Research Paper

1 |C h u
Justin Chu
11/9/15
ECON 171a
India’s Sanitation Push: Impact on Mortality
Introduction:
For the past two decades, the Indian government has made a strong push to eliminate
open defecation, especially in rural areas due to the prevalence of death through rotaviruses –
viruses transmitted through the fecal-oral route. These viruses cause nearly 334,000 deaths
among children out of the 2.3 million deaths in India each year – nearly 15% of all deaths.1
The
main cause for such prevalence of these diseases in India is a lack of sanitary toilets in many
village centers which causes many residents to resort to open defecation. Starting in 1992, the
Indian government launched a program called Nirmal Bharat Abhiyan (renamed the Total
Sanitation Campaign in 2012) to incentivize community-led total sanitation (CLTS). This CLTS
effort revolved around providing localized means of construction through toilet construction
subsidies and lump sums to villages marked as “open defecation free.”2
. After his election in
2014, Indian Prime Minister Narendra Modi launched Swachh Bharat Abhiyan (Clean India
Mission) which launched the biggest cleanliness drive in Indian history.3
The majority of this
program focuses on the construction of Individual Household Latrines (further known as
IHHLs), cluster, community, and school toilets in all Gram Panchayats (municipalities).4
Part of
this program brought the monitoring into the social media realm with the inception of an app
used to take pictures and track progress of each finished construction project in a central internet
database. This database is sorted by time, state, and construction type; by sorting this data with
1
“Rotavirus mortality in India: estimates based on a nationally representative survey of diarrhoeal deaths,” World
Health Organization, Bulletin of the World Health Organization, 2012; vol. 90: 720-727.
2
“Restructuring of the Nirmal Bharat Abhiyan into Swachh Bharat Mission,” Press Information Bureau,
Government of India, Sep 24 2014.
3
Ibid.
4
Ibid.

2 |C h u
diagnosed deaths from diarrheal diseases, one can begin to regress the effects of the new toilet
implementation program on overall health.
Literature Review:
For the bulk of the data in terms of application of the program, the source will be the
SBM website which carries the completed construction data based on the number of unique
geotags placed by each district user. Unfortunately, access does not always equate to utilization
as many of the rural poor feel as though these new toilets are not beneficial or necessary to their
own lives.5
In addition, many of our human development numbers on an annual basis will come
from the Center Bureau for Health Intelligence as well as the Annual Status of Education Report
which is facilitated by Pratham. As was previously stated, these numbers may not truly reflect
the overall impact of these individual variables in India; however, they offer very comprehensive
health and education surveys respectively, especially on the state-level. By utilizing these more
focused studies, one can create a more satisfactory model.
The main area of focus for the research will be the states of Uttar Pradesh, Madhya
Pradesh, and Rajasthan which are all along the northern edge of India. These states were chosen
due to their BIMARU status which is an acronym of the state names which is coincidentally – or
not – very similar to the Hindi word bimar meaning "sick." Bihar was not chosen due to the lack
of reported data in terms of both the SBM/NBA and disease death related data. These BIMARU
states share geographical boundaries as well as the fact that they all lag behind the rest of India in
terms of overall development factors such as real GDP growth and human development goals.
By targeting these less-developed areas, the impact of the program itself as opposed to other state
5
“PM Modi fulfills promise of 80 lakh toilets but not many takers in rural India,” Sharma, Neetu: India Today, Aug
14, 2015. Web.

