstream specification, I similarly examine the heterogeneous eﬀects by
the diﬀerent levels of treatment capacities in upstream states/districts. This heterogeneity
analysis uses the baseline level of STP capacities to address the concern of endogenous
construction of STPs in response to water pollution caused by latrine construction under
the SBM.51
49

The district-level STP capacities are highly susceptible to measurement errors due to missing observations of STPs in the CPCB inventory. Some districts may be flagged as districts with zero treatment
capacity due to missing observations, even if they actually have STPs. Therefore, I also use state-level STP
capacities, which are less susceptible to measurement errors due to further aggregation.
50
The median value is calculated after setting zero capacity for the states/districts with no STP.
51
Consistent with the fact that it can take 5-10 years to plan and construct STPs, there was not a
substantial increase in STP capacity during the SBM period. The total STP capacity increased by 52%

25

Eﬀects on Water Quality.—I find that the negative externality on water quality is substantial in areas with lower treatment capacities in the IV design. As shown in Panel A of
Table 5, one additional latrine per square kilometer increases fecal coliform by 3.7% in stateswith lower treatment capacities (Column 3). On the other hand, I find no eﬀect in stateswith higher treatment capacities (Column 2). Similarly, I find that one additional latrine persquare kilometer increases fecal coliform by 5.1% in districts with lower treatment capacities(Column 5), although the eﬀect becomes insignificant in districts with higher treatment capacities (Column 4). In the upstream-downstream specification, I similarly find a negative
externality of upstream latrine construction on downstream water quality when upstream
states/districts have lower treatment capacities (Columns 4 and 6 of Panel A of Table 4).
I find similar results in the DID design in Panel B of Table 5. The coeﬃcients of (1 −Latrinepred )·P ostt show that a district with baseline latrine coverage of 50% would experience
an increase in fecal coliform of about 75-90%, relative to a district with 100% baseline latrine
coverage, in areas with lower treatment capacities (Columns 3 and 5). Considering the factthat the baseline latrine coverage was 39.2% in 2013, the average eﬀects of the SBM in stateswith lower treatment capacities can be calculated as (1 − 0.392) × 1.790 = 1.088 in the DID
design, which is relatively close to the average policy eﬀect (0.976) in the IV design. On theother hand, consistent with the results of the IV design, I do not find negative externality
in the areas with higher treatment capacities (Columns 2 and 4).
The DID event study design shows the negative externality on water quality in areas
with lower treatment capacities has become substantial two years after the start of SBM,
and this eﬀect has become larger over time. The estimated coeﬃcients from the event-study
specification in equation 6 are reported in Figure 8. First, Figure 8 reassuringly shows nodiﬀerential pre-trends for all panels, which enhances the validity of the parallel pre-trends
assumption. Second, Figure 8 highlights that the negative externality in states with lower
treatment capacities has become substantial since 2016, two years after the start of the SBM(Panel B).52 This lagged eﬀect is consistent with the fact that a diﬀerential increase in thenumber of latrines among districts with diﬀerent levels of baseline coverage starts around2016 in Appendix Figure A4.53 This negative externality on water quality become larger
from 2013 to 2021, even though latrine coverage more than doubled in the same timeframe. Moreover, areas
with lower baseline STP capacities did not experience a more substantial increase in STP construction, as
shown in the positive correlation between the baseline level of STP capacity in 2013 and the change in STP
capacity from 2013 to 2021 at the state level (CPCB, 2015, 2021).
52
Appendix Figure A5 shows event study plots that compare districts with higher and lower treatment
capacities. Because I find diﬀerential pre-trends in the case of districts with lower treatment capacities(Panel B), I focus on the results based on state-level variation in treatment capacities.
53
These results are robust to an alternative specification of a binary treatment indicator that takes thevalue one when district’s baseline latrine coverage is lower than the national median (Appendix Table B1
and Appendix Figure A6).

26

over time from 2016 to 2019, which is consistent with the fact it typically takes between 1.5
to 3 years for latrines to fill up with fecal sludge and subsequently be emptied (CPHEEO,
2013).
These diﬀerential eﬀects of the SBM by the treatment capacity of fecal sludge suggestthat the dumping of fecal sludge emptied from latrines is the main mechanism accounting
for increased river pollution. These findings are consistent with the theoretical predictionthat the magnitude of water pollution is larger in the case of lower treatment capacity due
to larger marginal damage. Conversely, the null eﬀect on water quality in areas with higher
treatment capacities suggests it is unlikely the eﬀect is caused by other channels (e.g., theseepage of fecal matter from latrines to rivers).
Eﬀects on Health.—The analysis of heterogeneous eﬀects allows me to explicitly investigate the negative externality on health. The negative externality can be captured as adiﬀerence between the health eﬀect of areas with lower treatment capacities (significant riverpollution) and that of areas with higher treatment capacities (insignificant river pollution).
Panel C of Table 5 shows the results of heterogeneous eﬀects on diarrheal post-neonatal
mortality by treatment capacities at both state and district levels in the IV design. I findthat the magnitude of the positive health eﬀect is 0.064 (3% decrease) in states with higher
treatment capacities (Column 2), while the magnitude reduces to 0.020 (1% decrease) instates with lower treatment capacities (Column 3). The average policy eﬀect of the SBM is a
0.625 decrease (26% decrease) in diarrheal mortality in states with lower treatment capacities,
which is about two-thirds smaller than the eﬀect of a 1.507 decrease (71% decrease) in stateswith higher treatment capacities (Columns 2-3). This diﬀerence in the magnitude of positive
health eﬀects is statistically significant (p-value = 0.003). The diﬀerence in the coeﬃcients
becomes imprecise when I compare the eﬀects by district-level treatment capacities (Columns
4-5).
In the upstream-downstream specification of the IV design, I find that the upstream
latrine construction has smaller positive health eﬀects of decreasing diarrheal mortality inthe reference district when the upstream states/districts have lower treatment capacities(Columns 4 and 6 of Panel B of Table 4). In these cases of lower treatment capacities, thepositive health eﬀect of upstream latrine construction becomes null, which suggests that thepositive net health eﬀect that comes from increased latrine construction in the reference
district is completely oﬀset by the water pollution externalities.
These results, together with those on water quality eﬀects, suggest that the increased
water pollution due to the SBM, which is substantial in areas with lower treatment capacities,
has negative health consequences. Although the overall health eﬀect is positive, the water27

pollution externalities reduce the magnitude of this positive health eﬀect, as predicted in thetheoretical prediction.
7.2

