UEA_MSc_IE_2016_Dissertation

i
Can Improved Pit Latrines Reduce
Childhood Diarrhoea?
New Evidence from Bangladesh
Khandaker Aminul Islam
A dissertation submitted to the School of International Development of the
University of East Anglia in Part-fulfilment of the requirement for the degree of
Master of Science
September 2016

ii
Table of Contents
List of Acronyms ..................................................................................................................... iv
List of Tables and Figures......................................................................................................... v
Abstract.................................................................................................................................... vi
Acknowledgements.................................................................................................................vii
Introduction............................................................................................................................viii
1. Literature Review ........................................................................................................... 1
1.1 Effects of sanitation interventions on childhood diarrhoea .............................................. 1
1.2 Access to sanitation facilities and diarrhoea prevalence in Bangladesh........................... 5
1.3 Conceptual linkages between sanitation intervention and diarrhoeal diseases................. 7
2. Empirical Framework .................................................................................................. 10
2.1 Data source...................................................................................................................... 10
2.2 Propensity score method (PSM) for causal inference..................................................... 11
2.2.1 Estimating the propensity scores....................................................................................14
2.2.2 Matching and estimating the average treatment effect................................................15
2.2.3 Checking the matching quality.......................................................................................17
2.3 Limitations...................................................................................................................... 17
3. Results............................................................................................................................ 19
3.1 Description of sample ..................................................................................................... 19
3.2 Descriptive statistics of covariates and propensity score estimation.............................. 20
3.3 Average treatment effect................................................................................................. 24
3.4 Heterogeneous treatment effect ...................................................................................... 25
3.5 Robustness checks .......................................................................................................... 25
3.5.1 Balancing test....................................................................................................................25
3.5.2 Standardised bias, joint significance and pseudo-R2 ..................................................26
3.5.3 Hidden bias and sensitivity analysis ..............................................................................27
4. Discussion of results...................................................................................................... 29
4.1 Impact of improved pit latrine (IPL) on childhood diarrhoea......................................... 29
4.2 Robustness of the PSM impact estimates ....................................................................... 30
4.3 Linking the impact of IPL to the conceptual model of barriers to disease transmission 32
4.4 Policy and research recommendations.............................................................................. 33

iii
Conclusion .............................................................................................................................. 35
Bibliography.......................................................................................................................... 37
Appendix 1: Pre-matching descriptive statistics of stratified samples ................................... 46
Appendix 2: Histogram of propensity scores for stratified samples....................................... 56
Appendix 3: Use of common support for different matching specifications.......................... 58
Appendix 4: Heterogeneous treatment effects of IPL on childhood diarrhoea ...................... 59
Appendix 5: Post-matching covariate balance (individual t-test) for full sample and stratified
samples.................................................................................................................................... 60
Appendix 6: Summary statistics of matching quality for stratified samples .......................... 70
Appendix 7: Sensitivity analysis for heterogeneous effects ................................................... 71

iv
List of Acronyms
ATT Average Treatment Effect on the Treated
BBS Bangladesh Bureau of Statistics
BDHS Bangladesh Demographic Health Survey
CIA Conditional Independence Assumption
CS Common Support
DHS Demographic Health Survey
EA Enumeration Areas
GoB Government of Bangaldesh
KM Kernel Matching
IPL Improved Pit Latrine
LR X2
Likelihood Ratio Chi-square Statistics
MDG Millennium Development Goal
NGO Non-Governmental Organization
NIPORT National Institute of Population Research and Training
NN5 Five Nearest Neighbours
NNM Nearest Neighbour Matching
POU Point of Use
PRSP Poverty Reduction Strategy Paper
PS Propensity Score
PSM Propensity Score Matching
QED Quasi-Experimental Design
RCT Randomized Control Trial
SDG Sustainable Development Goals
TSC Total Sanitation Campaign
UK The United Kingdom
UNICEF United Nations International Children’s Emergency Fund
WHO World Health Organization
WHO/UNICEF-JMP WHO/UNICEF Joint Monitoring Programme
WSH Water, Sanitation and Hygiene

v
List of Tables and Figures
Table 1: Coverage of IPL by location, child age and diarrhoea infection
Table 2 PSM estimates of Average Treatment Effect on the Treated (ATT) for probability of
diarrhoea incidence (full sample)
Table 3: Summary statistics for matching quality for full sample
Table 4: Sensitivity analysis for sample: Wilcoxon’s signed rank test
Figure 1: Conceptual model of barriers to disease transmission resulting from Water,
Sanitation and Hygiene (WSH) programme
Figure 2: Map of Bangladesh showing location of 7 divisions.
Figure 3: Kernel density plot of propensity score (PS) distribution by treatment and control
group
Figure 4: Percentile distribution of PS before matching
Figure 5: Histogram of PS by treatment group after matching
Figure 6: Distribution of PS by treated and untreated group after matching

vi
Abstract
Diarrhoea is the second leading cause of mortality for children under the age of five years.
There is strong consensus that improved sanitation facilities act as a barrier to the
transmission of diarrhoeal diseases. Achieving access to adequate and equitable sanitation for
all by 2030 is a target of the Sustainable Development Goals (SDG). Existing impact
evidence of improved sanitation on childhood diarrhoea remains controversial because of the
methodological challenges in measuring it. The improved pit latrine (IPL) is one of the most
common types of toilet facilities in Bangladesh. Impact evidence of this toilet facility on
child diarrhoea has not been updated since 1990. This dissertation examines the causal-effect
of IPL on diarrhoea among children under five using the 2014 Bangladesh Demographic
Health Survey (BDHS) dataset. Employing Propensity Score Matching (PSM) techniques,
this dissertation finds that IPL significantly reduced the risk of diarrhoea infection among
children by 1.3 percentage points. There is considerable heterogeneity in the effects of IPL
and no statistically significant treatment effect for children in urban areas and children older
than 23 months were found. The findings from this dissertation will contribute to the
development of a long-term sanitation strategy for Bangladesh within the framework of the
Poverty Reduction Strategy Paper (PRSP). For a short-term development initiative, the
findings can be used in sanitation campaigns to avert childhood diarrhoea. Sensitivity
analysis suggests the estimated effects from the nearest neighbour matching are not bias-free
because of the influence of confounding variables. Thus, further research using a pretest and
posttest longitudinal design is recommended to produce more robust impact estimates for
IPL.

vii
Acknowledgements
I am indebted to the UK Foreign and Commonwealth Office for supporting my education at
University of East Anglia in the form of a Chevening scholarship, administered in 2015-16.
This dissertation was a great experience of learning about and reflecting on impact evaluation
discourse. Huge gratitude goes to my supervisor, Dr. Bereket Kebede, for his invaluable
direction and time for discussion. Without his technical guidance and encouragement, it
would have been a difficult task to complete this paper. Thanks also to Dr. Maren
Duvenduck for her initial input.
Finally, thanks also to my wife, Tania Sultana, for her magnificent cooperation and sacrifice
to our family during my study.

viii
Introduction
Diarrhoea is the second leading cause of death in children under five. Each year, diarrhoea
kills approximately 0.8 million children under five (WHO, 2013). Childhood diarrhoea also
causes long-term detrimental effects on physical growth and cognitive function (Kosek et al.,
2003). Repeated episodes of diarrhoea can lead to nutritional problems, lowering of disease
resistance capacity and increased mortality (Islam & Karim, 1992). Children in developing
countries suffer the most from diarrhoeal infections and around 78% of all diarrhoea related
deaths occur in Africa and South-East Asia (Boshi-Pinto et al., 2008).
Unsafe and inadequate sanitation facilities and hygiene increase the transmission of
diarrhoeal diseases (WHO, 2015). Statistics suggest that 3.1% of all deaths are attributable
to unsafe water, sanitation and hygiene (WHO, 2002). Yet improved sanitation remains out
of reach for many people around the world. According to the Millennium Development Goal
(MDG) assessment, around two-fifths (38%) of the world’s population do not have access to
improved sanitation facilities (UNICEF &WHO, 2015). Numerically, this translates as 2.4
billion people using unimproved sanitation, including 946 million people who practice open
defecation (United Nations, 2015). Feachem (1984) argues that hygienic practices and
hygienic facilities such as improved toilet facilities can stop the transmission of diarrhoea
pathogens. Globally, the importance of sanitation facilities to reducing child morbidity and
mortality is widely recognised. The United Nations has included “the universal and equitable
access to safe and affordable drinking water, sanitation and hygiene for all by 2030” as a
target of SDG (WHO/UNICEF-JMP, 2015).
Given the current situation, developing countries continue to invest resources in their
sanitation sector to reduce child mortality and morbidity. To date, development researchers
have generated invaluable knowledge on the effects of improved sanitation on childhood
diarrhoea; however, the evidence is inconsistent, and continues to stimulate debates around
intervention design, evaluation approaches and measurement techniques. Specifically, the
broad definition of improved sanitation developed by WHO & UNICEF (2000) seems to
pose significant methodological challenges for impact evaluation. The definition covers a
wide range of toilet facilities that are dissimilar in terms of functions and usage in different

ix
contexts. As a result, inconsistency in impact estimates may also be associated with the
composite definition. Blum & Feachem (1983) in a meta-analysis of 53 health impact
studies found none completely free of methodological shortcomings. Because of the
methodological challenges, impact evaluation outcomes of sanitation interventions often
suffer from understated or overstated estimates.
The use of IPL, one of the toilet facilities within the definition of improved sanitation, is
widespread in developing countries. More than one third (35%) of people in Bangladesh use
IPL (NIPORT et al., 2016), for example. However, evidence of its effectiveness on diarrhoea
has not been updated since 1990. Using the latest Bangladesh Demographic Health Survey
(BDHS) dataset and PSM technique, this dissertation aims to contribute to the existing
impact knowledge of improved sanitation by examining the causal-effects of IPL on
childhood diarrhoea. This focus has emerged from the fact that diarrhoeal diseases still claim
the lives of many children under five in Bangladesh, despite an improvement in access to
safe water and sanitation facilities in the last two decades. It is estimated that 6% of all deaths
of children under five in Bangladesh is attributable to diarrhoeal disease (WHO, 2015).
However, lack of impact evidence for specific sanitation interventions hampers informed
decision-making about appropriate interventions and resource investment. This dissertation
will inform the Government of Bangladesh (GoB), and the development actors in Bangladesh
and beyond, about the impact of IPL in reducing childhood diarrhoea. The main research
question of this dissertation is: “Whether and to what extent does IPL influence diarrhoea
among children under five?”
This dissertation is organised as follows. Chapter 1 presents a literature review that firstly
focuses on the existing knowledge about the impact of sanitation interventions in the
developing world, before giving an overview of the sanitation facility and childhood
diarrhoea situation in Bangladesh. This chapter introduces a conceptual model of how WSH
programmes stop the transmission of diarrhoea pathogens to the human body.
Chapter 2 discusses the methodology of this dissertation. It begins with an overview of data
sources, which includes the sampling procedure, definitions of the health outcome and the

x
treatment variables. The chapter then describes the PSM estimation method, including its
applicability for this evaluation and finally, it explains the limitations of this research design.
The results from the PSM analysis are presented in Chapter 3. Chapter 4 discusses the
results, including triangulation with existing evidence, reflections on the conceptual model
and finally, some policy recommendations.

