i

Can Improved Pit Latrines Reduce

Childhood Diarrhoea?

New Evidence from Bangladesh

Student Registration Number

3298795

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

Word Count: 11, 985

iii

Table of Contents

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

iv

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

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

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

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Abstract Diarrhoea is the second leading cause of under-five mortality. 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 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 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

neighbor 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

UEA 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 diarrhea also

causes long-term detrimental effects on linear growth and physical cognitive function (Kosek

et al., 2003) and repeated episodes of diarrhea can lead to nutritional problems, lowering of

resistance 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 regions (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 population do not have access to

improved sanitation facilities (UNICEF &WHO, 2015). Numerically, this translates as 2.4

billion people (one in three) using unimproved sanitation, including 946 million people who

practise 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

diarrhea; 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 diarrhea

has not been updated since 1990. Using the latest 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

treatment variables. The chapter then describes the PSM estimation method, including its

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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 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 RCT 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 the children in households without

2

IPL (odds ratio: 0.76, 95% confidence interval 0.58-1.01). Similarly, Baltazar et al. (1988) in

the Philippines estimated a 20% reduction of 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 & 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 child

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 diarrhea in households connected

to a sewerage system (Vasquez & Aksan, 2015). A multilevel regression analysis on the

2007 and 2012 Indonesian DHS data, on the contrary, showed no significant effect of an

improved toilet on diarrhoea (Komarulzaman et al., 2016).

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Some studies have revealed that the type of toilet and sharing facilities influence the

prevalence of child diarrhoea. A cross-sectional survey between June & 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 only 6% of children in

households with flush toilet suffered from diarrhoea compared to 11.2% in the households

with other types of toilet (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) countries, 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 child 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 child 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 child 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, the treatment effect on the lowest socio-

economic groups was the reverse, suggesting the intervention caused diarrhoea, but this was

statistically insignificant. 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, sceptic tank or pit latrine,

ventilated improved pit latrine, compositing 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

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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 of

sanitation intervention on childhood diarrhoea.

1.2 Access 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 more than 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). This urban-rural

6

difference 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 diarrhea 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).

8

This conceptual model explains that integrated WSH programmes are likely to have a

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 data. 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 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 of 15-49

from 17, 300 households were interviewed

(NIPORT et al., 2016).

The 2014 BDHS included three types of questionnaire: 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 of the households that they „usually use‟. 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 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 only 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 child 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 neighbor 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 because of use of 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 neighbors 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 of root the average of 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.

2.3 Limitations

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 are also 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 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. Firstly, the sample is described. Before

depicting the ATT estimates of IPL on childhood diarrhoea, the chapter presents the

descriptive statistics and estimation of PS for the full sample and stratified samples. Lastly,

the results of the robustness checks are presented.

3.1 Description of sample

Table 1 shows the distribution of IPL by children‟s age group, 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

Disaggregates

Households with IPL,

N (column %)

Households without

IPL, N (column %)

Total

households, N

(column %)

Age category

Children <24

months old 1420 (61.08) 3071 (59.42) 4, 491 (59.94)

Children

=>24 months

& <=59

months

905 (38.92)

2097 (40.58)

3,002 (40.06)

Location type Rural 1755 (75.48) 3,364 (65.09) 5119 (68.32)

Urban

570 (24.52)

1804 (34.91)

2374 (31.68)

Diarrhoea

infection Infected 87 (3.74) 282 (5.46) 369 ( 4.92)

Uninfected

2238 (96.26)

4886 (94.54)

7124 (95.08)

Total (row %) 2325 (31.02) 5168 (68.98) 7493

Note: Figures in the parenthesis denote distribution in percentage

This indicates 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

20

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. This statistic has prompted this

dissertation to examine whether any causal association exists between low incidence of

diarrhoea and IPL.

3.2 Descriptive statistics of covariates and propensity score estimation

Appendix 1 (Table: 5.0-5.4) presents the pre-matching descriptive statistics of covariates and

PS logit models for the full and stratified samples, Particularly, the differences in mean

values of most of the selected variables across the treated and non-treated groups of the full

sample (see Table 5.0, column 5) are statistically significant. This indicates that matching

would likely to be successful and 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

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

21

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, and professional and

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 area and households in the divisions other than in Dhaka are

more likely to participate in IPL interventions, while 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 division are highly likely to participate in IPL interventions.