Heterogenous Eﬀects on Health by Exposure to River Pollution

I show that the health eﬀects of latrine construction vary by the intensity of exposure to riverpollution. The positive health eﬀects are found to be smaller for those with greater exposure
to river pollution. This result suggests that the latrine construction under the SBM has anegative externality on health through the water pollution channel.
In this heterogeneity analysis, I use proximity to rivers as a proxy for exposure to riverpollution. Proximity to rivers increases exposure to river pollution because people living
close to rivers are more likely to use river water for bathing, consumption, household use,etc. Thus, I examine how health eﬀects vary by the proximity to rivers.
To measure the proximity to rivers, I construct an indicator of how many people live
near rivers based on the VIIRS nighttime light data in 2013. First, I calculate the totalnighttime luminosity of each district. Second, I calculate the total nighttime luminosity ofareas within 5/10/15 kilometers of rivers for each district.54 Third, I calculate the districtlevel ratio of nighttime luminosity within specified distances from rivers by dividing the valueof the second step by the value of the first step.55
Based on this constructed indicator, I compare health eﬀects in districts with higher
exposure to river pollution with those in districts with lower exposure in the IV design. A
given district is defined as highly exposed to river pollution if its ratio of the total nighttime
luminosity of the area close to rivers is higher than the sample median.
I find that positive health eﬀects are smaller in districts with a higher proportion ofnighttime luminosity near rivers, i.e., higher exposure to river pollution. As shown in Table
6, the positive health eﬀects in higher exposure districts (Columns 1, 3, and 5) are about halfof those in lower exposure districts (Columns 2, 4, and 6), regardless of diﬀerent distancesfrom rivers. These diﬀerential eﬀects suggest that the negative externality on health is caused
by increased water pollution due to latrine construction.
54
I use rivers ≥30-meter wide at mean annual discharge, available in the Global River Widths fromLandsat (GRWL) Database (Allen and Pavelsky, 2018). This dataset covers more rivers, including smaller
ones, than the dataset of major rivers used in the upstream-downstream analysis.
55
One limitation of using nighttime light data as a proxy of population density near rivers is that thisdata cannot distinguish urban from rural populations. If the population density near rivers is diﬀerent for
urban and rural areas within a district, this paper does not reflect that diﬀerence.

28

8Conclusion

My analysis documents an unintended negative consequence of latrine construction in India.
Although open defecation has been commonly blamed for causing negative externalities, I
show that latrine construction has larger water pollution externalities than open defecation
due to poor treatment of fecal sludge. I then show that investments in latrines are less
eﬀective at improving child health in areas where the water pollution eﬀects are larger.
Specifically, I examine the consequences of India’s nationwide sanitation policy, the SBM.
I exploit two features of the SBM to identify its causal eﬀects on water quality and health.
First, the fact that soil infiltration rates determine the cost and diﬃculty of latrine construction according to the oﬃcial technical guidelines during the post-SBM period renders soil
infiltration rates suitable as an instrument for latrine construction. Second, the fact that all
districts had achieved almost universal latrine coverage by the target date, regardless of theirbaseline latrine coverage, allows me to use the baseline coverage as a continuous treatment.
Based on these two identification strategies, I find that the SBM increases river pollution
by 72%, which is a substantial eﬀect. The negative externality on water quality exists only
in areas with lower treatment capacities, where the dumping of fecal sludge is more likely
to happen. Moreover, I show that the SBM reduces diarrheal mortality by 36% overall.
However, this positive health eﬀect is two-thirds smaller in states with lower treatment
capacities (26% reduction) than in states with higher treatment capacities (71% reduction).
The back-of-the-envelope calculations show that the mortality benefits are worth thecost of the SBM policy. The mortality benefits, i.e., reduction in diarrheal post-neonatal
mortality, are calculated to be 16.9 million USD,56 almost equivalent to the subsidy cost for
latrine construction (17.4 million USD) at the district level.57 The benefits would be larger
than this estimate if I took into account the health eﬀects for other age groups and otherbenefits such as improved educational outcomes. Although this result suggests that the SBM
is successful overall, suﬃcient treatment of fecal sludge would increase the mortality benefits
even more with a low additional cost. The additional benefits of higher treatment capacity
are similarly calculated to be 18.1 million USD, which is larger than the additional cost of
constructing and operating more sewage treatment plants (11.5 million USD).58
56

The mortality benefits are estimated by multiplying the total number of reduced mortalities under the
SBM (30.3) by the estimate of the value of a statistical life in India (0.56 million USD according to Majumderand Madheswaran (2018)). The total number of reduced mortalities is calculated based on the estimatedaverage policy eﬀect (0.827 per 1,000 people) and the estimate of the district-level mean population of age
0-1 (0.36 million people). This population estimate is calculated from the district-level mean population(1.57 million people) and the percentage of the population of age 0-4 (9.32%) in the 2011 Census.
57
The cost is calculated by multiplying the amount of the SBM subsidy (150 USD) by the mean change
in the number of latrines (0.11 million).
58
Additional benefits are calculated based on the diﬀerence in the estimated average policy eﬀects between

29

My results present several policy implications for developing countries promoting sanitation and other similar policies. The first clear implication is that policymakers should
consider the possibility of negative externalities of sanitation investment on water quality
and health. An enabling environment that includes eﬀective treatment of fecal sludge canmake sanitation policies more eﬀective, which should be considered in the second phaseof the SBM that started in 2020. The need for better fecal sludge management is also acommon issue in South Asian countries, including Bangladesh, Nepal, and Pakistan, where
sewage and fecal sludge have been inadequately treated regardless of making good progressin improving access to toilets (WaterAid, 2019). Future studies may investigate the causal
eﬀects of the treatment of fecal sludge on water quality and health because my approachherein is to examine heterogeneous eﬀects by the baseline variation in the treatment capacityof fecal sludge.
Second, my findings on the negative externalities have implications for other policies,
such as waste management. Waste management policy similarly requires consideration of
the various stages involved, ranging from the collection of waste to safe recycling and disposalof waste. Focusing only on the collection of waste may cause negative externalities on theenvironment due to untreated waste. Investigating the existence of negative externalities
associated with other similar policies may be a fruitful area for future research.

higher and lower treatment capacities at the state level (1.507-0.625=0.882 per 1,000 people). Additionalcosts are calculated based on the capital and O&M cost for 15 years (0.23 million USD/million liter per day)of the most common technology, Upflow Anaerobic Sludge Blanket (estimated based on CPCB (2013)), andthe district-level diﬀerence in STP capacity between states with higher and lower treatment capacities.

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Childhood Cognitive Skills: Evidence from India’s Total Sanitation Campaign.” Journalof Human Resources, 51(2): 298–327.
Spears, Dean, and Amit Thorat. 2019. “The puzzle of open defecation in rural India:
evidence from a novel measure of caste attitudes in a nationally representative survey.”
Economic Development and Cultural Change, 67(4): 725–755.
Wang, Dongqin, and Yanni Shen. 2022. “Sanitation and work time: Evidence from thetoilet revolution in rural China.” World Development, 158, p. 105992.
WaterAid. 2019. “Faecal sludge management landscape in South Asia.” Urban Sanitation
Working Group, South Asia Region, WaterAid.
Yishay, Ariel Ben, Andrew Fraker, Raymond Guiteras, Giordano Palloni,
Neil Buddy Shah, Stuart Shirrell, and Paul Wang. 2017. “Microcredit and willingness to pay for environmental quality: Evidence from a randomized-controlled trial35

of finance for sanitation in rural Cambodia.” Journal of Environmental Economics andManagement, 86 121–140.

36

100

98.9

100

Latrine Coverage (%)
60
80

84.6

65.2

51

40

42.9
38

2012

39.2

2013

2014

2015

2016

2017

2018

2019

Year

Figure 1: Latrine Coverage in Rural India
Notes: This figure documents the proportion of households that have latrines in rural
India between 2012 and 2019. A vertical dashed line shows the starting year of the
Swachh Bharat Mission.