1
1. Literature Review
This chapter reviews the relevant literature on the impact of sanitation on childhood
diarrhoea. First, the chapter presents existing evidence about the impacts of sanitation
interventions in the developing world. This is followed by a section describing the sanitation
facilities and the prevalence of childhood diarrhoea in Bangladesh. Finally, this chapter
reflects on the linkages between sanitation and child health, using Pruess et al.’s conceptual
model (2002, as cited in Waddington & Snilstveit, 2009a).
1.1 Effects of sanitation interventions on childhood diarrhoea
To date, a significant number of impact evaluation literature has investigated the effects of
sanitation in developing countries. Most studies focus on health outcomes, particularly
diarrhoeal risk among children. The findings regarding the effect of improved sanitation on
childhood diarrhoea is inconsistent, and vary between the measurement approaches, between
the type of toilet facilities used, and between shared and non-shared toilet facilities.
The effects of improved sanitation on child diarrhoea from randomised control trials (RCT)
are mixed. An RCT using a 7-day recall period between 2009 and 2011 in 80 rural villages in
Modhya Pradesh, India did not find any effect of sanitation on child diarrhoea (Patil et al.,
2014), while another RCT using the same recall period between May 2010 and December
2013 in 100 rural villages in Odisha, India showed a lower prevalence of diarrhoea (8.8%) in
children under five in the intervention group compared to 9.1% in the control group (Clasen
et al., 2014). Clasen et al. (2007), however, argued that greater protective effects are
generally reported from RCT. Furthermore, large scale RCTs to examine the causal effects of
sanitation facilities are deemed to be unfeasible given the operational complexities involved
and inadequate financial resources (Schimdt, 2014).
Given these constraints, Briscoe et al. (1985, as cited in Schimdt, 2014) claimed that case
control seems to be the most cost-effective way to evaluate the health impact of sanitation.
For example, Daniels et al. (1990) in rural Lesotho, and Aziz et al. (1990) in rural
Bangladesh using case-controlled design found 24% fewer episodes of diarrhoea among
children below five in households with IPL compared to children in households without IPL

2
(odds ratio: 0.76, 95% confidence interval 0.58-1.01). Similarly, Baltazar et al. (1988) in the
Philippines estimated a 20% reduction of childhood diarrhoea in households with IPL. These
three estimates seem almost consistent.
The effect of improved sanitation on childhood diarrhoea using Quasi Experimental Design
(QED) has shown some consistency; however, this seems to be lower than the effects from
RCTs. Bose (2009), applying PSM on the 2001 and 2006 Nepal Demographic Health Survey
(DHS) data with a 14-day recall period, found a 5% lower incidence of diarrhoea (ATT-
0.052, p<0.05) in children below five with improved sanitation than those children without.
Similarly, Kumar & Vollmer (2011), applying multiple methods such as PSM, linear
probability model and weighted least square regression, estimated the causal effect of
improved sanitation on child diarrhoea in India and found 2.2%, 0.8% and 1.0% points lower
incidence in the treated group than in the control group. However, Begum et al. (2013), using
only the PSM method on the 1996 and 2007 BDHS data, found a 0.8% points reduction of
diarrhoea among children with improved sanitation but this estimate was statistically
insignificant. Other than Bose (2009) and Kumar & Vollmer (2011), no other evaluators who
applied PSM, carried out sensitivity analysis to examine the unobserved heterogeneity in the
estimates.
Non-experimental studies also show inconsistent effects of improved sanitation on childhood
diarrhoea. Applying a theoretical model of health outcomes, an analysis using the 2007-2008
district level household survey data of India claimed a 47% percent reduction in diarrhoea
prevalence in children in households with improved sanitation in a village fully covered with
sanitation than in children in households without improved sanitation in a village not
covered; one fourth of this benefit was attributed to the direct benefit of sanitation while the
rest was associated with positive external effects (Andres et al., 2014). Furthermore, analysis
using the hierarchal logit model on the 2006 Living Standard Measurement Survey data of
Guatemala showed an average of 20% lower incidence of diarrhoea in households connected
to a sewerage system (Vasquez & Aksan, 2015). A multilevel regression analysis on the
2007 and 2012 Indonesian DHS datasets, on the contrary, showed no significant effect of an
improved toilet on diarrhoea (Komarulzaman et al., 2016).

3
Some studies have revealed that the type of toilet and sharing facilities influence the
prevalence of child diarrhoea. A cross-sectional survey between June 2003 and August 2003
in Ghana, by Boadi & Kuitunen (2005), covering a random sample of 489 children below the
age of six showed higher reductions in diarrhoea associated with flush toilets and pit latrines.
Another cross-sectional study applying simple binomial regression to the third round of
India’s National Family Health Survey (2005-2006) showed that 6% of children in
households with flush toilet suffered from diarrhoea compared to 11.2% in the households
with other types of toilets (Singh & Singh, 2014).
Sharing toilet facilities with other households also influences diarrhoea incidences among
children. To examine the effect of shared toilet facilities, Baker et al. (2016), applying
matched case-control methods in Africa (Kenya and Mali) and South Asia (Bangladesh and
Pakistan), found that sharing a sanitation facility with just one to two other households can
increase the risk of diarrhoea in young children, compared to the households using a private
facility. Households with private sanitation and those sharing a sanitation facility with 1-2
other households faced moderate-to-severe incidences in Kenya, Mali, Bangladesh and
Pakistan sites (ibid). Similarly, Boadi & Kuitunen (2005) reported that households who
share a toilet facility with more than five other households are more likely to have a high
incidence of childhood diarrhoea (X2
= 41.73, 4df, p<0.0001).
A number of meta analyses also showed inconsistent results regarding the effects of
improved sanitation on childhood diarrhoea that are likely to be attributable to the application
of different measurement approaches, quality of data and inclusion criteria. A meta-study by
Fuller et al. (2015) showed a mixed effect of improved sanitation on diarrhoea prevalence
across surveys. Among 217 DHS surveys, 41 surveys showed improved sanitation as having
a protective effect, 168 showed no statistically significant effect and the remaining 7
indicated a statistically significant negative effect. By contrast, some systemic evaluations
produced strong positive results regarding the effects of sanitation on childhood diarrhoea.
Specifically, Speich et al. (2016) carried out a systemic review and included odd ratios of 54
studies for meta-analysis, finding sanitation facilities significantly associated with lower
likelihoods of infection of intestinal pathogens. Furthermore, some systemic reviews suggest

4
that the size of the estimates might vary according to the robustness of the studies. For
example, Esrey et al. (1991), using a synthesis of 11 studies, calculated a 22% reduction in
child diarrhoea in households with improved sanitation; however, they estimated a 36%
reduction based on five rigorous studies.
Studies also show mixed findings regarding the heterogeneous treatment effects of improved
sanitation according to children’s age group, wealth and location. More specifically, Khanna
et al. (2008) found that wealth had no significant role in influencing childhood diarrhoea
among households with improved sanitation, while Kumar & Vollmer (2011) found a
statistically significant effect of 2.5% and 0.8% points lower incidence for the wealthiest and
middle socio-economic groups respectively. However, there was a statistically insignificant
treatment effect on the lowest socio-economic groups, suggesting the intervention caused
diarrhoea. Again, Bose (2009) claimed a greater impact of improved sanitation for
households with children below 24 months by over 11% points, while Kumar & Vollmer
(2011) found no difference in effect between children below 24 months and those between 24
months and 59 months old. There is very limited evidence regarding the rural-urban
disaggregated impact. Improved sanitation significantly reduces (ATT -0.034, p<0.05)
childhood diarrhoea in rural areas while no positive effect was found in urban areas because
of the poor balance in matched samples over the unmatched samples (Roushdi et al., 2103).
Lastly, most studies used the broad definition of ‘improved sanitation’. This may have
generated some methodological challenges in constructing a comparable treatment and
control group, particularly for QED. The latest definition of improved sanitation
(WHO/UNICEF-JMP, 2015) is also broad in that it encompasses a wide range of basic
sanitation facilities (flush/pour flash to piped sewer system, septic tank or pit latrine,
ventilated improved pit latrine, composting toilet or pit latrine with a slab not shared with
other households). The application of this combined definition can hamper the homogeneity
between treated and control groups, thereby underestimating or overestimating the effect of
the interventions.
This section concludes that there is limited consistent evidence on the impact of improved
sanitation on childhood diarrhoea. This knowledge gap has resulted mainly from the use of

5
different estimation approaches. Although RCT is expected to generate a precise estimate, it
is not widely used in impact evaluations of sanitation interventions. QED, particularly PSM,
is the most common approach in measuring causal effects of sanitation interventions.
However, the accuracy of estimates is limited by cross-sectional datasets, and use of different
matching approaches. Some systematic reviews suggest that methodological robustness is
essential to produce precise estimates. Besides, use of different recall periods, composite
definition of improved sanitation and type of toilet facility may influence the effects and
results of sanitation intervention on childhood diarrhoea.
1.2Access to sanitation facilities and diarrhoea prevalence in Bangladesh
Bangladesh is one of most densely populated countries in the world, with 1063 people per
square kilometer (BBS, 2015). Further, ongoing rural to urban migration continues to
generate demand for access to sanitation facilities in urban areas. Currently, two-thirds (66%)
of the total population of Bangladesh lives in rural areas (http://www.worldbank.org/data).
Although according to the MDG assessment, Bangladesh has made good progress by
reducing to about one-third (38%) of the population living without improved toilet facilities
(UNICEF & WHO, 2015), there is still huge room for improvement in sanitation facilities.
Access to sanitation
More than half of Bangladesh’s population (52%) use unimproved toilet facilities (NIPORT
et al., 2016). The use of improved sanitation facilities is strongly correlated with wealth
(BBS & UNICEF, 2015). Lack of education, awareness of the benefits of improved toilet
facilities and the rapid expansion of slums and settlements in divisional cities also hinder
access to improved sanitation.
In Bangladesh, the use of improved toilet facilities has doubled in the last decade and more
than a third of households (36%) across the country use IPL (NIPORT et al., 2005 & 2016).
Almost a quarter of households (24%) rely on shared toilet facilities, though shared toilet
facilities in urban areas (33%) are more prevalent than in rural (20%) areas (NIPORT et al.,
2016). Sanitation coverage in slum and squatter settlements in divisional cities is poor with
between 2-15 households sharing one latrine (www.wateraid.org). The urban-rural difference