While estimating PS, 25 missing values were generated. Thus, the total sample reduced from

7493 to 7468. 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 sample 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 .8Propensity Score

Untreated Treated

24

3.3 Average treatment effect

The ATT was estimated by using the psmatch2 command on Stata 14. Matching with NN5

and NN3 (three 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

as a reduction of 24% diarrhoea incidence (1.3*100/5.46) for a comparison group with

average 5.46% of children 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

Figures may not add up due to rounding

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 area 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 for significance of differences for the matched group for aggregated and

disaggregated samples are presented in the 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 X

2. The reduction of pseduo-R

2 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 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 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 was different (case-control design).

However, the findings from this research does not match with the findings from Begum et al.

(2013) despite using 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

composite treatment variable may mislead by overestimating or underestimating the effect of

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

the 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

use of broad definition in the research in Nepal. This research, on the contrary, does not find

30

any significant effect on children from 24 months to 59 months old. This is likely to 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 the 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 this very young children, it is assumed 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 this research, the

estimated effect in rural areas is likely to be related to the greater number of treated

households and less diverse improved toilet facilities, whereas the insignificant effect in

urban areas is perhaps to 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.

Estimates from NNM from this research are highly sensitive to hidden bias, thus, it suggests

31

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 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 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 the 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 most of the researches, a recall period

exceeding 48 hours is methodologically problematic (Blum & Feachem, 1983; Boerma et al.,

1991), which can also influence the estimates. Thus, the robustness of the estimates from this

research is subject to the limitations of the research design and dataset.

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 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 broad

definition of improved sanitation in the initial stage of this research design. The balancing

test often failed to provide expected values of T test and level of standard bias after

matching. It is assumed that use of composite definition may have had an influence on past

research results. However, this pre-mature assumption requires to be proved by a

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

Halving the proportion of the population without sustainable access to safe drinking-water

and basic sanitation by 2015 was one of the targets of the MDG 7 -ensure environmental

sustainability. 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 seems not to be high; however, the

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 PRSP for

Bangladesh in order to achieve the global WSH target by 2030 for SDG. 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 quite low compared to the value of a child‟s life and

disease burden cost of diarrhoea.

For the medium to short-term strategy, the GoB 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, TSC is recommended for

maximisation of the impact of an IPL.

As discussed, the estimated ATT may be biased due to confounding variables; therefore,

future research applying a longitudinal design (such pretest and posttest) and incorporating a

34

combination of analytical approaches such as PSM 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 PSM methods evaluated the effect of IPL on diarrhoea among

children below five. The data used for analysis comes from a nation-wide DHS in

Bangladesh.

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 the households with

IPL as compared to those households without IPL. This dissertation also finds considerable

impact heterogeneity of IPL on diarrhoea. Child‟s age and their location are found to be the

important factors for the impact of IPL on diarrhoea. This research finds IPL can

significantly diminish diarrhoea among children below 2 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 the children in the households with IPL in rural area than

those households without.

From the perspective of 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 this research 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 TSC in 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.

It is important to acknowledge that this dissertation used 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 has to

36

be cautiously interpreted. It seems 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.

37

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46

Appendix 1: Pre-matching descriptive statistics of stratified samples Table 5.0: Descriptive statistics of covariates (pre-matching) and PS logit model for full sample

Propensity score logit (1-2) Two-sample t-test (3-5)

Control variables Coefficients Standard error

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

Mean

differences

(3) - (4)

1 2 3 4 5

Pipe water (1=Yes) 1.463*** 0.221 0.026 0.095 -0.069***

Tubewell water

(1=Yes) 2.237*** 0.156 0.950 0.749 0.204***

Age of household

head 0.066*** 0.011 44.633 40.599 4.033***

Squared age of

household head -0.0005*** 0.000 2221.73 1849.72 372.00***

Household size 0.054*** 0.011 6.462 5.866 0.596***

Wealth status binary (Reference category: poorest wealth quintile)