37

Panel A. Treatment Capacity Low

Panel B. Treatment Capacity High

Figure 2: Welfare Eﬀects of the Swachh Bharat Mission
Notes: This figure examines how the subsidy under the SBM changes the deadweight loss (DWL) in twocases: (A) low treatment capacity (low Qstp ) and (B) high treatment capacity (high Qstp ). The subsidy
shifts down the marginal cost (MC) from M C pre to M C post . Marginal damage (MD) represents the negative
externality on health, which occurs when the number of latrines is larger than the treatment capacity level(Qstp ). Marginal benefit (MB) represents direct health benefits because of reduced open defecation. This
figure shows that DWL increases more significantly in the case of low treatment capacity in Panel A.

38

Panel A. Treatment Capacity Low

Panel B. Treatment Capacity High

Figure 3: Eﬀects of the Swachh Bharat Mission on Water Quality and HealthNotes: This figure examines how the SBM aﬀects water quality and health in two cases: (A) low treatment
capacity (low Qstp ) and (B) high treatment capacity (high Qstp ). Total benefit and total damage in thisfigure are based on the marginal benefit and marginal damage plotted in Figure 2. Eﬀects on health andwater quality are represented by the changes in net benefit and total damage, respectively. This figure showsthat SBM improves health overall and increases water pollution. In the case of low treatment capacity inPanel A, the magnitude of health eﬀects is smaller, and the magnitude of eﬀects on water quality is larger.

39

Figure 4: Distribution of Water Quality Monitoring Stations in India
Notes: This figure shows water quality monitoring stations in orange dots, district boundaries inblack lines, and rivers in blue lines. The data source of river lines is Allen and Pavelsky (2018).

40

Figure 5: Available Water Capacity (mm/m) across Districts

Figure 6: Latrine Coverage (%) in 2013 across Districts
Notes: Districts with no data on latrine coverage are displayed to be blank in Figure 6. These
districts correspond to urban areas where latrine data are not recorded under the SBM.

41



/RJ )HFDO&ROLIRUP$YHUDJH






$:DWHU4XDOLW\%DVOLQH6SHFLILFDWLRQ'LDUUKHDO3RVWQHRQHWDO0RUWDOLW\ SHU






            

%+HDOWK%DVHOLQH6SHFLILFDWLRQ            

Figure 7: Event Study Plots of Reduced-Form Regressions of Available Water Capacity
Notes: This figure shows the regression coeﬃcients of the logarithm of fecal coliform (Panel A)
and diarrheal post-neonatal mortality per 1,000 people (Panel B) on the interaction terms between
Available Water Capacity and year dummies. The 95% confidence intervals are shown with dashedlines. Standard errors are clustered at the district level. Panel A includes monitoring station fixedeﬀects, year fixed eﬀects, and precipitation as a control, while Panel B includes district fixed eﬀects,
year fixed eﬀects, and precipitation as a control.

42

5
4
Log(Fecal Coliform - Average)-3 -2 -1 0 1 2 3-4

A. All2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

5

4

Log(Fecal Coliform - Average)-3 -2 -1 0 1 2 3-4-5

Notes: This figure shows the regression coeﬃcients of the logarithm of fecal coliform in equation 6. The 95% confidence
intervals are shown with dashed lines. Standard errors are clustered at the district level. All regressions include
monitoring station fixed eﬀects, basin-year fixed eﬀects, and the following controls: precipitation, VIIRS nighttime
luminosity, population, the proportions of Scheduled Caste and Scheduled Tribe members, and literacy rates. Panel B
shows a result in states where treatment capacities of sewage treatment plants are lower than the median, while PanelC shows a result in states with higher treatment capacities.

Figure 8: The Dynamic Eﬀects on Water Pollution (Log of Fecal Coliform)

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

C. State-level Treatment Capacity High2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

B. State-level Treatment Capacity Low-554
Log(Fecal Coliform - Average)-3 -2 -1 0 1 2 3
-4
-5

43

Table 1: Summary Statistics
Mean

SD

Min

Max

Observations

2606.42
20.2
9.22
2.69
0.48
1.07
0.02
12.93
35.55
43
1341.98

143774.77
13.65
6.19
1.8
0.34
0.72
0.04
13.39
41.92
25.48
778.05

0
0.5
0.23
0.07
0.01
0.03
0
0.01
0.03
0.08
214.05

10000035
71.86
32.41
9.48
1.83
3.87
0.38
89.7
283.01100
5589.17

4939
2359
2359
2359
2359
2359
2359
586
586
586
1946

722.17
9.79
4.64
1.46
0.2
0.51
0.01
22.52
59.06
76.78
1312.2330385.66
7.28
3.44
1.07
0.15
0.38
0.04
18.96
57.05
27.43
878.46

0
0.29
0.14
0.05
0.01
0.02
0
0.01
1.12
3.58
196.091750013
35.57
16.76
5.21
0.73
1.86
0.33
146.87430.09100
10061.3

5553
2022
2022
2022
2022
2022
2022
1814
1814
1814
1814

128.0328.17
709.5
1572.08
16.75
16.98
61.16
0.71
0.38

25.91
105.03782.06
1077
9.69
25.24
10.44
1.57
0.23

19.79
0
0
28.99
0
0
28.66
0.01
0

150
947.5
2307.75
6074.19
53.39
98.10
88.7
17.98
1

337
337
337
337
337
337
337
337
317

Panel A. Time-varying variables: pre-SBM (2007-2013)
Fecal coliform - average (thousand MPN/100ml)
Diarrheal early-neonatal mortality (per 1000 people)
Diarrheal late-neonatal mortality (per 1000 people)
Diarrheal post-neonatal mortality (per 1000 people)
Diarrheal age 1-4 mortality (per 1000 people)
Diarrheal under 5 mortality (per 1000 people)
Malaria age 0-4 mortality (%)
Number of latrines (ten thousand)Number of latrines per sq. km
Latrine coverage (%)
Precipitation (mm)Panel B. Time-varying variables: post-SBM (2014-2019)
Fecal coliform - average (thousand MPN/100ml)
Diarrheal early-neonatal mortality (per 1000 people)
Diarrheal late-neonatal mortality (per 1000 people)
Diarrheal post-neonatal mortality (per 1000 people)
Diarrheal age 1-4 mortality (per 1000 people)
Diarrheal under 5 mortality (per 1000 people)
Malaria age 0-4 mortality (%)
Number of latrines (ten thousand)Number of latrines per sq. km
Latrine coverage (%)
Precipitation (mm)Panel C. Variables not varying over time
Available water storage capacity (mm/m)
District-level capacity of sewage treatment plants (MLD) - 2013
State-level capacity of sewage treatment plants (MLD) - 2013
Population (thousand) - 2011
% Scheduled caste population - 2011
% Scheduled tribe population - 2011
% Literate population - 2011
VIIRS nighttime luminosity (nW/cm2/sr) - 2013
Ratio of total nighttime luminosity within 5 km of rivers - 2013

Notes: This table shows summary statistics of time-varying variables for pre-SBM periods (2007-2013) in Panel A andpost-SBM periods (2014-2019) in Panel B, and summary statistics of time-invariant variables in Panel C. The latrine data
are available only from 2012-2019, while data of other time-varying variables are available from 2007-2019. MPN and MLD
denote “most probable number” and “million liters per day,” respectively.