6
in shared toilet facilities is likely to be linked with wealth disparity between people in rural
and urban areas.
Overall, the use of unimproved toilet facilities in Bangladesh has reduced by more than half
during the last ten years (NIPORT et al, 2005 & 2016). The type of toilet facilities however
varies between rural and urban areas. Currently, almost a quarter of households (23.7%) in
urban areas use flush or pour flush toilets, while in rural areas, only 5.4 % of households use
those facilities (NIPORT et al., 2016). Furthermore, the use of an IPL in rural (27.3%) areas
is almost double the use of the same latrine in urban (13.8%) areas (ibid). In the last decade,
open defecation and reliance on a hanging toilet/ bush has decreased in rural areas from 45%
to 8.6% (ibid).
Diarrhoea prevalence
Diarrhoea is one of the main causes of child mortality in Bangladesh. Evidence suggests that
13.3% of deaths in children below five are associated with confirmed symptoms of diarrhoea
and a further 5.3%, associated with possible symptoms (Baqui et al., 2001). In the last
decade, prevalence of diarrhoea among children below five has decreased from 7.5% to 5%
(NIPORT et al., 2005 & 2016). During this time, prevalence of diarrhoea was highest among
children aged 6-23 months (ibid). Though the relationships between mother’s education,
wealth and diarrhoea prevalence is not linear, diarrhoea prevalence is less among children
whose mothers have completed secondary or higher education, and lowest among children
living in households in the fourth wealth quintile (NIPORT et. al, 2016). There is no
difference in diarrhoea prevalence between rural and urban areas, and the difference between
male and female children is also minimal.
In summary, the use of improved toilet facilities in Bangladesh has significantly increased in
the last decade, with IPL still the dominant type of sanitation facility. There is no updated
empirical evidence regarding the effect of IPL on childhood diarrhoea.
Although a substantial amount of knowledge has been generated on the impact of improved
sanitation, controversies about impact estimates and methodological debates continue.
Besides, less attention has been paid to quantifying the impact of specific toilet facilities.
Measuring heterogeneous treatment effects is useful to target the population with particular

7
types of intervention. This review has also found that there is no concrete evidence of the
heterogeneous treatment effect of improved sanitation by age group or location. Having
considered the gaps in existing impact evidence of sanitation, particularly in the context of
Bangladesh, this dissertation addresses the following research questions: “Does IPL have an
effect on childhood diarrhoea and if so, to what extent?”, “Does the effect of IPL vary
between children below 24 months and those between 24 and 59 months?” and “Is the effect
of IPL on children in rural areas different from the effect on children in urban areas?”
1.3 Conceptual linkages between sanitation intervention and diarrhoeal diseases
The main objectives of any sanitation intervention are to improve living conditions and
health by reducing incidences of diseases such as diarrhoea. To understand how a sanitation
intervention stops the transmission of diarrhoeal diseases, a theory-based impact evaluation
study would be required but this research, because it uses a secondary quantitative dataset,
cannot explain the process of causal relation between sanitation and diarrhoea. Wholey
(1983, as cited in Weiss, 1997) underlined that evaluators should analyse the logical linkages
between interventions and expected outcomes to examine whether there is a reasonable
likelihood that goals will be achieved. Pruss et al. (2002, as cited in Waddington & Snilstveit
(2009a) has modelled (Figure 1) how transmission of disease pathogen to human body can be
stopped by WSH interventions.
Figure 1 explains that improved sanitation such as sanitation and hygiene interventions
intend to break the cycle of disease transmission from faeces to the environment in the first
round, while water and hygiene interventions seek to interrupt second round transmission
routes (Waddington & Snilstveit, 2009a). The mechanism of breaking disease transmission
reflects the logical sequence of WSH interventions that contribute to protecting children from
diarrhoea infection. These linkages between interventions reflect the theories of change
linked to the programme activities, intermediate outcomes and ultimate programme goals
(White, 2009; Weiss, 1997 & Wholey, 1987). Gaining better understanding of theories of
change help unpack the ‘black box’ of pretest and posttest evaluation studies (Bamberger et
al., 2012).
This conceptual model explains that integrated WSH programmes are likely to have a

8
significant impact on childhood diarrhoea. Waddington & Snilstveit (2009a) argued that
multiple interventions encompassing water, sanitation and / or hygiene would have
complementary effects. In reality, there are substantial operational differences between
hygiene interventions and water-sanitation facilities: the two are usually the responsibility of
separate ministries and personnel in developing countries (Feachem, 1984).
Furthermore, existing literature (such as Esrey et al., 1991, Fewtrell et al., 2005, and
Waddington et al., 2009) does not show any promising effects of combined interventions on
childhood diarrhoea. The DHS dataset does not contain required treatment variables as per
the conceptual model; thus it is unlikely to be feasible to examine the effectiveness of
multiple interventions. Having said that, this dissertation attempts to examine the effect of
IPL within the given conceptual model of barriers to disease transmission.

9
Figure 1: Conceptual model of barriers to disease transmission resulting from WSH
programme. Orange arrows represent routes along which intervention reduces risk of
pathogen transmission.
Sanitation
Sanitation
Ground/
surface
water
Source
treatment
Drinking
water
Hygiene
POU
Fingers
Faeces Health
status
Point of Use (POU)
Fields
& flies
Food
Hygiene

10
2. Empirical Framework
Having positioned this dissertation within the literature on the effect of sanitation
interventions, this section describes the method applied in measuring the effects of
IPL on childhood diarrhoea. First, the source of data, and its quality are discussed. Second,
PSM method as an impact assessment tool for this dissertation is outlined. Finally, the
chapter presents the limitations of the research design.
2.1 Data source
This research used the 2014 BDHS, a nationally representative and standardised cross-
sectional dataset. The BDHS survey followed a two-stage stratified sampling procedure, first
selecting 600 Enumeration Areas (EA) with
probability proportional to the EA size, with 207
EAs from urban areas and 393 from rural areas.
A complete list of all households in the EAs was
then constructed to develop a sampling frame
for the second stage of the process. In the
second stage, 30 households on average were
systematically selected from each EA to
generate statistically reliable estimates of key
demographic and health variables for the
country as a whole, and for urban and rural areas
separately, and also for each of the 7 divisions
(Figure 2). From the 17,989 sampled
households, 17,863 married women aged 15-
49 from 17,300 households were interviewed
(NIPORT et al., 2016).
The 2014 BDHS included three types of questionnaires: a household questionnaire, a
women’s questionnaire and a community questionnaire. The household questionnaire
captured information about the dwelling unit, such as the source of water, type of toilet
facilities, and materials used to construct the floor, roof and walls, while the women’s
Figure 2: Map of Bangladesh showing location of
7 divisions. Source: NIPORT et al., 2016

11
questionnaire covered a wide range of information such as household background
characteristics, immunization and illness of the children below five. Health related
information of 7,500 children below five was collected during the women’s individual
interviews. Although the DHS dataset is widely used for impact evaluation purposes, it is not
designed for impact evaluation of sanitation. As a result, some important variables linked to
the IPL and diarrhoea may have been missed. The DHS survey questionnaire asks
respondents about the toilet facility that they ‘usually use’ within their households. Members
of the households may thus have access to other toilet facilities not included in the survey
(Fink et al., 2011: 1197).
This dissertation uses the household data file merged with the children’s data file to include
necessary control variables for the estimation. The children below five (7,493) in the
households are chosen as the sample for the analysis. This research considered those
households as the treatment group that uses an ‘IPL’ i.e. a household owned slab or
ventilated pit latrine not shared with other households, while the households without an IPL
were considered the control group. The definition of ‘IPL’ is based on the WHO/ UNICEF-
JMP’s (2015) latest criteria for improved water and sanitation.
Childhood diarrhoea, on which the effects of the treatment will be estimated, has been
selected as the dependent variable. The 2014 BDHS survey using a two-week recall period
collected incidence of diarrhoea infection among children below five (NIPORT et al., 2016).
2.2 Propensity score method (PSM) for causal inference
Impact evaluation intends to determine whether and to what extent the changes in outcome
are attributable to the programme interventions rather than other factors (Khandker et al.,
2010:7). To evaluate the attribution of an intervention, a factual, which in this case is “what
has happened to the incidence of childhood diarrhoea in the households with IPL”, has to be
compared to a counterfactual “what would have happened to childhood diarrhoea in those
households without IPL.” Identifying an appropriate counterfactual is the core challenge of
impact evaluation (Baker, 2000) because a household or an individual at a given point in time
cannot possess two concurrent identities: participant and non-participant.

12
Having considered the underlying issues of impact evaluation and the DHS cross-sectional
dataset, the QED is the most suitable evaluation design for this dissertation. Among the
QED, PSM, the second-best approach to experimental design (Baker, 2006:6) was chosen
because inferring causality from the observational data, according to Rubin’s causal model
(1974), requires constructing a counterfactual. PSM addresses the missing counterfactual by
constructing a statistical comparison group through modelling the probability of participation
in the programme based on observed characteristics unaffected by the programme. However,
conditioning on all covariates in relation to programme participation, as discussed in
Caliendo & Kopeinig (2005), is limited in a situation of high dimensional vector, X. To
minimize the dimensionality problem, Rosenbaum & Rubin (1983:44) suggest using a
balancing/propensity score (PS) and show that matching propensity scores P(X) is as
effective as matching covariates X. The probability of participation is denoted as PS=P(X) =
Pr (T=1|X), where T refers to the assignment to the treatment conditional on a set of
observed characteristics ‘X’ (Khandker et. al., 2010: 55).
Based on the probability of participation model, the PS for the treatment and control group is
estimated and then the treatment group (participants) is matched with the control group (non-
participants) based on similar PS. The mean difference in outcomes across the two groups is
estimated as the impact of the intervention. However, the preciseness of PSM estimates relies
to a lesser extent on two main assumptions: Conditional Independence Assumption (CIA)
and Common Support (CS) condition. First, CIA, as described in Khandker et al (2010),
posits “a set of observable covariates X that are not affected by treatment, potential outcomes
Y are independent of treatment assignments T”. If Yi T
denotes outcomes for participants and
YiC
outcomes for non-participants, the CIA is written as,
(Yi
T
, Yi
C)
⊥ Ti |Xi.
The CIA is also called unconfoundedness (Rosenbum & Rubin, 1983). This assumption, as
argued by Khandker et al. (2010), implies that participation in the programme entirely
depends on observable characteristics. However, Bryson et al. (2002) say “where data do not
contain all the variables influencing both participation and the outcome, CIA is violated since
the programme effect will be accounted for in part by information which is not available to