Poorer quintile 0.719*** 0.094 0.192 0.187 0.005

Middle quintile 1.046*** 0.098 0.248 0.168 0.079***

Richer quintile 1.519*** 0.111 0.294 0.162 0.131***

Richest quintile 0.998*** 0.147 0.153 0.215 -0.062***

Partner's occupation binary ( Reference category: unskilled laborer)

Farmer & small

business 0.158** 0.070 0.368 0.309 0.059***

Skilled worker 0.087 0.078 0.237 0.234 0.003

Professional & large

business 0.191 0.122 0.107 0.090 0.016**

Main wall material binary (Reference category: bamboo and mud)

Tin 0.160** 0.080 0.446 0.355 0.091***

Brick & cement -0.237** 0.094 0.380 0.453 -0.073***

Note: Robust standard errors are presented; significance level: ***p<0.01, **p<0.05, *p<0.1

47

Propensity score logit (1-2) Two-sample t-test (3-5)

Control Variables Coefficients Standard

error

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

Mean

differences

(3) - (4)

1 2 3 4 5

Type of cooking material binary (Reference category: gas)

Charcoal &

kerosene 0.872*** 0.124 0.690 0.442 0.248***

Crop residue &

dung 0.450*** 0.141 0.250 0.397 -0.147***

Respondent's education binary (Reference category: below primary)

Completed primary 0.210*** 0.071 0.559 0.492 0.066***

Completed

secondary 0.215 0.132 0.080 0.059 0.021***

Completed higher 0.309** 0.130 0.114 0.103 0.010

Partner's education binary (Reference category: below primary)

Completed primary 0.144** 0.069 0.409 0.348 0.060***

Completed

secondary 0.215* 0.127 0.077 0.059 0.017***

Completed higher 0.106 0.118 0.162 0.141 0.021**

Additional variables (1 = Yes, 0 = No)

Own any cultivable

land 0.083 0.055 0.436 0.410 0.026**

Division binary (Reference category: Dhaka)

Barisal 0.518*** 0.111 0.141 0.105 0.035***

Chittagong 0.399*** 0.098 0.215 0.181 0.033***

Khulna 0.578*** 0.115 0.120 0.104 0.015**

Rajshahi 0.542*** 0.113 0.124 0.121 0.002

Rangpur 0.480*** 0.112 0.129 0.118 0.011

Sylhet 0.189* 0.108 0.146 0.170 -0.024***

Region binary (1 = urban, 0 = rural)

Urban -0.521*** 0.070 0.245 0.349 -0.103***

_Cons -7.011*** 0.358

Observations (N) 7468

Pseudo-R2 0.1544

Log-likelihood -3912.081

Prob>Chi2 0.0000

48

Table 5.1: Descriptive statistics of households with children less than 24 months’ old

Propensity score logit (1-

2) Two-sample t-test (3-5)

Control variables Coefficients Standard

error

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

Mean differences

(3) - (4)

1 2 3 4 5

Pipe water

(1=Yes) 1.697*** 0.356 0.027 0.094 -0.067***

Tubewell water

(1=Yes) 2.579*** 0.261 0.951 0.707 0.243***

Age of household

head 0.054*** 0.018 45.05 41.31 3.746***

Squared age of

household head -0.0004** 0.000 2273.31 1930.07 343.22***

Household size 0.069*** 0.018 6.645 6.037 0.608***

Wealth status binary (Reference category: poorest wealth quintile)

Poorer quintile 0.790*** 0.156 0.192 0.188 0.003

Middle quintile 1.086*** 0.161 0.244 0.178 0.065***

Richer quintile 1.628*** 0.182 0.308 0.165 0.142***

Richest quintile 1.122*** 0.240 0.154 0.213 -0.058***

Main wall material binary (Reference category: bamboo and mud)

Tin 0.1220 0.133 0.464 0.349 0.114***

Brick & cement -0.339** 0.158 0.375 0.477 -0.101***

Type of cooking material binary (Reference category: gas)