44

Table 2: The Eﬀect on Water Quality (Log of Fecal Coliform (FCf))

Number of latrines
per sq. km

OLS

IV - First StageIV - Second Stage

DID

(1)
Log(FCf)(2)# of Latrines per sq. km(3)
Log(FCf)(4)
Log(FCf)

0.006∗∗∗(0.002)0.030∗∗∗(0.008)0.283∗∗∗(0.052)AWC * Post (=1)
(1 - 2013 Latrine
Coverage) * Post (= 1)
Observations
R2
Number of Stations
Number of Districts
KP F-Stat
AR 95% CI
Average Policy Eﬀect

0.647(0.527)
7,201
0.020
1,189
337
0.142

7,201
0.091
1,189
337
29.954
-

7,201
1,189
337
[.015, .049]
0.719

10,385
0.860
1,187
335
-

Notes: The coeﬃcients are reported. Standard errors, clustered at the district level, are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. Regressionsof Columns 1-3 include monitoring station fixed eﬀects, year fixed eﬀects, and precipitation as acontrol. A regression of Column 4 includes monitoring station fixed eﬀects, basin-year fixed eﬀects,
and the following controls: precipitation, VIIRS nighttime luminosity, population, the proportionsof Scheduled Caste and Scheduled Tribe members, and literacy rates. The KP F-Stat refers to theWald version of the Kleibergen and Paap (2006) rk-statistic on the excluded instrumental variables for non-i.i.d. errors. The AR 95% CI reports the 95% confidence interval, which is robust
to the weak instrument based on the Anderson and Rubin (1949) test. Average policy eﬀects arecalculated by multiplying the estimated coeﬃcients by the change in the number of latrines per
square kilometer after the SBM started in 2014.

45

Table 3: The Eﬀect on Health (Diarrheal Post-neonatal Mortality (per 1,000 people))

Number of latrines
per sq. km

OLS

IV - First StageIV - Second Stage(1)
Diarrheal Mortality(2)# of Latrines per sq. km(3)
Diarrheal Mortality-0.007∗∗∗(0.001)-0.029∗∗∗(0.005)0.244∗∗∗(0.039)AWC * Post (=1)
Observations
R2
Number of Districts
KP F-Stat
AR 95% CI
Mean of Dep. Variable
Average Policy Eﬀect

2,696
0.657
337
.
2.282
-0.188

2,696
0.131
337
39.24833.374-2,696
337[-.042, -.021]
2.282-0.827

Notes: The coeﬃcients are reported. Standard errors, clustered at the district level, are inparentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. All
regressions include district fixed eﬀects, year fixed eﬀects, and precipitation as a control. The
sample is limited to districts that have monitoring stations used in the specification of water
quality. The KP F-Stat refers to the Wald version of the Kleibergen and Paap (2006) rk-statisticon the excluded instrumental variables for non-i.i.d. errors. The AR 95% CI reports the 95%
confidence interval, which is robust to the weak instrument based on the Anderson and Rubin(1949) test. Average policy eﬀects are calculated by multiplying the estimated coeﬃcients by thechange in the number of latrines per square kilometer after the SBM started in 2014.

46

Table 4: Upstream-Downstream Analysis
All
(1)
All

State-level Capacity(2)
All

District-level Capacity

(3)
High

(4)
Low

(5)
High

(6)
Low

-0.046(0.031)0.031∗∗∗(0.011)-0.004(0.011)0.037∗(0.023)

1,109
171
73
19.519[-.111, .037]

1,117
194
84
41.162[.010, .063]

1,097
180
75
53.262[-.033, .018]

1,131
185
93
15.137[-.014, .121]

Panel A. Dependent Variable: Log(Fecal Coliform)
Upstream number of
latrines per sq. km

0.015(0.011)

Downstream numberof latrines per sq. km
Observations
Number of Stations
Number of Districts
KP F-Stat
AR 95% CI

0.050(0.042)
2,228
365
154
50.475[-.008, .039]

2,215
365
147
2.391
[..., ...]

Panel B. Dependent Variable: Diarrheal Post-neonatal Mortality (per 1,000 people)
Upstream number of
latrines per sq. km-0.011∗(0.006)

Downstream number of
latrines per sq. km-0.041∗∗∗(0.010)-0.014∗∗(0.007)-0.000(0.010)-0.025(0.016)Equality Test (Upstream)
Observations
Number of Districts
KP F-Stat
AR 95% CI
Mean of Dep. Variable-0.010(0.006)p = 0.015
824
103
78.696[-.023, .001]
2.576

760
95
2.375
[..., .023]
2.621

432
54
33.304[-.073, -.024]
2.534

392
49
33.484[-.026, .002]
2.623

p = 0.280
456
57
59.873[-.030, -.000]
2.428

368
46
18.756[-.029, .026]
2.759

Panel C. Dependent Variable: Number of Latrines per sq. km in Reference District (district-level analysis)
Upstream number of
latrines per sq. km
Downstream number of
latrines per sq. km

0.726∗∗∗(0.154)

1.327∗∗∗(0.214)0.684∗∗∗(0.160)0.649∗∗∗(0.170)0.907∗∗(0.358)1.637∗∗(0.740)Notes: The coeﬃcients are reported. Standard errors, clustered at the district level, are in parentheses. ***, **, and *indicate significance at the 1%, 5%, and 10% levels, respectively. The sample is limited to monitoring stations (PanelA) and districts (Panels B and C) located along major rivers in India. Observations, number of districts, and KP
F-Stat of Panel C are the same as Panel B. Upstream and downstream districts are defined as those within the range of[0, 150] kilometers from a reference station/district. Panel A includes monitoring station fixed eﬀects, year fixed eﬀects,
and the following controls: precipitation and the interaction of Available Water Capacity and post-SBM indicator ofa reference district. Panels B and C include district fixed eﬀects, year fixed eﬀects, and the same controls as Panel A.
Column 3 reports a result in upstream states where treatment capacities of sewage treatment plants are higher thanthe median, while Column 4 reports a result in upstream states with lower treatment capacities. Similarly, Columns
5 and 6 compare results based on the diﬀerent levels of upstream treatment capacities at the district level. The KP
F-Stat refers to the Wald version of the Kleibergen and Paap (2006) rk-statistic on the excluded instrumental variables
for non-i.i.d. errors. The AR 95% CI reports the 95% confidence interval, which is robust to the weak instrument
based on the Anderson and Rubin (1949) test. The open-ended confidence intervals show that the searched grids do
not extend far enough to capture the point where the rejection probability crosses above the 95%.