13
the evaluator”.
Although CIA cannot be examined directly, working with a good dataset allows the evaluator
to include as many control variables as possible that might influence programme
participation (Khandker et al., 2010). However, Esrey & Habicht (1985) argue that it is
impossible to identify and measure all confounding factors that could affect the intervention
and comparison groups; thus there is always the possibility in observational studies that the
reported effects are not due to the intervention but to other unobservable factors. For
example, the DHS dataset does not include data on whether households clean the pits.
Cleaning of pits is likely to be correlated to IPL and transmission of diarrhoea. This implies
that unobservable factors may underestimate or overestimate the impact of IPL.
The CIA, whether it holds or not, can be indirectly examined by carrying out sensitivity
analysis on the PSM estimates (Rosenbaum, 2002). Using sensitivity analysis, the level of
bias on the estimates can be determined. This type of analysis attempts to answer the
question of how sensitive the estimates are to the hidden bias (Guo & Fraser, 2015: 358).
Therefore, this research carried out sensitivity analysis to examine the selection on
confounding variables. The usefulness of sensitivity analysis, however, is not beyond
criticism. Robins (2002) proved that Rosenbaum’s sensitivity parameter i.e. gamma (Γ) is
applicable for the criteria of a ‘paradoxical measure’ and further claimed that sensitivity
analysis based on a ‘paradoxical measure of hidden bias’ may be scientifically impractical.
Second, besides CIA, a CS or overlap condition of treated and untreated group has to hold to
produce PSM results. This overlap assumption is symbolized as:
0 < P (Ti = 1|Xi) < 1
This rules out the phenomenon of absolute predictability of treatment (T) given covariates
(X) (Caliendo & Kopeinig, 2005). Furthermore, it explains that both treated and untreated
groups with propensity score between 0 - 1 have equal probability of being both participants
and non-participants (Heckman et al., 1999:55). This CS assumption is identified as ‘strong
ignorability’ by Rosenbum and Rubin (1983), while, Khandker et al. (2010) suggest that the
CS assumption can be relaxed to P (Ti=1|Xi) <1 for estimating the ATT.

14
Because of the strong ignorability condition, observations outside the CS region can be
discarded to ensure better comparability (Heckman et al., 1997a). Dropping samples out of
the CS region, especially for a smaller sample size, results in fewer matches, thereby
increasing bias in the estimation. However, the DHS is a reasonably large dataset, and it is
unlikely that removing some observations outside the CS region will lead to any significant
bias.
Given that CIA and CS assumptions hold across the treated and untreated groups, the PSM
estimator for ATT, according to Caliendo & Kopeinig (2005), can be specified as:
ATT = E {E[Y T
| T = 1, P(X)] – E[Y C
| T = 0, P(X)]}
2.2.1 Estimating the propensity scores
To estimate the PS, first, a model is chosen for the estimation, and then the variables to be
included in the model are selected. For binary treatment variables, the probability of
participation versus non-participation produces similar results irrespective of the application
of the logit or probit model (Caliendo & Kopeinig, 2005), but there is no concrete suggestion
regarding which functional form is suitable. This dissertation applied binomial logit to
estimate PS for matching the treatment and control groups. It used five sets of PS estimation
models (all children below five, children below 24 months, children between 24 months and
59 months, children in rural areas and children in urban areas) to measure aggregated and
disaggregated treatment effects, as suggested by Dehejia and Wahba (2002).
The reason for selecting below 24 months as the cut-off for age disaggregated analysis is that
children below this age are more susceptible to diarrhoeal infection (Bado et al., 2016;
Budhathoki et al., 2016). Besides, the use of IPL in Bangladesh, as mentioned before, is
much higher in rural areas than in urban areas and there is lack of new evidence on its effect
on childhood diarrhoea. The binary outcome for IPL for all five models takes the value of
one if the household has access to IPL and zero otherwise.
The selection of observable variables used to construct the PS model can make a substantial
difference to the efficiency of the estimator (Smith & Todd, 2005:333). The selection of a set
of observable variables has to satisfy the CIA, but there is no adequate guidance on how to

15
choose a set of variables that determine probability of participation in a programme
(Khandker et al., 2010). Exclusion of any important variables from the programme
participation model can seriously increase bias in resulting estimates (Heckman et al., 1997).
However, adding additional conditioning variables may also intensify a CS problem (Smith
& Todd, 2005) or may over-parameterise the specifications (Bryson et al., 2002). As such,
only variables that are unaffected by participation should be included in the propensity score
estimation model (Caliendo & Kopeinig, 2005). Further to this, Smith & Todd (2005) and
Sianesi (2004) suggest following economic theory and evidence from previous research in
identifying variables for the PS estimation model.
Following the above advice and evidence from previous research (such as Begum et al.,
2013; Bose, 2009; Khanna, 2008), this study selected a wide range of control variables from
the dataset such as age of household head, household size, respondent’s education and wealth
index. It converted wealth, division, education, occupation, wall materials and cooking
materials into “categorical dummies” to strengthen the logit models. Besides, a polynomial
variable of the age of household heads was included to improve the explanatory power of the
model.
The research examines the common support area by plotting the kernel density and the
histograms of the PS for the treatment and control groups. The PS estimation equation is not
a model for independent variables; therefore, the estimation results such as t-statistics and the
adjusted R2
are not very meaningful (Khandker et al., 2010).
2.2.2 Matching and estimating the average treatment effect
After estimating the PS, a number of matching algorithms are used for matching treatment
and comparison groups. This research applied both Nearest Neighbour Matching (NNM) and
Kernel matching (KM) algorithms, the most common matching algorithms for the PSM
method. The use of different matching algorithms enables results to be compared from one
matching to another, which is an indication of the ATT measure’s robustness (Khandker et
al., 2010). However, both matching algorithms have advantages and disadvantages.

16
NNM is the most straightforward algorithm. It allows an observation in the comparison
group to be matched with an observation in the treatment group with the closest propensity
score (Caliendo & Kopeinig, 2005). One can select ‘n’ nearest neighbours to carry out
matching and matching with five nearest neighbours (NN5) is commonly used (Khandker et
al., 2010). Matching can be carried out with and without replacement. For matching with
replacement, an untreated observation can be used more than once, but it is not applicable for
matching without replacement (Caliendo & Kopeinig, 2005).
Furthermore, NNM may result in poor matches if the difference in PS for a treated
observation and its nearest untreated neighbour is large. This problem can be solved by
imposing a tolerance (caliper) on the largest PS distance (Khandker et al., 2010). Moreover,
a large number of untreated participants is likely to be dropped with the use of a caliper,
which increases the sampling bias (ibid). Given the smaller number of treated observations
compared to the untreated, NNM with replacement and caliper was used in this research.
KM, a non-parametric matching estimator, uses weighted averages of all observations in the
control group to form the counterfactual outcome (Caliendo & Kopeinig, 2005), whereas
NNM only uses the closest neighbours within the selected caliper. Therefore, the major
advantage of KM is lower variance because it uses more information (Heckman et al.,
1997a). However, a limitation of the KM approach is that it may choose observations that
also result in bad matches (Caliendo & Kopeinig, 2005). To improve KM matching, a
bandwidth is applied; however, underlying features may be disrupted by a large bandwidth
resulting in a biased estimate. The choice of bandwidth is, therefore, a compromise between
a small variance and an unbiased estimate (ibid). Given the advantages, the study also
applied KM with bandwidth to produce an unbiased ATT for IPL.
Applying the matching approaches, the ATT can be calculated as the mean difference in
outcome between participants and matched non-participants, if CIA and CS are valid, as
explained previously.

17
2.2.3 Checking the matching quality
After ATT estimation, this research carried out tests for checking the matching quality. The
reason for checking the matching quality is that conditioning is done on the PS but not on all
covariates; therefore, the balance of the distribution of the relevant variables in both the
control and treatment group requires examination (Caliendo & Kopeinig, 2005). For this, the
balance of covariates before and after matching, are checked in two ways. First, a two-
sample t-test, according to Rosenbaum & Rubin (1985:35), is undertaken to examine
differences in covariate means for treated and untreated matched groups. Before matching,
differences are expected in the covariates for two groups, but after matching, the covariates
should be balanced in both groups, and thus no significant differences are expected to be
found (Caliendo & Kopeinig, 2005).
The second approach used in this research is to judge the reduction in standard bias from
‘pstest’ after matching the treatment and control groups, recommended by Rosenbum &
Rubin (1985). The standard bias is defined as “the difference of sample means in the treated
and matched control subsamples as a percentage of the square root of the average sample
variance in both groups” (Caliendo & Kopeinig, 2005). Although there is no specific
instruction, a standard bias below 3-5% after matching is considered sufficient for unbiased
estimates (ibid). Besides these two approaches, Sianesi (2004) suggests comparing pseudo-R2
before and after matching. Pseudo-R2
indicates how well the predictors explain the
participation probability (Caliendo & Kopeinig, 2005). After matching, the pseudo-R2
will be
lower. Additionally, the likelihoods ratio chi-square statistics (LR X2)
after matching allows
one to reject the null hypothesis of the covariates being jointly insignificant (ibid).
As discussed in section 2.2, sensitivity analysis is also carried out to test how sensitive the
ATT estimates are to the confounding factors influencing allocation to treatment.
2.3Limitations
This section discusses the methodological limitations of PSM as an evaluation tool and also
presents some other challenges in measuring the impact of sanitation which is implied in this
research.
Despite PSM being treated as an alternative to RCT, its main drawback is that it cannot

18
address the selection of unobservable factors. Second, its reliance on the balance of observed
covariates does not secure balance of unobserved characteristics (Williamson et al., 2011).
Thus, failure to include a relevant confounder in the PS model may result in a biased PSM
estimate (ibid). As discussed in section 2.2, the application of a matching method is sensitive
to the quality of the data. The vulnerability of the PSM estimates to the sensitivity analysis
lies with the quality of the data (Duvendack & Palmer-Jones, 2012). However, despite the
credibility of the DHS data, there is uncertainty with regards to the influence of unobserved
heterogeneity because DHS is not designed for sanitation impact evaluation, as mentioned in
section 2.2. Therefore, use of one-point in time cross-sectional data may further influence the
limitations of the PSM method. Given these methodological short-comings, the estimates in
this research could be argued to be biased.
Similar to other quantitative designs, PSM cannot unveil the insights of the ‘black box’ of
how an IPL does or does not work (Bryson et al., 2002). This dissertation would have had
much more explanatory power if it had adopted a theory-driven evaluation study. However,
this was not possible due to time constraints.
The analysis may also have suffered from endogeneity because of the infectious nature of
diarrhoea. To examine the endogeneity problem, this research did not find any variable in the
dataset that is strongly correlated with the participation in IPL but is not directly associated
with the diarrhoea for an instrumental variable regression.