Charcoal &

Kerosene 0.879*** 0.199 0.693 0.423 0.270***

Crop residue &

dung 0.485** 0.226 0.246 0.424 -0.178***

Respondent's education binary (Reference category: below primary)

Completed

primary 0.237** 0.118 0.563 0.510 0.053***

Completed

secondary 0.367* 0.203 0.095 0.065 0.029***

Completed higher 0.445** 0.201 0.134 0.113 0.021*

49

Propensity score logit (1-

2) Two-sample t-test (3-5)

Control variables Coefficients Standard

error

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

Mean

differences

(3) - (4)

1 2 3 4 5

Partner's education binary (Reference category: below primary)

Completed

primary 0.119 0.111 0.432 0.382 0.050**

Completed

secondary 0.236 0.197 0.090 0.065 0.024**

Completed higher 0.299* 0.178 0.176 0.146 0.030**

Additional variables (1 = Yes, 0 = No)

Own any

cultivable land 0.055 0.090 0.428 0.408 0.020

Division binary (Reference category: Dhaka)

Barisal 0.484*** 0.174 0.151 0.109 0.042***

Chittagong 0.391** 0.156 0.222 0.175 0.046***

Khulna 0.515*** 0.180 0.127 0.110 0.016

Rajshahi 0.436** 0.180 0.119 0.125 -0.006

Rangpur 0.402** 0.178 0.123 0.118 0.005

Sylhet 0.119 0.176 0.125 0.155 -0.029**

Region (1 = urban, 0 = rural)

Urban -0.508*** 0.114 0.248 0.350 -0.102***

_Cons -7.085*** 0.568

Summary Statistics

Observations (N) 3001

Pseudo-R2 0.1748

Log-likelihood -1515.398

Prob>Chi2 0.0000

50

Table 5.2: Descriptive statistics of households with children between 24 months & 59 months’ old

Propensity score logit (1-2) Two-sample t-test (3-5)

Control variables Coefficients Standard

error

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

Mean

differences

(3) - (4)

1 2 3 4 5

Pipe water (1=Yes) 1.326*** 0.283 0.026 0.096 0.070***

Tubewell water

(1=Yes) 1.989*** 0.197 0.949 0.772 0.172***

Age of household head 0.071*** 0.015 44.36 40.11 4.249***

Squared age of

household head -0.0005*** 0.000 2188.86 1794.86 394.00***

Household size 0.0474*** 0.015 6.346 5.749 0.596***

Wealth status binary (Reference category: poorest wealth quintile)

Poorer quintile 0.693*** 0.119 0.192 0.186 0.006

Middle quintile 1.052*** 0.124 0.251 0.162 0.089***

Richer quintile 1.496*** 0.138 0.285 0.160 0.125***

Richest quintile 0.956*** 0.183 0.152 0.216 0.064***

Partner's occupation binary ( Reference category: unskilled laborer)

Farmer & small

business 0.273*** 0.089 0.381 0.303 0.077***

Skilled worker 0.146 0.100 0.231 0.230 0.000

Professional & large

business 0.269 0.150 0.105 0.090 0.015

Main wall material binary (Reference category: bamboo and mud)

Tin 0.182* 0.101 0.435 0.359 0.076***

Brick & cement -0.173 0.118 0.383 0.437 -0.054***

Type of cooking material binary (Reference category: gas)

Charcoal & kerosene 0.879*** 0.160 0.688 0.455 0.233***

Crop residue & dung 0.437** 0.180 0.253 0.379 -0.126***

Respondent's education binary (Reference category: below primary)

Completed primary 0.248*** 0.085 0.557 0.480 0.076***

Completed secondary 0.195 0.164 0.071 0.055 0.016**

Completed higher 0.228 0.156 0.102 0.097 0.004

51

Propensity score logit(1-

2) Two-sample t-test (3-5)

Control variables Coefficients Standard

error

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

Mean

differences

(3) - (4)

1 2 3 4 5

Additional variables (1 = Yes, 0 = No)

Own any

cultivable land 0.101 0.071 0.461 0.473 0.011

Division (Reference category: Dhaka)