47

Table 5: The Eﬀects on Water Quality and Health by Treatment Capacity of Fecal Sludge
All

State-level Capacity

District-level Capacity

(1)
All

(2)
High

(4)
High

(5)
Low

(3)
Low

Panel A. Dependent Variable: Log(Fecal Coliform) - IV Design
Number of latrines
per sq. km

0.030∗∗∗(0.008)-0.031(0.025)0.037∗∗∗(0.007)0.014(0.009)0.051∗∗∗(0.017)

Observations
Number of Stations
Number of Districts
KP F-Stat
AR 95% CI
Average Policy Eﬀect

7,201
1,189
337
29.954[.015, .049]
0.719

3,453
579
182
7.576
[-.123, .018]-0.666

3,748
610
155
39.516[.025, .054]
0.976

2,902
466
96
13.648[-.012, .034]
0.286

4,299
723
241
11.931[.023, .105]
1.342

Panel B. Dependent Variable: Log(Fecal Coliform) - DID Design(1 - 2013 Latrine
Coverage) * Post (= 1)

0.647(0.527)0.372(0.775)1.790∗∗∗(0.660)0.496(0.911)1.496∗∗(0.654)

Observations
R2
Number of Stations
Number of Districts

10,385
0.860
1,187
335

5,075
0.869
577
182

5,281
0.883
606
151

4,240
0.881
465
95

6,110
0.879
719
238

Panel C. Dependent Variable: Diarrheal Post-neonatal Mortality (per 1,000 people)
Number of latrines
per sq. km-0.029∗∗∗(0.005)Equality Test
Observations
Number of Districts
KP F-Stat
AR 95% CI
Mean of Dep. Variable
Average Policy Eﬀect-0.064∗∗∗(0.013)-0.020∗∗∗(0.004)p = 0.003
2,696
337
39.248[-.042, -.021]
2.282-0.8271,096
137
16.295[-.110, -.045]
2.132-1.5071,600
200
37.216[-.030, -.013]
2.386-0.625-0.023∗∗∗(0.006)-0.032∗∗∗(0.008)p = 0.384
768
96
22.345[-.044, -.013]
1.753-0.5701,928
24118.092[-.057, -.021]
2.493-0.960Notes: The coeﬃcients are reported. Standard errors, clustered at the district level, are in parentheses. ***,**, and * indicate significance at the 1%, 5%, and 10% levels, respectively. Panel A includes monitoring
station fixed eﬀects, year fixed eﬀects, and precipitation as a control. Panel B includes monitoring station
fixed eﬀects, basin-year fixed eﬀects, and the following controls: precipitation, VIIRS nighttime luminosity,
population, the proportions of Scheduled Caste and Scheduled Tribe members, and literacy rates. Panel C
includes district fixed eﬀects, year fixed eﬀects, and precipitation as a control. Column 2 reports a resultin states where treatment capacities of sewage treatment plants are higher than the median, while Column
3 reports a result in states with lower treatment capacities. Similarly, Columns 4 and 5 compare results
based on the diﬀerent levels of treatment capacities at the district level. The KP F-Stat refers to the Waldversion of the Kleibergen and Paap (2006) rk-statistic on the excluded instrumental variables for non-i.i.d.
errors. The AR 95% CI reports the 95% confidence interval, which is robust to the weak instrument basedon the Anderson and Rubin (1949) test. The open-ended confidence interval shows that the searched grids
do not extend far enough to capture the point where the rejection probability crosses above the 95%.
Average policy eﬀects are calculated by multiplying the estimated coeﬃcients by the change in the numberof latrines per square kilometer after the SBM started in 2014.

48

Table 6: The Eﬀects on Health by Exposure to River Pollution (Diarrheal Post-neonatal
Mortality (per 1,000 people))
Proximity to Rivers - 5km

Number of latrines
per sq. km
Observations
Number of Districts
KP F-Stat
AR 95% CI
Mean of Dep. Variable
Average Policy Eﬀect

Proximity to Rivers - 10km

Proximity to Rivers - 15km

(1)
High

(2)
Low

(3)
High

(4)
Low

(5)
High

(6)
Low

-0.024∗∗∗(0.004)-0.043∗∗(0.021)-0.025∗∗∗(0.004)-0.040∗∗(0.016)-0.024∗∗∗(0.004)-0.044∗∗(0.019)1,264
15850.344[-.033, -.017]
2.513-0.792

1,272
159
4.199
[..., -.019]
2.163-1.0571,280
160
36.650[-.036, -.017]
2.461-0.831

1,288
161
6.929
[..., -.019]
2.182-0.966

1,296
162
38.835[-.034, -.017]
2.499-0.782

1,296
162
5.270
[..., -.022]
2.125-1.086

Notes: The coeﬃcients are reported. Standard errors, clustered at the district level, are in parentheses. ***,**, and * indicate significance at the 1%, 5%, and 10% levels, respectively. All regressions include district fixedeﬀects, year fixed eﬀects, and precipitation as a control. Column 1 reports a result in districts where the ratiosof total nighttime luminosity within 5 kilometers of rivers are higher than the median, while Column 2 reports a
result in districts with lower ratios. Columns 3-6 similarly report results when the distance is changed to 10 and
15 kilometers. The KP F-Stat refers to the Wald version of the Kleibergen and Paap (2006) rk-statistic on the
excluded instrumental variables for non-i.i.d. errors. The AR 95% CI reports the 95% confidence interval, which isrobust to the weak instrument based on the Anderson and Rubin (1949) test. The open-ended confidence intervalshows that the searched grids do not extend far enough to capture the point where the rejection probability crosses
above the 95%. Average policy eﬀects are calculated by multiplying the estimated coeﬃcients by the change in
the number of latrines per square kilometer after the SBM started in 2014.

49

Appendix
Unintended Consequences of Sanitation: Negative Externalities on Water
Quality and Health in India
Kazuki Motohashi

Contents
A Additional Figures

52

B Additional Tables

58

List of Figures
A1
A2
A3
A4
A5
A6

Top 10 Countries by the Number of People Practicing Open Defecation in 2013 52
Event Study Plots of Reduced-Form Regressions of Available Water Capacity
in Upstream-Downstream Specification . . . . . . . . . . . . . . . . . . . . . 53
Illustration of Upstream-Downstream Analysis . . . . . . . . . . . . . . . . . 54
Diﬀerential Change in the Number of Latrines between Districts with Lower
Baseline Coverage and Districts with Higher Baseline Coverage . . . . . . . . 55
Heterogeneity in Dynamic Eﬀects on Water Pollution (Log of Fecal Coliform)
by District-level Treatment Capacities . . . . . . . . . . . . . . . . . . . . . 56
The Dynamic Eﬀects on Water Pollution (Log of Fecal Coliform) in the Caseof Binary Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

List of Tables
B1
B2
B3
B4
B5
B6

DID Results: The Eﬀect on Water Quality (Log of Fecal Coliform) in theCase of Binary Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Upstream-Downstream Analysis: Alternative Buﬀer Sizes . . . . . . . . . .
The Eﬀects on Multiple Types of Diarrheal Mortality (per 1,000 people) . .
Falsification Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Robustness Check - Spillovers from Neighboring Districts: The Eﬀect on Water Quality (Log of Fecal Coliform) . . . . . . . . . . . . . . . . . . . . . . .
Robustness Check - Influence from Urban Areas . . . . . . . . . . . . . . . .
50

58
59
60
61
62
63

B7

Robustness Check - Balanced Panel: The Eﬀect on Water Quality (Log ofFecal Coliform) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51

64

Additional Figures

e

r

qu
bi

am
oz
M

M
ad

ag

as

ca

na
hi

n
C

da
Su

er
ig
N

st
an
ki

ia

Pa

op

ia
on

Et
hi

es

ia
er
In
d

ig
N

In
di
a

0
20
40
60
80
Percentage of People practicing OD (%)

Number of people practicing OD (million)
0
100
200
300
400
500

A

Number of people practicing OD (million)
Percentage of People practicing OD (%)

Figure A1: Top 10 Countries by the Number of People Practicing Open Defecation in 2013
Notes: This figure documents the top 10 countries by the number of people
practicing open defecation. It plots both the number of people practicing open
defecation and the percentage of people practicing open defecation for these10 countries. The data source is the database of the WHO/UNICEF Joint
Monitoring Programme for Water Supply, Sanitation, and Hygiene.