3 |C h u
factors should be more distinct especially given their relatively similar low strata of each state
involved.
In terms of other studies referenced, press releases and other similar nongovernmental
organizations reflecting general conditions of water and sanitation within the borders; however,
the bulk of the literature will come from the data provided by the state level and the reflections
provided in the model. In addition, the Indonesian government started a similar program which
can serve part of understanding the challenges facing India. Since the program is still developing,
many of the studies and accompanying surveys that would address the SBM’s efficacy are not
completed; despite that, India, and especially the Indian government, utilized similar structures
in their push to mitigate air and water pollution which is analyzed in a paper by Michael
Greenstone and Rema Hanna called Environmental Regulations, Air and Water Pollution, and
Infant Mortality in India. While the aforementioned government programs are not perfect
representations of the SBM, they each serve as important proxy lessons to create an analytic
apparatus for the SBM. The model combined with third-party research and on-the-ground stories
in newspapers and magazines will serve as the basis of the policy conclusions by combining
empirical statistics and insider perspective to create the most accurate picture as many of the
numbers can be deceptive without utilizing the most pertinent information.
Model
The basis of the model is a non-linear state-fixed effects regression in which nearly all of
the variables are being measured with the natural logarithm transformation applied to them. The
natural logarithmic transformation on the dependent variable (Yjt), total instances of diarrhea or
diarrheal deaths, and the independent variables: total toilets constructed, GDP per capita, total
Model 1 - Nonlinear regression using income

4 |C h u
sanitation facilities (for drinking water) constructed, boils down the analysis to the forms of
percentages. For example, a 10% increase of an independent variable will result in a 1.10*β (the
coefficient in front of the dependent variable) change in the dependent variable. By using a non-
linear approach, the implicit assumption is that toilet construction, while holding all other factors
constant, does not have a fixed impact on instances of disease and death regardless of existing
facilities. In addition, the state fixed effects are measured in the (n-1) model in which the first
state is dropped and measured through the constant and the other states are attributed to certain
state ids.
In the above equation, β1 represents overall toilets constructed. β2 is measured in level 1 –
a baseline test for literacy issued to children which serves as a proxy for parental education since
many of these illiterate children most likely come from illiterate households and vice versa –
rural literacy rates. β3 is the GDP per capita as measured by overall GDP produced by each state
divided by the population and measured in crores, and β4 is the annual state production of
covered water dispensaries to combat clean water problems in the area. In addition, µt represents
the fixed effects of each state (Uttar Pradesh, Madhya Pradesh, and Rajasthan). As is the case in
all regressions, these variables were chosen to address any omitted variables that may have an
impact on Yjt beyond toilet construction. Literacy and extended education as a result, income,
and construction of clean water projects all could have an impact on conception of an easily-
transmitted, yet easily-avoided malady like acute diarrhea. The observations take five years’
worth of data on the state-by-state annual statistics; therefore, with the five years from 2010-
2014 and the three states, there are 15 observations in the panel data model.
However, the model can be tweaked in a more focused way to understand how the SBM
and NBA affected people and consequently – the impact of the program on disease. For the

5 |C h u
NBA, the program paid households subsidies to build the toilets in their own homes. The
government offered 4600 rupees to any homeowner, especially poor home owners, to construct
their own individual toilets.6
As a result, there exists an interaction factor between individual
toilet constructions because building a toilet has the cash incentive which may make the
homeowner more likely to begin construction of the toilet, consequently, impacting their disease
instances. Due to the multicollinearity of GDP per capita and the interaction term of the
continuous variable of GDP per capita and toilets constructed, the variable for GDP per capita is
dropped out and exchanged for the interaction term.
Model 2 - New regression with interaction term
Data
For the 15 panel observations, most of the data falls into a relatively reasonable range
when applying the natural logarithmic transformation; unfortunately, when trying to expand the
observations by increasing the time scale from annual to quarterly or monthly is not applicable as
many of the observations fluctuate in random patterns as the construction numbers are delayed in
their reporting. Subsequently, the same problem emerges when attempting to use district level
data as the other data such as cases of acute diarrhea or literacy rate are not accurately reported
for each year. Therefore, using the annual data is the only way to insure validity for the
measurements within the data. Unfortunately, when attempting to run the extended fixed effects
regression model, the increased number of independent variables minimized the observations in
favor of degrees of freedom used in the F-test which is a measure to illustrate the fit of a
regression model on the data.
6
“Restructuring of the Nirmal Bharat Abhiyan into Swachh Bharat Mission,” Press Information Bureau,
Government of India, Sep 24 2014.