Barisal 0.536*** 0.145 0.134 0.103 0.031***

Chittagong 0.393*** 0.127 0.210 0.185 0.024**

Khulna 0.616*** 0.151 0.115 0.099 0.015

Rajshahi 0.590*** 0.146 0.127 0.118 0.008

Rangpur 0.530*** 0.145 0.133 0.118 0.015

Sylhet 0.210 0.137 0.159 0.181 0.021**

Region (1 = urban, 0 = rural)

Urban -0.529*** 0.090 0.242 0.347 -0.104***

_Cons -6.906*** 0.462

Summary Statistics

Observations (N) 4476

Pseudo-R2 0.1437

Log-likelihood -2392.00

Prob>Chi2 0.0000

Notes: Robust standard errors are presented; significance level ***p<0.01, **p<0.05, *p<0.1

52

Table 5.3: Descriptive statistics of households in rural areas

Propensity score logit (1-

2) Two-sample t-test (3-5)

Control Variables Coefficients Standard

error

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

Mean

differences

(3) - (4)

1 2 3 4 5

Pipe water (1=Yes) 2.120*** 0.335 0.013 0.012 -0.000

Tubewell water (1=Yes) 2.032*** 0.170 0.959 0.810 0.148***

Age of household head 0.064*** 0.013 45.241 40.913 4.327***

Squared age of

household head -0.0004*** 0.000 2284.55 1885.66 398.89***

Household size 0.046*** 0.013 6.521 5.964 0.557***

Wealth status binary (Reference category: poorest wealth quintile)

Poorer quintile 0.852*** 0.103 0.225 0.249 -0.024*

Middle quintile 1.288*** 0.110 0.278 0.192 0.085***

Richer quintile 1.930*** 0.132 0.271 0.113 0.157***

Richest quintile 1.233*** 0.176 0.104 0.082 0.021**

Main wall material binary (Reference category: bamboo & mud)

Tin 0.134 0.090 0.463 0.407 0.055***

Brick & cement -0.337*** 0.110 0.337 0.350 -0.01

Type of cooking material binary (Reference category: gas)

Charcoal & kerosene 0.576*** 0.220 0.709 0.473 0.235***

Crop residue & dung 0.061 0.231 0.267 0.497 -0.230***

Respondent's education binary (Reference category: below primary)

Completed primary 0.073 0.082 0.564 0.515 0.048***

Completed secondary 0.057 0.158 0.078 0.046 0.032***

Completed higher 0.133 0.162 0.093 0.059 0.033***

53

Propensity score logit (1-

2) Two-sample t-test (3-5)

Control Variables Coefficients Standard

error

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

Mean

differences

(3) - (4)

1 2 3 4 5

Partner's education binary (Reference category: below primary)

Completed

primary 0.092 0.079 0.411 0.352 0.059***

Completed

secondary 0.086 0.154 0.071 0.049 0.021***

Completed higher 0.182 0.136 0.143 0.083 0.059***

Additional variables (1 = Yes, 0 = No)

Own any

cultivable land 0.035 0.066 0.429 0.412 0.016

Division binary (Reference category: Dhaka)

Barisal 0.647*** 0.134 0.135 0.112 0.023**

Chittagong 0.513*** 0.120 0.222 0.172 0.050***

Khulna 0.745*** 0.142 0.110 0.101 0.008

Rajshahi 0.575*** 0.138 0.112 0.130 -0.017*

Rangpur 0.692*** 0.133 0.134 0.133 0.001

Sylhet 0.348*** 0.128 0.163 0.183 -0.019*

_Cons -6.475*** 0.429

Summary Statistics

Observations (N) 5118

Pseudo-R2 0.1577

Log-likelihood -2770.990

Prob>Chi2 0.0000

Notes: Robust standard errors are presented; significance level ***p<0.01, **p<0.05, *p<0.1

54

Table 5.4: Descriptive statistics of households in urban areas

Propensity score logit (1-

2) Two-sample t-test (3-5)

Control variables Coefficients Standard

error

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

Mean

differences

(3) - (4)