52

-.02

Log(Fecal Coliform - Average)
-.01
0
.01
.02

.03

A. Water Quality: Upstream-Downstream Specification2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Diarrheal Post-neonetal Mortality (per 1,000)
-.01
-.005
0
.005
.01

B. Health: Upstream-Downstream Specification2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Figure A2: Event Study Plots of Reduced-Form Regressions of Available Water Capacity in
Upstream-Downstream Specification
Notes: This figure shows the regression coeﬃcients of the logarithm of fecal coliform (Panel A) and diarrheal
post-neonatal mortality per 1,000 people (Panels B) on the interaction terms between upstream Available
Water Capacity and year dummies. Both panels use the Available Water Capacity of upstream districts
within the range of [0, 150] kilometers from a reference district. The 95% confidence intervals are shownwith dashed lines. Standard errors are clustered at the district level. Panel A includes monitoring station
fixed eﬀects, year fixed eﬀects, and the following controls: precipitation and the interaction of Available
Water Capacity and post-SBM indicator of a reference district. Panel B includes district fixed eﬀects, year
fixed eﬀects, and the following controls: precipitation and the interaction of Available Water Capacity andpost-SBM indicator of a reference district.

53

Upstream
Districts

Monitoring Station (Reference)
150km

Downstream
Districts

Figure A3: Illustration of Upstream-Downstream Analysis
Notes: This figure shows the research design of the upstream-downstream analysis, whichanalyzes the eﬀect of latrine construction in upstream districts on water quality in a reference monitoring station (or health outcomes in a reference district). Upstream districts
are selected from districts that (i) intersect with river segments whose elevations arehigher than the elevation of the reference station (district) and (ii) fall within a range of[0, 150] kilometers from the reference station (district) in the baseline specification. This
figure shows district boundaries in grey lines and rivers in blue lines. It highlights the
upstream districts in green, the reference district in yellow, and the downstream districts
in red.

54

80
Number of latrines per square kilometer
0
20
40
60
-20
2012

2013

2014

2015

2016

2017

2018

2019

Figure A4: Diﬀerential Change in the Number of Latrines between Districts with Lower
Baseline Coverage and Districts with Higher Baseline Coverage
Notes: This figure shows the regression coeﬃcients of the number of latrines per square
kilometer on the interaction terms between (1- baseline latrine coverage in 2013) andyear dummies at the district level. The regression includes district fixed eﬀects, year fixedeﬀects, and the following controls: precipitation, VIIRS nighttime luminosity, population,
the proportions of Scheduled Caste and Scheduled Tribe members, and literacy rates. The
95% confidence intervals are shown with dashed lines. Standard errors are clustered at
the district level.

55

5
4
Log(Fecal Coliform - Average)-3 -2 -1 0 1 2 3-4

A. All2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

5

4

Log(Fecal Coliform - Average)-3 -2 -1 0 1 2 3-4-5

Notes: This figure shows the regression coeﬃcients of the logarithm of fecal coliform in equation 6. The 95% confidence
intervals are shown with dashed lines. Standard errors are clustered at the district level. All regressions include
monitoring station fixed eﬀects, basin-year fixed eﬀects, and the following controls: precipitation, VIIRS nighttime
luminosity, population, the proportions of Scheduled Caste and Scheduled Tribe members, and literacy rates. Panel
B shows a result in districts where treatment capacities of sewage treatment plants are lower than the median, while
Panel C shows a result in districts with higher treatment capacities.

Figure A5: Heterogeneity in Dynamic Eﬀects on Water Pollution (Log of Fecal Coliform) by District-level Treatment Capacities

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

C. District-level Treatment Capacity High2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

B. District-level Treatment Capacity Low-55
Log(Fecal Coliform - Average)-3 -2 -1 0 1 2 3 4
-4
-5

56

5
4
Log(Fecal Coliform - Average)-3 -2 -1 0 1 2 3-4

A. All2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

5

4

Log(Fecal Coliform - Average)-3 -2 -1 0 1 2 3-4-5

Notes: This figure shows the regression coeﬃcients of the logarithm of fecal coliform in equation 6 when I adopt abinary treatment indicator that takes the value one when the district’s latrine coverage is lower than the median.
The 95% confidence intervals are shown with dashed lines. Standard errors are clustered at the district level. All
regressions include monitoring station fixed eﬀects, basin-year fixed eﬀects, and the following controls: precipitation,
VIIRS nighttime luminosity, population, the proportions of Scheduled Caste and Scheduled Tribe members, and literacy
rates. Panel B shows a result in states where treatment capacities of sewage treatment plants are lower than the median,
while Panel C shows a result in states with higher treatment capacities.

Figure A6: The Dynamic Eﬀects on Water Pollution (Log of Fecal Coliform) in the Case of Binary Treatment

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

C. State-level Treatment Capacity High2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

B. State-level Treatment Capacity Low-554
Log(Fecal Coliform - Average)-3 -2 -1 0 1 2 3
-4
-5

57

B

Additional Tables

Table B1: DID Results: The Eﬀect on Water Quality (Log of Fecal Coliform) in the Caseof Binary Treatment
All

State-level Capacity

District-level Capacity

(1)
All

(2)
High

(3)
Low

(4)
High

(5)
Low

2013 Latrine Coverage Low (= 1)* Post (= 1)

0.425∗∗(0.213)0.311(0.284)0.475∗∗(0.234)0.830(0.500)0.253(0.230)Observations
R2
Number of Stations
Number of Districts

10,385
0.860
1,187
335

5,075
0.869
577
182

5,281
0.882
606
151

4,240
0.882
465
95

6,110
0.878
719
238

Notes: This table reports the regression coeﬃcients of the logarithm of fecal coliform inequation 5 when I change a continuous treatment measure to a binary treatment indicator thattakes the value one when the district’s latrine coverage is lower than the median. Standard
errors, clustered at the district level, are in parentheses. ***, **, and * indicate significanceat the 1%, 5%, and 10% levels, respectively. All regressions include monitoring station fixedeﬀects, basin-year fixed eﬀects, and the following controls: precipitation, VIIRS nighttime
luminosity, population, the proportions of Scheduled Caste and Scheduled Tribe members,
and literacy rates. Column 2 reports a result in states where treatment capacities of sewage
treatment plants are higher than the median, while Column 3 reports a result in states withlower treatment capacities. Similarly, Columns 4 and 5 compare results based on the diﬀerent
levels of treatment capacities at the district level.