6 |C h u
Looking at Figure 5, while the absolute totals of the variables do suffer from
occasionally large variances and, consequently, high standard deviations, they still form only a
small fraction of the mean in most cases meaning the points do not differ too much as a
proportion of the mean. That point can be illustrated in examining the kurtosis of the tails which
indicate the amount of outliers within the observations; most of these points do not exceed the
“baseline” value of 3 implying the tails are not oversaturated with observations which would
hamper the OLS assumptions of the model. For the total values, especially for the construction
numbers, one must expect a rather large fluctuation between the points given the differences
between states which will be covered more in the model results.
Looking at Figure 6, one can see that the dependent variable (“lntotalcases”) does not
have a very large standard deviation which is indicative of the logarithmic transformation as the
natural logarithm requires rather large values to fluctuate in value; however, it is also indicative
that the percentages of those contracting these symptoms do not vary greatly from year to year.
The same can be said for the independent variables (“lntoilets”, “lnBPLtoilets”, “lnGDPPC”,
“lev1rurallit, lnsharespent”). In addition, the kurtosis and skewness of each variable except
“lnsharespent” reflects a relatively normal distribution of the points indicates there are no strong
outliers which show strength in the data.
Case Studies
The main analytic lens applied for overall success for these types of programs in a similar
situation will be the implementation of the toilet construction initiative in Indonesia; the crucial
paper is a working paper written by the World Bank entitled Impact Evaluation of a Large-Scale
Rural Sanitation Project in Indonesia written by Lisa Cameron, Manisha Shah, and Susan Olivia
(hereafter referred to as Cameron et al.) The writers studied eight rural districts in 160

7 |C h u
communities in Indonesia attempting to study the Total Sanitation and Sanitation Market
(TSSM) project.7
While the toilet construction in the treated communities increased by three
percentage points and diarrhea prevalence was only 3.3 percent in the treated areas compared to
4.6 in those that were not, it did not impact sanitation habits such as washing hands, open
defecation, or using soap which also have a large impact on diarrhea conception.8
When
measuring certain baseline demographic descriptive statistics such as defecting in an improved
sanitation facility, in the open, washing hands after going to the toilet, and having soap, none of
the respective difference-in-difference p-values are statistically significant except for the
construction of toilet variable, for obvious reasons.9
Unfortunately, that means that the program’s
efficacy was not explicitly tied to toilet construction but the decreases in diarrhea in the control
area may be correlated to other factors outside of the scope of the TSSM. For the SBM, this
factor is important in attempting to discern the impact of toilets and knowledge of good
sanitation practices by itself.
The supplementary case study is the piece written by Greenstone and Hanna in which
they discuss the efficacy of the Indian government attempting to rein in air and water pollution:
the Supreme Court Action Plans and Mandated Catalytic Converters, as well as the National
River Conservation Plan.10
Through a Quandt likelihood ratio test, the researchers found that
there was almost no impact from the adoption of the National River Conservation Plan (NRCP)
but a definitive structural break from the air pollution policies from which very changes emerged
as an engaged public galvanized the additional push needed to execute the policy despite less
7
“Impact Evaluation of a Large-Scale Rural Sanitation Project in Indonesia,” Cameron et.al, The World Bank,
Water and Sanitation Program, Feb 2013.
8
Ibid.
9
Ibid.
10
“Environmental Regulations, Air and Water Pollution, and Infant Mortality in India,” Greenstone et al,
Massachusetts Institute of Technology, Feb 2011.