19
3. Results
This chapter presents the results derived from PSM in five sections. In section 3.1, the
sample is described. In section 3.2, the chapter presents the descriptive statistics and
estimation of PS for the full sample and stratified samples. In sections 3.3 and 3.4, the ATT
estimates of IPL on childhood diarrhoea are depicted.. Lastly, in section 3.5 the results of the
robustness checks are presented.
3.1 Description of sample
Table 1 shows the distribution of IPL by children’s age groups, by location and by diarrhoea
infection. According to the statistics in Table 1, the IPL coverage in rural areas is three times
as large as in urban areas.
Table 1: Coverage of IPL by location, child age and diarrhoea infection
The chapter then
Disaggregates
Households with IPL,
N (column %)
Households without
IPL, N (column %)
Total
households, N
(column %)
Age category
Children <24
months old 1,420 (61.08) 3,071 (59.42) 4,491 (59.94)
Children
=>24 months
& <=59
months 905 (38.92) 2,097 (40.58) 3,002 (40.06)
Location type
Rural 1,755 (75.48) 3,364 (65.09) 5,119 (68.32)
Urban 570 (24.52) 1,804 (34.91) 2,374 (31.68)
Diarrhoea
infection
Infected 87 (3.74) 282 (5.46) 369 (4.92)
Uninfected 2,238 (96.26) 4,886 (94.54) 7,124 (95.08)
Total (row %) 2,325 (31.02) 5,168 (68.98) 7,493
Note: Figures in the parenthesis denote distribution in percentage

20
The results in Table 1 indicate that IPL has been widely introduced to rural areas to improve
sanitation conditions. Furthermore, more than three-fifths of IPL coverage goes to
households with children under 24 months. This large coverage among these households is
likely to be linked with the targeting criteria for the intervention. Table 1 also reveals that a
lower incidence of diarrhoea (3.74%) is found among households with IPL compared to
5.46% in households without IPL. This statistic has prompted this dissertation to examine
whether any causal association exists between the lower incidence of diarrhoea and IPL.
3.2 Descriptive statistics of covariates and propensity score estimation
Appendix 1 (Tables 5.0-5.4) presents the pre-matching descriptive statistics of covariates and
PS logit models for the full and stratified samples. The differences in mean values of the
majority of the selected variables across the treated and non-treated groups of the full sample
(see Appendix 1; Table 5.0, column 5) are statistically significant. This indicates that
matching would likely be successful and would improve the precision of the estimates
(Kumar &Vollmer, 2011). However, the differences are narrow except for some
demographic variables such as age of household head and household size. Moreover, signs
of the mean differences for the variables of pipe water, richest wealth quintile, wall material
(brick and cement), cooking material (crop residue and dung), Sylhet division and urban area
are negative. This indicates that the untreated sample households are likely to be wealthier
than the treated households, particularly in urban areas.
The differences in mean values of the covariates for the stratified samples (see Table 5.1-5.4,
column 5) are also largely similar to the mean differences of the covariates for the entire
sample with a few exceptions for the rural sample. For example, the mean difference for the
poorest quintile of the rural sample is negative. This reveals that the untreated poorest
households are wealthier than the treated poorest households.
The results of logit regressions for the full sample and stratified samples are also presented in
Appendix 1 (Table: 5.0-5.4, column 1-2). Table 5 (column 1) shows that almost all covariates
significantly influence the household’s participation in IPL. The households with access to
tubewell water, or with access to pipe water and the households in the upper wealth quintile
compared to the poorest are more likely to participate in IPL interventions. This finding is

21
similar to the findings of the study undertaken in Nepal (Bose, 2009). Furthermore, the
households depending on charcoal, kerosene, and crop-residue as fuel material compared to
gas are highly likely to participate in the intervention. However, the household’s location
(urban) and wall material (brick and cement) show a negative influence in IPL participation.
This explains that households in urban areas are less likely to participate in IPL interventions.
Households belonging to upper occupational groups (skilled worker, professional and/or
large business) and ownership of cultivable land do not have any significant bearing on the
probability of participation in IPL. This may be because of considerable heterogeneity within
the richest quintile.
Tables 5.1 to 5.4 in Appendix 1 provide the results of logit regressions (column 1-2) for the
stratified samples. The influence of some covariates on the model for children under 24
months varies from the influence on the model for children between 24 months and 59
months old. In particular, households with children under 24 months with respondent’s
education above the below primary level are more likely to participate in the intervention
compared to households with the same age children where respondents’ education is below
primary level. This situation is almost reverse for the model of children between 24 months
and 59 months. Similarly, the influence of some covariates on the model for children in rural
areas varies from the influence on the model for children in urban areas. In particular,
wealthier households in rural areas and households in the divisions other than in Dhaka are
more likely to participate in IPL interventions. This is not the case for the urban model where
wealth does not have any significant influence on IPL participation and households in
Rajshahi and Chittagong divisions are highly likely to participate in IPL interventions.
While estimating PS, 25 missing values were generated. Thus, the total sample reduced from
7,493 to 7,468. Figure 3, a Kernel density plot, illustrates the comparative PS distribution
between the treatment and control group. It also shows a significant area of overlap despite a
skewed distribution for non-treated households towards the higher end of the PS.

22
Figure 3: Kernel density plot of PS distribution by treatment and control group
Figure 4, the box plot, shows the disparity in percentile distribution of PS between the
treatment and control group before matching. The median value of PS for the treatment
group (0.4415) is more than double that of the median value of PS for the control group
(0.2157). This illustrates a disparity between the treatment and control group that may be due
to non-random selection of assignment. The descriptive statistics suggest that the skewness
for the control group (0.627) is much higher than that of the treatment group (-0.136).
Figure 4: Percentile distribution of PS before matching

23
Moreover, the histogram, Figure 5, and the density plot, Figure-6, after NNM, display
another form of visualization of the level of CS. They reveal an almost balanced overlap
between the treated and untreated groups. Both Figure 3 and Figure 5 provide adequate
evidence of CS assumption before and after matching. Figure-6 illustrates an equal
probability of participation of the matched treated and untreated groups after reducing the PS
disparity between them. Appendix 2 (Figure 5.1 -5.4) presents the histograms of PS for the
stratified samples showing the same evidence for CS assumptions. Furthermore, a detailed
numeric distribution of CS for full sample and stratified samples is included in Appendix 3
(Table 6) that shows that a limited number of treated samples were out of the CS, except for
NN5 matching for children in rural areas.
Figure 5: Histogram of PS by treated and untreated group after matching (NN5, caliper 0.09)
Figure 6: Distribution of PS by treated and untreated group after matching
0 .2 .4 .6 .8
Propensity Score
Untreated Treated

24
3.3Average treatment effect
The ATT was estimated by using the psmatch2 command on Stata 14. Matching with NN5
(five nearest neighbours) seemed a more effective selection for this research than matching
with a single nearest neighbour. This may be associated with the unavailability of adequate
single nearest neighbours in the untreated group, due to skewed PS distribution.
Table 2 presents the ATT of IPL on childhood diarrhoea. Both KM and NN5 matching show
a significant reduction in diarrhoea incidence among children in households with IPL.
According to KM and NN5 matching, the mean incidence of diarrhoea among children in the
households with IPL is 1.3 and 1.2 percentage points lower than among children in the
households without IPL respectively. This difference, if 1.3 percent is considered, translates
to a 24% reduction in the incidence of diarrhoea (1.3*100/5.46) for a comparison group with
5.46% of children on average with diarrhoea in the past two weeks. In addition, both
estimates from the matching algorithms are almost equal in size and are statistically
significant at 5% and 10% significance levels respectively. The resulting almost equal size of
ATT estimates from both matching algorithms is likely to indicate robustness of the
estimation.
Table 2: PSM estimates of ATT for probability of diarrhoea incidence (full sample)
Matching method Sample Treatment Control Difference S.E. T-Stat
Kernel
(bwidth = 0.09)
Unmatched 0.037 0.054 -0.017 0.005 -3.21
Matched 0.037 0.050 -0.013** 0.006 -2.12
NN5
(caliper = 0.09)
Unmatched 0.037 0.054 -0.017 0.005 -3.21
Matched 0.037 0.049 -0.012* 0.007 -1.76
Notes: Significance level: ***p<0.01, **p<0.05, *p<0.1

25
3.4 Heterogeneous treatment effect
Disaggregated PSM results show that the effect of IPL on diarrhoea incidence varies by sub-
groups such as age and location of the children. Table 7 in Appendix 4 indicates that ATT on
children below 24 months, based on KM, is 3.4 percentage points lower than for children of
the same age group without IPL. This estimate is significant at 1% significance level, and is
also consistent with NN5 estimate.
Table 7 further reveals that the effect of IPL among children in rural areas is quite promising.
The ATT estimates, based on KM and NN5, for children in rural areas with IPL are 1.7 and
1.8 percentage points lower respectively than for children without IPL. Both estimates are
statistically significant at 5% significance level. However, no significant effect of IPL is
found for children in urban areas. The urban dwellers, particularly in slum areas, share their
latrine with other households. This research has excluded those families from the treatment
group who share their pit latrine with others.
Further discussion of the results and their implications are included in the next chapter.
3.5 Robustness checks
3.5.1 Balancing test
The results of t-test analysis for the matched group for aggregated and disaggregated samples
are presented in Tables 8.0-8.4 in Appendix 5. No statistically significant differences in
matched treatment and control group were found, except one wealth related covariate –the
richer quintile, which shows a significant difference at the 10% significance level. This might
be due to high heterogeneity in the richer quintile. Although the balancing tests do not predict
whether CIA holds, they provide a strong signal regarding the performance of the matching
estimates.

26
3.5.2 Standardised bias, joint significance and pseudo-R2
The level of standardised bias is an indication of matching quality. Tables (8.0 -8.4) in
Appendix-5 show the level of bias and bias reduction after KM. Except for the wealth
related covariates – the richer quintile in the specification for the full sample (Table 8.0) and
the richer and richest quintiles in the specification for the urban sample (Table 8.4) - the
standardised bias for other covariates has significantly reduced to below 3-5%, recognised as
sufficient by Caliendo & Kopeinig (2005). Table 3, given below, provides summary statistics
for matching quality of both KM and NNM for the full sample, showing a significant
reduction of pseduo-R2
and LR X2
. The reduction of pseduo-R2
means that both treated and
untreated in the matched group have an equal probability of participation in an IPL
intervention. Moreover, a reduced LR X2
statistic rejects the null hypothesis on the joint
insignificance of means of the variables. This is further evidence of matching quality.
Table 3: Summary statistics for matching quality for full sample, N=7468
Matching algorithm Sample
Pseudo-
R2
LR χ 2
p> χ 2
Mean
Bias
Median
Bias
Kernel
(bwidth =0.09)
Unmatched 0.154 1423.9 0.000 16.1 12.5
Matched 0.002 11.32 0.999 1.5 1.2
NN5 (caliper = 0.09)
Unmatched 0.154 1423.9 0.000 16.1 12.5
Matched 0.002 15.54 0.986 1.9 1.6
Besides these two important statistics, Table 3 also shows an overall low level of mean bias
and median bias of the covariates for the matched samples. This suggests that a high level of
matching has been accomplished. Moreover, the higher level of p value fails to reject the null
hypothesis that there is no difference between the treated and untreated samples in the
matched group. Table 9 in Appendix 6 presents the summary statistics for matching quality
for stratified samples and the findings are almost similar to the findings discussed above.