58

Table B2: Upstream-Downstream Analysis: Alternative Buﬀer Sizes
Buﬀer Distances from Reference Stations/Districts(1)0-50km(2)0-100km(3)0-150km(4)50-150km(5)100-150km

(6)
Full

Panel A. Dependent Variable: Log(Fecal Coliform)
Upstream number of
latrines per sq. km

0.014(0.012)0.017(0.013)0.015(0.011)

0.003(0.009)0.001(0.007)0.037∗∗(0.016)

Observations
Number of Stations
Number of Districts
KP F-Stat
AR 95% CI

1,758
287
133
23.148[-.011, .049]

2,152
352
151
36.766[-.010, .048]

2,228
365
154
50.475[-.008, .039]

2,008
325
140
38.427[-.018, .021]

1,488
238
112
49.767[-.019, .014]

2,235
367
155
73.913[.005, .074]

Panel B. Dependent Variable: Diarrheal Post-neonatal Mortality (per 1,000 people)
Upstream number of
latrines per sq. km-0.011∗(0.006)-0.012∗(0.006)-0.011∗(0.006)-0.012∗(0.006)-0.017∗∗∗(0.006)0.003(0.010)Observations
Number of Districts
KP F-Stat
AR 95% CI
Mean of Dep. Variable

688
86
58.692[-.026, .002]
2.695

808
101
61.264[-.025, .001]
2.571

824
103
78.696[-.023, .001]
2.576

704
88
78.481[-.026, .001]
2.763

488
61
77.325[-.030, -.004]
3.078

840
105
83.728[-.014, .027]
2.570

Notes: The coeﬃcients are reported. Standard errors, clustered at the district level, are in parentheses. ***,**, and * indicate significance at the 1%, 5%, and 10% levels, respectively. The sample is limited to monitoring
stations (Panel A) and districts (Panel B) located along major rivers in India. In Columns 1-5, I change buﬀer
sizes for identifying upstream districts. In Column 6, I include all upstream districts without the restriction on
a buﬀer size. Panel A includes monitoring station fixed eﬀects, year fixed eﬀects, and the following controls:
precipitation and the interaction of Available Water Capacity and post-SBM indicator of a reference district.
Panel B includes district fixed eﬀects, year fixed eﬀects, and the same controls as Panel A. The KP F-Stat refers
to the Wald version of the Kleibergen and Paap (2006) rk-statistic on the excluded instrumental variables for
non-i.i.d. errors. The AR 95% CI reports the 95% confidence interval, which is robust to the weak instrument
based on the Anderson and Rubin (1949) test.

59

Table B3: The Eﬀects on Multiple Types of Diarrheal Mortality (per 1,000 people)

Number of latrines
per sq. km
Observations
Number of Districts
KP F-Stat
AR 95% CI
Mean of Dep. Variable
Average Policy Eﬀect(1)
Early-neonatal(2)Late-neonatal(3)
Post-neonatal(4)
Age 1-4(5)
Under 5-0.240∗∗∗(0.040)-0.108∗∗∗(0.018)-0.029∗∗∗(0.005)-0.006∗∗∗(0.001)-0.013∗∗∗(0.002)

2,696
337
39.248[-.345, -.175]
16.428-6.8002,696
337
39.248[-.156, -.079]
7.663-3.0782,696
337
39.248[-.042, -.021]
2.282-0.8272,696
337
39.248[-.009, -.005]
0.365-0.1742,696
337
39.248[-.018, -.009]
0.859-0.355

Notes: The coeﬃcients are reported. Standard errors, clustered at the district level, are in parentheses.
***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. All regressions include
district fixed eﬀects, year fixed eﬀects, and precipitation as a control. The sample is limited to districts
that have monitoring stations used in the specification of water quality. The KP F-Stat refers to theWald version of the Kleibergen and Paap (2006) rk-statistic on the excluded instrumental variables for
non-i.i.d. errors. The AR 95% CI reports the 95% confidence interval, which is robust to the weak
instrument based on the Anderson and Rubin (1949) test. Average policy eﬀects are calculated bymultiplying the estimated coeﬃcients by the change in the number of latrines per square kilometer after
the SBM started in 2014.

60

61

7,176
1,189
337
IV
29.240[-.001, .001]

0.0001(0.0005)(4)

11,069
0.528
1,187
335
DID
-

-0.0060(0.0117)Log(pH)
(3)

7,084
1,184
336
IV
28.067[-.011, .005]-0.0034(0.0037)(6)

10,928
0.845
1,185
335
DID
-

-0.1051(0.1372)

Log(BOD)
(5)

7,094
1,181
336
IV
29.955[-.003, .001]-0.0008(0.0010)(7)

10,920
0.763
1,180
334
DID
-

0.0448(0.0368)(8)

Log(Dissolved O2)

6,379
1,142
319
IV
7.991
[..., .032]

0.0003(0.0162)

(9)

7,494
0.680
1,149
320
DID
-

-0.5631∗(0.3036)(10)

Log(Nitrate+Nitrite)

2,696
337
IV
39.248[-.0002, .0003]

0.0000(0.0001)(11)

Malaria Mortality (%)

Notes: The coeﬃcients are reported. Standard errors, clustered at the district level, are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels,
respectively. The IV specification in Columns 1, 3, 5, 7, and 9 includes monitoring station fixed eﬀects, year fixed eﬀects, and precipitation as a control. The IV specificationin Column 11 includes district fixed eﬀects, year fixed eﬀects, and precipitation as a control. The DID specification in Columns 2, 4, 6, 8, and 10 includes monitoring station
fixed eﬀects, basin-year fixed eﬀects, and the following controls: precipitation, VIIRS nighttime luminosity, population, the proportions of Scheduled Caste and Scheduled Tribemembers, and literacy rates. In Column 11, malaria mortality estimates for ages 0-4 are used. The KP F-Stat refers to the Wald version of the Kleibergen and Paap (2006)
rk-statistic on the excluded instrumental variables for non-i.i.d. errors. The AR 95% CI reports the 95% confidence interval, which is robust to the weak instrument based on theAnderson and Rubin (1949) test. The open-ended confidence interval shows that the searched grids do not extend far enough to capture the point where the rejection probability
crosses above the 95%.

10,945
0.839
1,185
334
DID
-

Observations
R2
Number of Stations
Number of Districts
Identification
KP F-Stat
AR 95% Cl

7,103
1,179
334
IV
28.358[-.005, .001]-0.0297(0.0319)-0.0023(0.0015)(2)(1 - 2013 Latrine Coverage)* Post (= 1)

Number of latrines per sq. km(1)

Log(Temperature)

Table B4: Falsification Tests

Table B5: Robustness Check - Spillovers from Neighboring Districts: The Eﬀect on Water
Quality (Log of Fecal Coliform)
All

State-level Capacity

District-level Capacity

(1)
All

(2)
High

(3)
Low

(4)
High

(5)
Low

Number of latrines
per sq. km

0.027∗∗∗(0.007)-0.027(0.019)0.037∗∗∗(0.006)0.017∗(0.009)0.043∗∗∗(0.013)Observations
Number of Stations
Number of Districts
KP F-Stat
AR 95% CI
Average Policy Eﬀect

7,253
1,197
489
44.626[.013, .042]
0.655

3,605
603
260
14.440[-.076, .010]-0.599

3,648
594
229
54.539[.027, .050]
0.952

3,300
529
185
26.013[-.003, .036]
0.362

3,953
668
304
15.433[.021, .081]
1.140(1 - 2013 Latrine
Coverage) * Post (= 1)