8 |C h u
than efficient government structures.11
The researchers concluded that this break emerged from a
factor of utility juxtaposed with more energized and efficient institutions (the Supreme Court)
that could effectively legislate and dole out proper regulations and create enforcement
mechanisms.12
In addition, the study showed that the efficacy of the program was tied to the
high avoidance costs for air pollution as people can only breathe air in a certain medium and
cannot buy imported air while water is more easily cleaned through boiling it.13
The main
conclusion from these points to the eventual success or failure or the SBM is that the government
policies can succeed when the populace stands behind the program and encourages their own
part of the policy. Another conclusion is that the program functions best when the avoidance
costs are high and people feel a definitive change in their avoidance of the pollutants, especially
in a comparison between water and air pollutants.
Results
While much of the program is rather new, the results gathered were a mixed affair in
terms of expectations. Before the experimentation with the model, many of the stories regarding
the efficacy of the SBM stated that many of those who had toilets constructed in their homes or
local townships felt too constrained to use the new toilets provided to them and still chose to
defecate in the open. Due to these untraceable biases, many of the independent variables lack the
statistical significance needed to assert their own validity as variables. While it is possible that
the lack of significance for some of these variables is due to the overall lack of observations,
looking at the graphs in Figure 2 clearly shows trends that would support the present hypothesis
that increased facilities and increased pressure to use them will decrease the instances of diarrhea
11
“Environmental Regulations, Air and Water Pollution, and Infant Mortality in India,” Greenstone et al,
Massachusetts Institute of Technology, Feb 2011.
12
Ibid.
13
Ibid.

9 |C h u
in these poor states. For example, the regression results in Figure 3 using the robust standard
error assumption still show the same relative t-statistic which implies that these observations are
relatively consistent even if the statistical significance is rather small compared to the desired
level. However, with that being said, there is clearly some form of bias that is accounting for the
fluctuations in the total instances which is clearly encapsulated in the variables marking the state
id in both Figure 1 and Figure 3 as state id 2 (Madhya Pradesh) and state id 3 (Rajasthan) are
statistically significant to a reasonable degree. In addition, the variable for GDP per capita
measured a positive variable, meaning that increases in overall GDP per capita may be
detrimental to health; however, one must consider the impact of the subsidies on the toilet and
see if that extra incentive at all impacts the overall levels of diarrhea.
While looking at the other model comparing the interaction factor of GDP per capita and
total below poverty line toilets constructed in a logarithmic transformation in Figure 4, there is a
clear significance level of both the construction of toilets below the poverty line and the subsidy
put in place for that construction. Unfortunately, the signs for both variables are negative and
positive respectively meaning that the impact of the subsidy for each toilet built is negatively
increasing or time. Thus, the model implies that the impact of toilet building with the subsidy,
which is already built into the legislation, the toilets themselves are only effective to a certain
point in a case of diminishing margins of returns. While many of the independent variables in
this regression lack overall statistical significance, it is important to include them as they serve to
eliminate any omitted variable bias. Whether or not the overall independent variables are
statistically significant, the individual state levels must also be considered in order to gain the
full picture.

10 |C h u
In analyzing both Model 1 and Model 2, there is a clear statistical significance in both
Madhya Pradesh (state 2) and Rajasthan (state 3) as opposed to Uttar Pradesh (constant).
Additionally, the signs on both of the states are negative which indicates that there are
underlying factors within each state that are not necessarily outlined in the model which help
lower the percentages of diarrhea occurrence. Therefore, there is some unknown factor or factors
in the model which shows that the states Madhya Pradesh and Rajasthan are healthier than Uttar
Pradesh beyond the variables stated in the two different regressions.
Conclusions and Policy
The program push for a “Clean India” is certainly not without its warts or inefficiencies;
however, the impact of toilets and toilet usage is still yet to be determined under the SBM and
NBA. Unfortunately, it is clear that toilets are not the only factor in overall health due to the fact
that they are not necessarily the biggest variable factor in the models; this news is of no real
surprise given the lessons provided in Indonesia with their TSSM program. While looking at the
stories surrounding the program itself, the reception has not been incredibly outstanding. In an
interview with Kartikay Mehrotra of Bloomberg Business, a woman named Sunita in Uttar
Pradesh stated that “only Dalits, the lowest Hindu caste, should be exposed to excrement in a
closed space.”14
That mentality is the main problem in the overall longevity of the program;
without people being willing to use the toilets, they are effectively useless in preventing
instances of diseases. As was the case in Indonesia, one can lead the program and build all the
sanitary conditions for toilets as necessary but they are not useful when left idle. As stated by
Archana Patkar, a program manager at the Water Supply & Sanitation Collaborative Council, “‘
the problem has gotten worse with the government thinking this is a supply driven problem. The
14
“India’s Toilet Race Failing as Villages Don’t Use Them,” Mehrotra, Kartikay, Bloomberg Business, Aug 4,
2014. Web.