27
3.5.3 Hidden bias and sensitivity analysis
As mentioned in the previous chapter (section 2.3) the PSM method cannot solve the
selection of unobservable factors influencing a household’s participation in IPL intervention.
For example, households with IPL may have additional motivation and commitment that may
drive them to invest more in the sanitation facility. These underlying drivers remain hidden
and can still bias estimates obtained from PSM methods.
To examine hidden biases in the estimates, this research carried out sensitivity analysis using
Wilcoxon’s signed rank test. The analysis provides a range of values (bounds) such as p
values or CI attributable to confounding factors (Rosenbaum, 2002). The presence of hidden
bias influences the odds of participating in the programme, which is called gamma, Γ (ibid).
Based on the Γ value, one can predict the level of bias in the estimate. When Γ is 1 at a
conventional significance level, the estimates are free of hidden bias, but when Γ > 1, the
interval of p values indicates uncertainty about the estimates. “As Γ increases, this interval
becomes longer and eventually it becomes uninformative, including both large and small p
values. The point, Γ, at which the interval becomes uninformative is a measure to hidden
bias” (Rosenbaum, 2005:1810).
Results of sensitivity analysis for the full sample, shown in Table 4, suggests the estimate
from NNM is highly sensitive to hidden bias. This reflects an uncertainty of the NN matching
estimate since Γ at 1 is statistically highly insignificant and starts to become significant at 1.4
but with improbability because of the large interval of p value.

28
Table 4: Sensitivity analysis: Wilcoxon’s signed rank test (full sample)
Gamma
Range of Significance Level
Lower bound Upper bound
1 0.933 0.933
1.1 0.745 0.990
1.2 0.456 0.999
1.3 0.206 0.999
1.4 0.070 0.999
1.5 0.018 0.999
1.6 0.003 0.999
1.7 0.000 0.999
1.8 0.000 1.000
1.9 0.000 1.000
2 0.000 1.000
Notes: Gamma (Γ) denotes log odds of different assignment due to unobserved factors
Table 10 in Appendix 7 presents the outcome of sensitivity analysis for the stratified
samples. The results of the analyses reflect the same as discussed above for the full sample
that the estimates from NNM suffer from the influence of unobservable variables.
Particularly, the unobserved heterogeneity is much stronger in the rural sample, for which
sensitivity analysis shows an insignificant p value for Γ.

29
4. Discussion of results
Having presented the impact estimates, the dissertation now moves on to discussing the
results, their robustness, and to reflect on how the results fit into the conceptual model of
breaking disease transmission cycle by WSH programmes. The chapter concludes with some
reflections on policy and future research implications.
4.1 Impact of improved pit latrine (IPL) on childhood diarrhoea
This dissertation hypothesized that there is no effect of IPL on diarrhoea among children
below five. The research rejects the null hypothesis and estimates that IPL can reduce
diarrhoea incidence by 24% among children below five. This estimate is consistent with
Aziz et al. & Daniels et al. (1990) although the measurement approaches of those previous
studies were different (case-control design).
However, the findings from this research does not match with the findings from Begum et al.
(2013) despite using the same measurement approach. Using the DHS 1997 and 2007,
Begum et al. did not find any significant effect (ATT: 0.8% points) of improved sanitation on
childhood diarrhoea. The probable reason for this inconsistency is that Begum et al. applied a
composite definition of improved sanitation that encompassed all types of improved toilets,
which are not supposed to be homogenous in terms of functions and effectiveness. Thus, the
composite treatment variable may mislead by overestimating or underestimating the effect of
the sanitation facility. Further research on specific toilet facilities could validate this
assumption.
The second hypothesis for this dissertation was that the effect of IPL does not vary between
children below 24 months and those from 24 months to 59 months old. This dissertation also
rejects this hypothesis claiming that IPL can significantly influence diarrhoea incidence
among children less than 24 months old. This effect cannot be validated due to lack of
evidence from Bangladesh. However, this finding is consistent with Bose (2009) who used
the 2001 and 2006 Nepal DHS datasets. The size of treatment effect from the research in
Nepal is much higher (ATT: -.11, p <0.05) than the estimate from this dissertation (ATT: -
0.034, p<0.05). This variation is likely to be associated with time, different context and the

30
use of a broad definition in the research in Nepal. This research, on the contrary, does not
find any significant effect on children from 24 months to 59 months old. This could be
connected with the children’s greater mobility at this age that exposes them to different
environments, which, in turn, hinders the effect of IPL on this age group.
One key question that may come to mind is how the IPL can have an effect on children less
than 24 months old who are too young to use the toilet independently. Neither past research
nor this research can answer this question because of the quantitative design. Although this
research, because of its fixed nature of design, cannot argue with any concrete evidence on
how this impact happens to very young children, it is a possibility that the use of IPL by the
children’s mother or their care takers is likely to have an indirect effect on the children’s
health. A theory-based and mixed- method driven impact evaluation may unpack this
assumption.
This dissertation also sought to examine the hypothesis that the effect of IPL among children
in rural areas does not differ from the effect among children in urban areas. The findings
from this research reject this null hypothesis too and make the inference that while IPL can
significantly lower the incidence of diarrhoea among children in rural areas, it cannot do the
same in urban area. There is no adequate urban and rural disaggregated knowledge to cross-
validate this finding from the same context. However, this finding is consistent with the
findings of research conducted in Egypt (Roushdi et al., 2013). From the research presented
in this thesis, the estimated effect in rural areas may be related to the greater number of
treated households and less diverse improved toilet facilities, whereas the insignificant effect
in urban areas is perhaps be connected with the existence of various improved toilet facilities
there and the exclusion of shared facilities from the definition of IPL. Sharing toilet facilities
in urban areas, particularly in slums is a more common phenomenon. However, exclusion
criterion may have minimised positive and negative externalities resulted from sharing
phenomenon in urban areas.
4.2 Robustness of the PSM impact estimates
The question can also be posed as to how robust the ATT estimates from this research are.
However, there is no straightforward answer when one uses both KM and NNM algorithms.

31
Estimates from NNM from this research are highly sensitive to hidden bias, thus, it suggests
a cautious interpretation while claiming preciseness of the estimates. Based on the
insignificant p value from the Wilcoxon’s signed ranked tests, the estimate from NNM
suggests that, apart from IPL, there are unseen factors that may have had an influence in the
reduction of diarrhoea. The influence of hidden bias cannot be determined on KM estimates,
thus, it is difficult to reject the precision of the ATT estimates. This is, in other words, a
‘methodological limbo’ for PSM estimates.
Additionally, the sensitivity analysis itself is not beyond controversy because researchers
might draw misleading and logically incoherent conclusions, based on paradoxical measures
of hidden bias, as argued by Robins (2002). This dissertation suggests that CIA is likely to be
vulnerable to one point-in time cross-sectional dataset, particularly to the DHS dataset used
for this dissertation. The applicability of sensitivity analysis to the cross-sectional dataset,
which is not objectively designed for any sanitation impact study, is likely to be less
informative and interpretative for the PSM method.
Likelihoods of externalities is reasonably high in the impact of sanitation due to the inter-
household dynamics in the community. Because of the use of QED and a one-time cross-
sectional dataset in this research, positive externalities (such as transmission of knowledge
regarding the importance of improved sanitation) or negative externalities (such as
transmission of disease organism from neighbours who use unimproved toilet facilities) may
not have been included in the PSM estimates. Corsi et al. (2011) and Alderman et al. (2003)
point out that positive spill-over and negative spill-over effects are associated with the
provision of toilet facilities in communities. Furthermore, although a 14-day recall period to
capture diarrhoea incidence is commonly used in many studies, a recall period exceeding 48
hours is methodologically problematic (for example, data accuracy) Blum & Feachem, 1983;
Boerma et al., 1991), which can also influence the estimates. This.

32
4.3 Linking the impact of IPL to the conceptual model of barriers to disease
transmission
The causal relationship established between IPL and childhood diarrhoea from this research
reflect the relevance of the conceptual model of barriers to disease transmission by WSH
interventions (Figure 1). According to the model, improved sanitation can break the
transmission of pathogen to the human body. Although this dissertation has partially
addressed the conceptual model, it appears that the use of a narrow definition (specific)
instead of a broad definition (generic) of improved sanitation would likely support the
construction of a homogenous treatment group for QED. The model offers a generic view on
how a WSH programme can interrupt the disease transmission; however, it is important to
recognise that sanitation practices are culturally driven and context specific. Therefore, the
model would likely to be much more effective as a framework for impact evaluation design if
one customizes the definition based on the particular intervention or context.
Moreover, the purpose of the combined definition for improved sanitation by the
WHO/UNICEF-JMP was to estimate the progress towards the MDG target for WSH that are
comparable among countries and across times (UNICEF &WHO, 2015). From this
monitoring perspective, the “one definition for all’ strategy seems to be effective; however, it
may not be practical from the perspective of an impact evaluation when one intends to
produce a precise impact estimate of improved sanitation.
Converting too much heterogeneous interventions into a homogenous treatment variable is
likely to dilute the causal directions between the treatment and outcome variables and
produce an unreliable impact estimate. This observation comes to mind while reviewing the
literature of the past sanitation impact studies and exploring the applicability of a broad
definition of improved sanitation in the initial stage of this research design. The balancing
test often failed to provide the expected t- test statistics and the level of standard bias after
matching. It is, therefore, assumed that the use of a composite definition may have had an
influence on past research results. However, this pre-mature assumption would need to be
proven by comparative research using both a generic definition of improved sanitation and a
specific definition of an improved toilet facility.

33
4.4 Policy and research recommendations
One of the targets of the Millennium Development Goal (MDG 7: ensure environmental
sustainability) was to halve the proportion of the population without sustainable access to
safe drinking-water and basic sanitation by 2015. Although Bangladesh has made
considerable progress in the improvement of sanitation facilities (UNICEF & WHO, 2015),
more than half of the population, according to NIPORT et al. (2016), is still beyond the reach
of an improved toilet facility. The current prevalence of diarrhoea among children in
Bangladesh appears not high; however, children’s vulnerability to this disease still remains
extreme due to the country’s diverse agro-ecological conditions such as a long rainy season,
water-logging conditions and drought. Moreover, diarrhoea is identified as one of the leading
causes of child deaths in Bangladesh. How can this problem be addressed and how can policy
help fully realize the benefits of improved sanitation? This research has found a significant
effect of IPL on childhood diarrhoea particularly among children below 2 and children in
rural areas. These findings can be used as substantial inputs to the development of the next
Poverty Reduction Strategy Paper for Bangladesh in order to achieve the global Water,
Sanitation and Hygiene Programme target by 2030 for Sustainable Development Goals. The
rationale for this policy recommendation is that the per person annual cost for IPL is less than
USD 5 (Hutton & Haller, 2004) which is low compared to the value of a child’s life and
disease burden cost of diarrhoea.
For the medium to short-term strategy, the Government of Bangladesh can facilitate a Total
Sanitation Campaign (TSC) in rural areas in cooperation with non-government organisations
(NGOs) and international development partners to inform households about the effectiveness
of IPL and share the knowledge about how improved sanitation facilities break the
transmission of diarrhoea pathogens to the human body. The TSC will help mobilize people
in rural communities to combat diarrhoea by ensuring an IPL facility in those households that
do not possess any toilet facility. Because of the likelihood of negative externality, the
ultimate benefit of improved sanitation can be undermined in a community where a few
households do not have improved toilet facility, but others have. Therefore, the total
sanitation campaign is recommended to maximize the impact of an IPL.