0.663(0.588)0.952(0.869)1.542∗∗(0.732)0.699(0.877)1.573∗∗(0.783)Observations
R2
Number of Stations
Number of Districts

10,464
0.862
1,194
487

5,131
0.863
581
244

5,285
0.897
608
238

4,850
0.878
528
184

5,573
0.887
663
300

Panel A. IV Design

Panel B. DID Design

Notes: The coeﬃcients are reported. Standard errors, clustered at the district level, are inparentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Panel A includes monitoring station fixed eﬀects, year fixed eﬀects, and precipitation as acontrol. Panel B includes monitoring station fixed eﬀects, basin-year fixed eﬀects, and thefollowing controls: precipitation, VIIRS nighttime luminosity, population, the proportionsof Scheduled Caste and Scheduled Tribe members, and literacy rates. Column 2 reports a
result in states where treatment capacities of sewage treatment plants are higher than the
median, while Column 3 reports a result in states with lower treatment capacities. Similarly,
Columns 4 and 5 compare results based on the diﬀerent levels of treatment capacities at the
district level. The KP F-Stat refers to the Wald version of the Kleibergen and Paap (2006) rkstatistic on the excluded instrumental variables for non-i.i.d. errors. The AR 95% CI reports
the 95% confidence interval, which is robust to the weak instrument based on the Anderson
and Rubin (1949) test. The open-ended confidence interval shows that the searched grids do
not extend far enough to capture the point where the rejection probability crosses above the95%. Average policy eﬀects are calculated by multiplying the estimated coeﬃcients by thechange in the number of latrines per square kilometer after the SBM started in 2014.

62

Table B6: Robustness Check - Influence from Urban Areas
No Exclusion

50km Exclusion

100km Exclusion

150km Exclusion

(1)

(2)

(3)

(4)

Panel A. Dependent Variable: Log(Fecal Coliform) - IV Design
Number of latrines
per sq. km

0.030∗∗∗(0.008)0.039∗∗∗(0.010)0.050∗∗∗(0.015)0.072∗∗(0.035)Observations
Number of Stations
Number of Districts
KP F-Stat
AR 95% CI
Average Policy Eﬀect

7,201
1,189
337
29.954[.015, .049]
0.719

5,295
890
284
25.785[.021, .067]
1.035

3,716
623
196
17.574[.026, .099]
1.369

2,492
421
125
5.693
[.026, ...]
1.902

Panel B. Dependent Variable: Log(Fecal Coliform) - DID Design(1 - 2013 Latrine
Coverage) * Post (= 1)

0.647(0.527)0.426(0.643)0.559(0.916)0.092(1.204)

Observations
R2
Number of Stations
Number of Districts

10,385
0.860
1,187
335

7,531
0.861
888
282

5,228
0.883
620
193

3,458
0.886
415
119

Panel B. Dependent Variable: Diarrheal Post-neonatal Mortality (per 1,000 people)
Number of latrines
per sq. km
Observations
Number of Districts
KP F-Stat
AR 95% CI
Mean of Dep. Variable
Average Policy Eﬀect-0.029∗∗∗(0.005)-0.028∗∗∗(0.006)-0.028∗∗∗(0.006)-0.028∗∗∗(0.006)2,696
337
39.248[-.042, -.021]
2.282-0.8271,512
18922.288[-.049, -.019]
2.395-0.8241,512
18922.288[-.049, -.019]
2.395-0.8241,512
18922.288[-.049, -.019]
2.395-0.824

Notes: The coeﬃcients are reported. Standard errors, clustered at the district level, are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. In Columns
2-4, I exclude monitoring stations (Panel A) and districts (Panel B) that are within a specified
distance from cities that have a population of 1 million and above. Panel A includes monitoring
station fixed eﬀects, year fixed eﬀects, and precipitation as a control. Panel B includes monitoring
station fixed eﬀects, basin-year fixed eﬀects, and the following controls: precipitation, VIIRS nighttime luminosity, population, the proportions of Scheduled Caste and Scheduled Tribe members,
and literacy rates. Panel C includes district fixed eﬀects, year fixed eﬀects, and precipitation as acontrol. The KP F-Stat refers to the Wald version of the Kleibergen and Paap (2006) rk-statisticon the excluded instrumental variables for non-i.i.d. errors. The AR 95% CI reports the 95% confidence interval, which is robust to the weak instrument based on the Anderson and Rubin (1949)
test. Average policy eﬀects are calculated by multiplying the estimated coeﬃcients by the change
in the number of latrines per square kilometer after the SBM started in 2014.

63

Table B7: Robustness Check - Balanced Panel: The Eﬀect on Water Quality (Log of Fecal
Coliform)
All

State-level Capacity

District-level Capacity

(1)
All

(2)
High

(3)
Low

(4)
High

(5)
Low

Number of latrines
per sq. km

0.024∗∗∗(0.009)-0.010(0.022)0.031∗∗∗(0.008)0.009(0.012)0.039∗∗∗(0.014)

Observations
Number of Stations
Number of Districts
KP F-Stat
AR 95% CI
Average Policy Eﬀect

3,776
472
158
12.357[.009, .048]
0.644

1,552
194
75
12.512[-.072, .032]-0.209

2,224
278
83
13.449[.018, .053]
0.926

1,600
200
53
4.018
[..., .048]
0.210

2,176
272
105
7.917
[.018, .086]
1.137(1 - 2013 Latrine
Coverage) * Post (= 1)

1.001(0.781)0.931(1.146)5.352∗∗∗(1.380)2.993∗(1.616)1.206(1.000)Observations
R2
Number of Stations
Number of Districts

3,433
0.838
276
145

1,625
0.877
130
70

1,781
0.867
144
73

1,572
0.879
126
57

1,847
0.841
149
87

Panel A. IV Design

Panel B. DID Design

Notes: The coeﬃcients are reported. Standard errors, clustered at the district level, are inparentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
The sample is limited to monitoring stations that have observations every year from 2012
to 2019, which yields a balanced panel. Panel A includes monitoring station fixed eﬀects,
year fixed eﬀects, and precipitation as a control. Panel B includes monitoring station fixedeﬀects, basin-year fixed eﬀects, and the following controls: precipitation, VIIRS nighttime
luminosity, population, the proportions of Scheduled Caste and Scheduled Tribe members,
and literacy rates. Column 2 reports a result in states where treatment capacities of sewage
treatment plants are higher than the median, while Column 3 reports a result in stateswith lower treatment capacities. Similarly, Columns 4 and 5 compare results based on thediﬀerent levels of treatment capacities at the district level. The KP F-Stat refers to theWald version of the Kleibergen and Paap (2006) rk-statistic on the excluded instrumental
variables for non-i.i.d. errors. The AR 95% CI reports the 95% confidence interval, which
is robust to the weak instrument based on the Anderson and Rubin (1949) test. The openended confidence interval shows that the searched grids do not extend far enough to capture
the point where the rejection probability crosses above the 95%. Average policy eﬀects arecalculated by multiplying the estimated coeﬃcients by the change in the number of latrines
per square kilometer after the SBM started in 2014.

64