11 |C h u
problem is that germs are invisible, and so understanding the threat of open defecation is far
removed from reality.”15
Therefore, the next step is doing internal research on the mentality
surrounding toilet usage to enlighten people to the changes that clean defecation practices can
have in their lives. Without properly addressing the mentality around the usage of toilets as
something only meant for lower strata of society, there is only so much that can be accomplished
by building toilets without any education. For example, when the World Bank released their
study on Indonesia, surveys on good sanitary behavior were released to both the treatment and
control group on their understanding of these topics; these results showed that the changes in
construction of sanitary areas did not create a significant impact on beliefs around proper
sanitation.16
By educating people below the poverty line, as the caste is not necessarily tied to
economic status, there can be a real impact of using those toilets on diarrhea death and diseases;
however, as the old saying goes, you can lead a horse to a toilet but you cannot make it use the
toilet.
15
“India’s Toilet Race Failing as Villages Don’t Use Them,” Mehrotra, Kartikay, Bloomberg Business, Aug 4,
2014. Web.
16
“Impact Evaluation of a Large-Scale Rural Sanitation Project in Indonesia,” Cameron et.al, The World Bank,
Water and Sanitation Program, Feb 2013.

12 |C h u
Figures:
Figure 1 – fixed effects regression using below poverty line household toilet construction
measurements.
Figure 2 - Graphs showing the changes in total cases of diarrhea and other indicator
variables measured by state id (1- UP, 2 - Madhya Pradesh, 3- Rajasthan)
_cons 3.836265 6.993411 0.55 0.598 -12.29057 19.9631
_Istate_id_3 -1.325917 .5417518 -2.45 0.040 -2.575199 -.0766347
_Istate_id_2 -.4617604 .4158162 -1.11 0.299 -1.420634 .4971135
lnGDPPC 1.230642 .6481366 1.90 0.094 -.2639639 2.725248
lev1rurallit .0206392 .0965196 0.21 0.836 -.2019354 .2432137
lntotalsancov -.1230762 .090466 -1.36 0.211 -.3316911 .0855387
lnBPLtoilets -.19465 .1377828 -1.41 0.195 -.5123778 .1230778
lntotalcases Coef. Std. Err. t P>|t| [95% Conf. Interval]
Robust
Root MSE = .27938
R-squared = 0.7461
Prob > F = 0.0002
F(6, 8) = 21.29
Linear regression Number of obs = 15
i.state_id _Istate_id_1-3 (naturally coded; _Istate_id_1 omitted)
. xi: regress lntotalcases lnBPLtoilets lntotalsancov lev1rurallit lnGDPPC i.state_id, r