34
As discussed, the estimated average treatment effect of the treated (ATT) may be biased due
to confounding variables; therefore, future research applying a longitudinal design (such
pretest and posttest) and incorporating a combination of analytical approaches such as the
propensity score method and difference-in difference would in all likelihood produce a more
robust estimate by controlling unobservable bias and time-variant heterogeneity. This
dissertation also suggests examining the effectiveness of the WHO/UNICEF’s broad
definition for improved sanitation compared with the narrow definition of improved toilet
facility.

35
Conclusion
This dissertation employing the PSM (Propensity Score Matching) methods evaluated the
effect of IPL (Improved Pit Latrine) on diarrhoea among children below five. The data used
for analysis comes from a nation-wide DHS in Bangladesh.
It is important to acknowledge that this dissertation used a one-point in time cross-sectional
dataset to examine the effect of improved pit latrines. Estimates from NNM suffer from
unobservable heterogeneity. The preciseness of the PSM estimates from this research ought
to be cautiously interpreted. The CIA for PSM method is likely to be vulnerable to the DHS
dataset used in this dissertation. Thus further research using a longitudinal design is required
to produce more precise and robust estimates.
Having hypothesized whether and to what extent IPL can reduce childhood diarrhoea, this
dissertation finds IPL can significantly influence diarrhoea infection among children.
Specifically, the diarrhoea incidence is 1.3 percentage points lower in households with IPL as
compared to households without IPL. This dissertation also finds considerable impact
heterogeneity of IPL on diarrhoea. A child’s age and location are found to be two important
factors for the impact of IPL on diarrhoea. This research finds IPL can significantly diminish
diarrhoea among children below 2 years of age by 3.4 percentage points. This impact of IPL
is huge to combat diarrhoea among children. Children in rural areas seem to benefit more
from the effect of IPL. This dissertation finds the diarrhoea incidence is 1.7 percentage points
lower among children in households with IPL in rural areas compared to households in rural
areas without IPL.
From the perspective of the public health service, the findings from this dissertation can
significantly contribute to the GoB in the formulation of long-term and short-term strategies
to fight against childhood diarrhoea. Specifically, the GoB can build the country’s next
PRSP considering the benefit of IPL compared to its per-person annual cost to promote
universal access to improved sanitation. Inclusion of these findings in the country’s PRSP
will guide the GoB to achieve the SDG’s global sanitation target by 2030. Second, the GoB
in partnership with donors and NGOs can undertake Total Sanitation Campaign (TSC) in

36
rural areas by disseminating the benefit of IPL in order to mobilize the rural community to
halt diarrhoea. Evidence- based TSC will inspire those households without a toilet to
establish an improved toilet facility to save their children from diarrhoea infection and other
health hazards.

37
Bibliography
Alderman, H., Hentschel, J. & Sabates, R., 2003. With the Help of One’s Neighbours of
Nutrition in Peru. Social Science & Medicine, 56(10), p. 2019–2031.
Andres, L.A., Briceno, B., Chase, C. & Echenique, J.A., 2014. Sanitation and Externalities:
Evidence from Early Childhood Health in Rural India. World Bank: Washington DC. Policy
Research Working Paper, 6737, p.1-39.
Aziz, K.M.A., Hoque, B.A., Hasan, K.Z., Patwary, M.Y., Huttly, S.R., Rahaman, M.M. &
Feachem, R.G., 1990. Reduction of Diarrhoeal Diseases in Children in Rural Bangladesh by
Environmental and Behavioural Modifications. Trans R Soc Trop Med Hyg, 84, p. 433-438.
Bado, A.R., Susuman, A.S. & Nebie, E.I., 2016. Trends and Risk Factors for Childhood
Diarrhoea in Sub-Saharan Countries (1990-2013): Assessing Neighbouring Inequalities.
Global Health Action, 9: 30166
Baker K.K., O’Reilly, C.E., Levine, M.M., Kotloff, K.L., Nataro, J.P., Ayers, T.L et al.,
2016. Sanitation and Hygiene-Specific Risk Factors for Moderate-to- Severe Diarrhoea in
Young Children in the Global Enteric Multicenter Study, 2007–2011: Case-Control Study.
PLoS Med 13(5): e1002010.
Baker, L. J., 2000. Evaluating the Impact of Development Projects on Poverty: A Handbook
for Practitioners. Directions in Development, Washington, D.C: World Bank.
Baltazar, J., J. Briscoe Mesola V et al. 1988. Can the Case-Control Method be Used to
Assess the Impact of Water Supply and Sanitation on Diarrhoea? A Study in the Philippines.
Bulletin of the World Health Organization, 66, p. 627-635.
Bamberger, M., Rugh, J. & Mabry, L., 2012. Real World Evaluation: Working Under
Budget, Time, Data and Political Constraints. 2nd Eds. Thousands Oaks: SAGE.
Baqui, A. H., Sabir, A.A., Begum, N., Arifeen, S.E., Mitra, S.N. & Black, R.E., 2001. Causes
of Childhood Deaths in Bangladesh: An Update. Acta Paediatr, 90.

38
BBS (Bangladesh Bureau of Statistics). 2015. Report on Bangladesh Sample Vital Statistics
2014. Dhaka: Reproduction, Documentation and Publication Section, Bangladesh Bureau of
Statistics. Ministry of Planning, Government of Bangladesh. Available at:
http://www.bbs.gov.bd [accessed July 20, 2016]
BBS & UNICEF (United Nation’s Children Fund). 2015. Monitoring the Situation of
Children and Women: Multiple Indicators Cluster Survey- 2012-2013. Dhaka: Ministry of
Planning, Government of Bangladesh.
Begum, S., Ahmed, M. and Sen, B., 2013. Impact of Water and Sanitation Interventions on
Childhood Diarrhoea: Evidence from Bangladesh. International Initiative for Impact
Evaluation (3ie), Grantee Final Report.
Blum, D., and R. G. Feachem. 1983. Measuring the Impact of Water Supply and Sanitation
Investment on Diarrhoeal Disease: Problems of Methodology. International Journal of
Epidemiology, 12, p. 357-365.
Boadi, K.O. & Kuitunen, M., 2005. Childhood Diarrheoal Morbidity in the Accra
Metropolitan Area, Ghana: Socio-Economic, Environmental and Behavioural Risk
Determinants, World Health & Population. [Online]. Available at:
http://www.electronichealthcare.net/content/17646/print [accessed 7 July, 2016]
Boerma, J., Black, R., Sommerfelt, A., Rutstein, S. and Bicego, G., 1991. Accuracy and
Completeness of Mothers’ Recall of Diarrhoea Occurrence in Pre-school Children in
Demographic and Health Surveys. International Journal of Epidemiology, 20, p. 1073–1080.
Boschi-Pinto, C., Velebit, L. & Shibuya, K., 2008. Estimating Child Mortality Due to
Diarrhoea in Developing Countries; Bulletin of the World Health Organisation, 86, p. 710-
717
Bose, R., 2009. The Impact of Water Supply and Sanitation Interventions on Child Health:
Evidence from DHS Surveys, In Bi-annual Conference on Impact Evaluation in Colombo,
Sri Lanka, 22-23 April, 2009. New Delhi, India: International Initiative for Impact
Evaluation (3ie).

39
Briscoe, J., Feachem, RG. & Rahaman, M.M., 1985. Measuring the Impact of Water Supply
and Sanitation Facilities on Diarrhoea Morbidity: Prospects for Case-control Methods. Offset
Publication WHO/CWS/85.3. Geneva, World Health Organisation.
Bryson, A., Dorsett, R. and Purdon, S., 2002. The Use of Propensity Score Matching in the
Evaluation of Active Labour Market Policies. Policy Studies Institute and National Centre
for Social Research, Working Paper No. 4, Department for Work and Pensions, London.
Budhathoki, S.S., Bhattachan, M., Yadav, A.K., Upadyaya, P. & Pokharel, P.K., 2016. Eco-
social and Behavioural Determinants of Diarrhoea in Under-five Children of Nepal: A
Framework Analysis of the Existing Literature. Tropical Medicine and Health, 44:7
Caliendo, M. & Kopeinig, S., 2005. Some Practical Guidance for the Implementation of
Propensity Score Matching. Forschungsinstitut zur Zukunft der Arbeit (IZA) Discussion
Paper No. 1588. May.
Clasen, T., Schimidt, W-P., Rabie, T., Roberts, I. & Cairncross, S. et al., 2007. Interventions
to Improve Water Quality for Preventing Diarrhoea: Systematic Review and Meta-analysis.
British medical journal, 334, p. 782–791. 
Clasen, T., Boisson, S., Routry, P., Torondel, B., Bell, M., Cumming, O., Ensink, J.,
Freeman, M., Jenkins, M., Odagiri, M., Ray, S., Sinha, A., Suar, M. & Schimidt, W-P., 2014.
Effectiveness of a Rural Sanitation Programme on Diarrhoea, Soil-transmitted Helminth
Infection, and Child Malnutrition in Odisha, India: A Cluster-randomised Trial. Lancet Glob
Health, 2: e645–653
Corsi, D. J., Chow, C. K., Lear, S. A., Rahman, M. O., Subramanian, S. V. & Teo, K. K.,
2011. Shared Environments: A Multilevel Analysis of Community Context and Child
Nutritional Status in Bangladesh. Public Health Nutrition, 14(6), p. 951–959.
Daniels, D.L., Cousens, S.N., Makoae, L.N. & Feachem, R.G., 1990. A Case-control Study
of the Impact of Improved Sanitation on Diarrhoea Morbidity in Lesotho. Bulletin of the
World Health Organization 68(4), p. 455-463.