13 |C h u
Figure 3 - Same regression as Figure 1 but with robust heteroskedastic errors
Figure 4- Same regression as Figures 1 and 3 but including a subsidy interaction term
(which drops out GDP variable)
_cons 1.40949 6.554861 0.22 0.835 -13.70605 16.52503
_Istate_id_3 -1.439068 .49245 -2.92 0.019 -2.57466 -.3034764
_Istate_id_2 -.5625891 .3866586 -1.46 0.184 -1.454226 .3290473
lnsharespent -.1044713 .0528974 -1.97 0.084 -.226453 .0175104
lnGDPPC 1.310905 .5904148 2.22 0.057 -.0505935 2.672404
lev1rurallit .0103106 .098293 0.10 0.919 -.2163535 .2369747
lnBPLtoilets -.1013052 .1005108 -1.01 0.343 -.3330836 .1304732
Robust
Root MSE = .26847
R-squared = 0.7656
Prob > F = 0.0004
F(6, 8) = 16.82
. xi: regress lntotalcases lnBPLtoilets lev1rurallit lnGDPPC lnsharespent i.state_id, r
_cons 3.836247 6.993453 0.55 0.598 -12.29068 19.96318
_Istate_id_3 -1.325916 .5417537 -2.45 0.040 -2.575203 -.0766302
_Istate_id_2 -.461761 .4158177 -1.11 0.299 -1.420638 .4971164
lnGDPxBPLtoilets 1.230642 .6481394 1.90 0.094 -.26397 2.725254
lev1rurallit .0206393 .09652 0.21 0.836 -.2019362 .2432148
lntotalsancov -.1230756 .0904663 -1.36 0.211 -.3316912 .08554
lnBPLtoilets -1.425292 .7257946 -1.96 0.085 -3.098977 .2483936
Robust
Root MSE = .27938
R-squared = 0.7461
Prob > F = 0.0002
F(6, 8) = 21.29
> ate_id, r
. xi: regress lntotalcases lnBPLtoilets lntotalsancov lev1rurallit lnGDPxBPLtoilets i.st

14 |C h u
99% 660.3888 660.3888 Kurtosis 5.810532
95% 660.3888 373.6622 Skewness 1.712458
90% 373.6622 257.7996 Variance 26962.84
75% 256.8474 256.8474
Largest Std. Dev. 164.2036
50% 144.026 Mean 183.3494
25% 56.7074 56.7074 Sum of Wgt. 15
10% 43.5264 54.2441 Obs 15
5% 3.75 43.5264
1% 3.75 3.75
Percentiles Smallest
sharespent
99% 72391.2 72391.2 Kurtosis 2.136755
95% 72391.2 65658.42 Skewness .5419822
90% 65658.42 61480.77 Variance 1.94e+08
75% 58636.92 58636.92
50% 42496.59 Mean 45256.68
25% 33366.44 33366.44 Sum of Wgt. 15
10% 30038.24 32087.83 Obs 15
5% 26539.93 30038.24
1% 26539.93 26539.93
GDPPC
99% 2915407 2915407 Kurtosis 6.888059
95% 2915407 1613384 Skewness 2.03895
90% 1613384 1166016 Variance 4.74e+11
75% 900769 900769
50% 648792 Mean 818640.1
25% 515427 515427 Sum of Wgt. 15
10% 252800 266197 Obs 15
5% 134873 252800
1% 134873 134873
totaltoilets
99% 1042578 1042578 Kurtosis 4.097271
95% 1042578 711103 Skewness 1.377213
90% 711103 621743 Variance 7.53e+10
75% 472521 472521
50% 213312 Mean 328844.5
25% 158175 158175 Sum of Wgt. 15
10% 81700 102905 Obs 15
5% 45359 81700
1% 45359 45359
totalBPLtoilets
99% 826246 826246 Kurtosis 2.096739
95% 826246 768021 Skewness -.2490236
90% 768021 745457 Variance 3.66e+10
75% 740328 740328
50% 535012 Mean 539293.5
25% 431893 431893 Sum of Wgt. 15
10% 227571 290705 Obs 15
5% 223106 227571
1% 223106 223106
totalcases
Figure 5- Summary Statistics of all dependent and independent variables in absolute form

15 |C h u
99% 6.492829 6.492829 Kurtosis 5.626488
95% 6.492829 5.923352 Skewness -1.469123
90% 5.923352 5.552183 Variance 1.445991
75% 5.548482 5.548482
50% 4.969994 Mean 4.76409
25% 4.037905 4.037905 Sum of Wgt. 15
10% 3.773368 3.993494 Obs 15
5% 1.321756 3.773368
1% 1.321756 1.321756
lnsharespent
99% 16.2 16.2 Kurtosis 2.161826
95% 16.2 15.9 Skewness .2154488
90% 15.9 15.1 Variance 2.265524
75% 15.1 15.1
50% 13.3 Mean 13.58667
25% 12.7 12.7 Sum of Wgt. 15
10% 11.4 12.4 Obs 15
5% 11.2 11.4
1% 11.2 11.2
lev1rurallit
99% 11.18984 11.18984 Kurtosis 1.938605
95% 11.18984 11.09222 Skewness .1607812
90% 11.09222 11.02648 Variance .0917857
75% 10.97912 10.97912
Largest Std. Dev. .3029615
50% 10.65718 Mean 10.67694
25% 10.41531 10.41531 Sum of Wgt. 15
10% 10.31023 10.37623 Obs 15
5% 10.18641 10.31023
1% 10.18641 10.18641
lnGDPPC
99% 13.85721 13.85721 Kurtosis 2.60935
95% 13.85721 13.47457 Skewness -.1402715
90% 13.47457 13.34028 Variance .700332
75% 13.06584 13.06584
50% 12.27051 Mean 12.39531
25% 11.97146 11.97146 Sum of Wgt. 15
10% 11.31081 11.54156 Obs 15
5% 10.72236 11.31081
1% 10.72236 10.72236
lnBPLtoilets
99% 13.62465 13.62465 Kurtosis 2.629621
95% 13.62465 13.55157 Skewness -.8406101
90% 13.55157 13.52175 Variance .1756777
75% 13.51485 13.51485
50% 13.19004 Mean 13.1254
25% 12.97593 12.97593 Sum of Wgt. 15
10% 12.33522 12.58006 Obs 15
5% 12.3154 12.33522
1% 12.3154 12.3154
lntotalcases
Figure 6- Summary Statistics of all dependent and independent variables in natural logarithm
form

16 |C h u
References:
1. “ASER Yearly Reports”, Aser Centre, Pratham Education Foundation,
http://asercentre.org, Web.
2. “CHBI National Health Profiles”, Central Bureau of Health Intelligence,
http://cbhidghs.nic.in , Web.
3. “Environmental Regulations, Air and Water Pollution, and Infant Mortality in India,”
Greenstone et al, Massachusetts Institute of Technology, Feb 2011.
4. “Impact Evaluation of a Large-Scale Rural Sanitation Project in Indonesia,” Cameron
et.al, The World Bank, Water and Sanitation Program, Feb 2013.
5. “India’s Toilet Race Failing as Villages Don’t Use Them,” Mehrotra, Kartikay,
Bloomberg Business, Aug 4, 2014. Web.
6. “India’s Toilet Race Failing as Villages Don’t Use Them,” Mehrotra, Kartikay, Bloomberg
Business, Aug 4, 2014. Web.
7. “National Rural Drinking Water Programme”, Government of India,
http://indiawater.gov.in , Web.
8. “PM Modi fulfills promise of 80 lakh toilets but not many takers in rural India,”
Sharma, Neetu: India Today, Aug 14, 2015. Web.
9. “Restructuring of the Nirmal Bharat Abhiyan into Swachh Bharat Mission,” Press
Information Bureau, Government of India, Sep 24 2014.
10. “Swachh Bharat Mission”, Government of India, http://tsc.gov.in , Web.

Research Paper

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (8)

Similar to Research Paper

Similar to Research Paper (20)

Research Paper