40
Dehejia, R. & Wahba, S., 2002. Propensity Score-Matching Methods for Nonexperimental
Causal Studies. The Review of Economics and Statistics, 84(1), p. 151–161. 
Duvendack, M. & Palmer-Jones, R., 2012. High Noon for Microfinance Impact Evaluations:
Re-investigating the Evidence from Bangladesh. Journal of Development Studies, 48,
p.1864-1880.
Esrey, S. A. and J. P. Habicht., 1985. The Impact of Improved Water Supplies and Excreta
Disposal Facilities on Diarrhoeal Morbidity, Growth, and Mortality among Young Children.
Cornell International Nutrition Monograph Series. Division of Nutritional Sciences, Cornell
University, Ithaca.
Esrey, S.A., Potash J.B. Roberts L & Shiff, C., 1991. Effects of Improved Water Supply and
Sanitation on Ascariasis, Diarrhoea, Dracunculiasis, Hookworm Infection, Schistosomiasis,
and Trachoma. Bulletin of the World Health Organization, 69(5), p. 609-621.
Feachem, R. G., 1984. Interventions for the Control of Diarrhoeal Diseases among Young
Children: Promotion of Personal and Domestic Hygiene. Bulletin of the World Health
Organization, 62(3), p. 467- 476.
Fewtrell, L., Kaufmann, R.B. Kay, D. Enanoria, W. Haller, L., & Colford , JM Jr.., 2005.
Water, Sanitation, and Hygiene Interventions to Reduce Diarrhoea in Less Developed
Countries: A Systematic Review and Meta-analysis. Lancet Infect Dis, 5, p. 42–52.
Fink, G., Gunther, I. & Hill, K., 2011. The Effect of Water and Sanitation on Child health:
Evidence from the Demographic and Health Surveys 1986–2007. Oxford University Press.
International Journal of Epidemiology, 40, p.1196–1204.
Fuller, J.A., Westphal, J.A., Kenney, B. & Eisenberg, J.N.S., 2015. The Joint Effects of
Water and Sanitation on Diarrhoeal Disease: A Multicountry Analysis of the Demographic
and Health Surveys. Tropical Medicine and International Health, 20 (3): 284–292.
Guo, S., & Fraser, W. M., 2015. Propensity Score Analysis: Statistical Methods and
Applications. 2nd Eds. Washington DC: SAGE.

41
Heckman, J. J., Ichimura, H., & Todd, P., 1997. Matching as an Econometric Evaluation
Estimator: Evidence from Evaluating a Job Training Programme. Review of Economic
Studies 64 (4), p. 605–654. 
Heckman, J., Ichimura, H., Smith, J., & Todd, P., 1997a. Characterizing Selection Bias Using
Experimental Data. Econometrica 66 (5), p.1017–1098. 
Heckman, J., LaLonde, R., and Smith. J., 1999. The Economics and Econometrics of Active
Labor Market Programs, in Handbook of Labor Economics Vol. III, ed. by O. Ashenfelter,
and D. Card, p. 1865–2097. Elsevier, Amsterdam.
Hutton, G. & Haller, L., 2004. Evaluation of the Costs and Benefits of Water and Sanitation
Improvements at the Global Level. Geneva: World Health Organisation.
Islam, M.Z. & Karim, M.A., 1992. Water, Sanitation and Hygiene in Rural Bangladesh.
Irrigation Engineering and Rural Planning, 23, p. 57-59
Khanna, Gauri. 2008. The Impact on Child Health from Access to Water and Sanitation and
Other Socioeconomic Factors. HEI Working Paper No: 02/2008. Graduate Institute of
International Studies, Geneva.
Khandker, S.R., Koolwal, G.B. and Samad, H.A., 2010. Handbook on Impact Evaluation:
Quantitative Methods and Practices. World Bank: Washington D.C.
Komarulzaman, A., Smits, J. & de Jong, E., 2016. Clean Water, Sanitation and Diarrhoea in
Indonesia: Effects of Household and Community Factors, Global Public Health. DOI:
10.1080/17441692.2015.1127985
Kosek, M., Bern, C. & Guerrant, R.L., 2003. The Global Burden of Diarrhoeal Disease, as
Estimated from Studies Published between 1992 and 2000. Bulletin of the World Health
Organization, 86, p.197-204.
Kumar, S. & Vollmer, S., 2011. Does Improved Sanitation Reduce Diarrhoea in Children in
Rural India? Discussion Paper, No. 107. Germany: Courant Research Centre.

42
NIPORT (National Institute of Population Research and Training), Mitra and Associates, and
ICF International. 2016. Bangladesh Demographic and Health Survey 2014. Dhaka,
Bangladesh, and Rockville, Maryland, USA: NIPORT, Mitra and Associates, and ICF
International.
NIPORT, Mitra and Associates, and ORC Macro. 2005. Bangladesh Demographic and
Health Survey 2004. Dhaka, Bangladesh and Calverton, Maryland, USA: National Institute
of Population Research and Training, Mitra and Associates, and ORC Macro.
Patil, S.R., Arnold, B.F., Salvatore, A.L., Briceno, B., Ganguly, S., Colford Jr, J.M. &
Gertler, P.J., 2014. The Effect of India’s Total Sanitation Campaign on Defecation
Behaviours and Child Health in Rural Madhya Pradesh: A Cluster Randomized Controlled
Trial. PLOS Medicine, Volume 11, Issue 8.
Prüss, A., Kay, D., Fewtrell, L. and Bartram, J., 2002. Estimating the Burden of Disease from
Water, Sanitation and Hygiene at a Global Level. Environmental Health Perspectives, 110
(5), p. 537-542.
Robins, J.M., 2002. Comment on “Covariance Adjustment in Randomized Experiments and
Observational Studies.” Statistical Science, 17, p. 309-321
Rosenbaum, P.R., & Rubin, D.B., 1983. The Central Role of the Propensity Score in
Observational Studies for Causal Effects. Biometrica, 70, p. 41-55.
Rosenbaum, P.R., & Rubin, D.B., 1985. Constructing a Control Group Using Multivariate
Matched Sampling Methods that Incorporate the Propensity Score. The American
Statistician, 39 (1), p. 33-38.
Rosenbaum, P.R. 2002. Observational Studies. 2nd ed. New York, NY: Springer.
Rosenbaum, P.R. 2005. Sensitivity Analysis in an Observational study. In B.S. Everitt & D.C
Howell (Eds). Encyclopedia of Statistics in Behavioral Science. New York: John Wiley, p.
1809-1814.

43
Roushdy, R., Maria, S. & Radwan, H., 2013. The Impact of Water Supply and Sanitation on
Child Health: Evidence from Egypt. Grantee Final Report, International Initiative for Impact
Evaluation (3ie).
Rubin, D., 1974. Estimating Causal Effects of Treatments in Randomized and
Nonrandomized Studies. Journal of Educational Psychology, 66, p. 688-701.
Schmidt, W-P., 2014. The Elusive Effect of Water and Sanitation on the Global Burden of
Disease. London: Tropical Medicine and International Health, Volume 19 No. 5, p. 522–
527.
Sianesi, B. 2004. An Evaluation of the Active Labour Market Programmes in Sweden. The
Review of Economics and Statistics, 86(1), p.133–155. 
Singh, A. & Singh, M.N., 2014. Diarrhoea and Acute Respiratory Infections among Under-
five Children in Slums: Evidence from India. PeerJ PrePrints.
Smith, J. A. & Todd. P.E., 2005. Does Matching Overcome LaLonde’s Critique of
Nonexperimental Estimators? Journal of Econometrics, 125, p. 305–353.
Speich, B., Croll, D., Fürst, T., Utzinger, J. & Keiser, J., 2016. Effect of Sanitation and Water
Treatment on Intestinal Protozoa Infection: A systematic Review and Meta-analysis. Lancet
Infect Dis, 16, p. 87–99.
United Nations, 2015. Millennium Development Goal Report. New York.
UNICEF & WHO (World Health Organisation), 2015. Progress on Sanitation and Drinking
Water - 2015 Update and MDG Assessment. Geneva: World Health Organization.
Vasquez, W.F. & Aksan, A-M., 2015. Water, Sanitation, and Diarrhoea Incidence among
Children: Evidence from Guatemala. Water Policy 17, p 932–945
Waddington, H., Snilstveit, B., White, H. & Fewtrell, L., 2009. Water, Sanitation and
Hygiene Interventions to Combat Childhood Diarrhoea in Developing Countries. Synthetic
Review 1 SR 001. New Delhi, India: International Initiative for Impact Evaluation (3ie)

44
Waddington, H. & Snilsveit, B., 2009a. Effectiveness and Sustainability of Water, Sanitation,
and Hygiene Interventions in Combating Diarrhoea. Journal of Development Effectiveness
Vol. 1, No. 3, p. 295–335.
WaterAid. (no date). WaterAid Bangladesh Contribution to Submission on Urbanisation and
Water. [Online]. Available at: http://www.wateraid.org [accessed July 17, 2016]
Weiss, C.H., 1997. Theory-Based Evaluation: Past, Present, and Future. New Directions for
Evaluations. Jossey-Bass Publishers, No. 76.
White, H., 2009. Theory-Based Impact Evaluation: Principles and Practice. International
Initiative for Impact Evaluation (3ie), Working Paper No.3,
WHO/UNICEF-JMP (Joint Monitoring Programme), 2015. WASH-Post 2015: Proposed
Indicators for Drinking Water, Sanitation and Hygiene. Available at: http://www
wssinfo.org/post-2015-monitoring/ [accessed 10 June, 2016]
WHO, 2002. The World Health Report. Geneva, Switzerland
WHO, 2013. Facts Sheets. [Online]. Available at
http://www.who.int/mediacentre/factsheets/fs330/en, [accessed May 24, 2016]
WHO, 2015. World Health Statistics. Available at:
http://www.who.int/gho/publications/world_health_statistics/2015/en/ [accessed May 30,
2016]
WHO & UNICEF, 2000. Global Water Supply and Sanitation Assessment 2000 Report.
Geneva: Water Supply and Sanitation Collaborative Council, WHO/UNICEF.
Wholey, J. S., 1983. Evaluation and Effective Public Management. Boston: Little, Brown.
Wholey, J. S., 1987. “Evaluability Assessment: Developing Programme Theory”, pp 35-46
in Using Programme Theory in Evaluation, edited by Brickman, New Directions for
Programme Evaluation No.33, San Francisco: Jossey-Bass.

45
Williamson, Z., Morley, R., Lucas, A. & Carpenter, J., 2011. Propensity Scores: From Naïve
Enthusiasm to Intuitive Understanding. SAGE, Statistical Method in Medical Research,
21(3), p. 273-293.
World Bank. (no date). Rural population (% of total population). [Online]. Available at:
http://www.worldbank.org/data. [accessed 13 June, 2016]

UEA_MSc_IE_2016_Dissertation

Recommended

Recommended

More Related Content

Similar to UEA_MSc_IE_2016_Dissertation

Similar to UEA_MSc_IE_2016_Dissertation (20)

Recently uploaded

Recently uploaded (20)

UEA_MSc_IE_2016_Dissertation