1 2 3 4 5

Pipe water (1=Yes) 2.355*** 0.477 0.068 0.251 -0.182***

Tubewell water (1=Yes) 3.430*** 0.438 0.921 0.624 0.296***

Age of household head 0.077*** 0.024 42.761 40.013 2.74***

Squared age of

household head -0.000*** 0.000 2028.30 1782.72 245.57***

Household size 0.077*** 0.022 6.280 5.682 0.597***

Partner's occupation binary ( Reference category: unskilled laborer)

Farmer & small business 0.342** 0.153 0.299 0.255 0.043**

Skilled worker 0.182 0.151 0.298 0.316 -0.018

Professional & large

business 0.602*** 0.206 0.181 0.156 0.162

Wealth status binary (Reference category: poorest wealth quintile)

Poorer quintile 0.232 0.249 0.092 0.072 0.020

Middle quintile 0.102 0.227 0.157 0.125 0.032**

Richer quintile 0.341 0.215 0.363 0.252 0.110***

Richest quintile -0.013 0.253 0.303 0.461 -0.158***

Type of cooking material binary (Reference category: gas)

Charcoal & kerosene 1.026*** 0.167 0.633 0.384 0.249***

Crop residue & dung 1.112*** 0.220 0.200 0.211 -0.011

Respondent's education binary (Reference category: below primary)

Completed primary 0.512*** 0.148 0.545 0.450 0.095***

Completed secondary 0.437* 0.253 0.085 0.083 0.002

Completed higher 0.594** 0.234 0.180 0.186 -0.006

55

Propensity score logit (1-

2) Two-sample t-test (3-5)

Control variables Coefficients Standard

error

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

Mean

differences

(3) - (4)

1 2 3 4 5

Partner's education binary (Reference category: below primary)

Completed

primary 0.263* 0.142 0.401 0.342 0.059***

Completed

secondary 0.549* 0.234 0.096 0.078 0.018

Completed higher -0.006 0.220 0.222 0.249 0.026

Additional variables (1 = Yes, 0 = No)

Own any

cultivable land 0.216** 0.106 0.457 0.405 0.052**

Division (Reference category: Dhaka)

Barisal 0.284 0.211 0.157 0.093 0.064***

Chittagong 0.347* 0.185 0.192 0.199 -0.006

Khulna 0.222 0.206 0.149 0.108 0.041***

Rajshahi 0.378* 0.205 0.159 0.104 0.055***

Rangpur -0.006 0.222 0.129 0.118 0.011

Sylhet -0.236 0.218 0.147 0.094 -0.052**

_Cons -8.531*** 0.798

Summary Statistics

Observations (N) 2363

Pseudo-R2 0.1593

Log-likelihood -1094.5897

Prob>Chi2 0.0000

Notes: Robust standard errors are presented; significance level ***p<0.01, **p<0.05, *p<0.1

56

Appendix 2: Histogram of propensity scores for stratified samples Figure: 5.1 Histogram of propensity scores by treatment group of households with children less

than 24 months’ old (KM, bwidth 0.01)

Figure: 5.2 Histogram of propensity scores by treatment group of households with children

between 24 months and 59 months’ old (KM, bwidth 0.07)

0 .2 .4 .6 .8Propensity Score

Untreated Treated: On support

Treated: Off support

0 .2 .4 .6 .8Propensity Score

Untreated Treated: On support

Treated: Off support

57

Figure: 5.3 Histogram of propensity scores by treatment group of households in rural areas

(KM, bwidth 0.005)

Figure: 5.4. Histogram of propensity scores by treatment group of households in urban areas

(KM, bwidth 0.03)

0 .2 .4 .6 .8Propensity Score

Untreated Treated: On support

Treated: Off support

0 .2 .4 .6 .8Propensity Score

Untreated Treated: On support

Treated: Off support

58

Appendix 3: Use of common support for different matching specifications

Table 6: Common support for different matching groups

Common Support

Matching groups

Matching

algorithm Assignments Off-support On-Support Total

All children

Kernel (bwidth

0.09)

Untreated 0 5149 5149

Treated 0 2319 2319

Total 0 7468 7468

NN (5) ( caliper

0.09)

Untreated 0 5149 5149

Treated 0 2319 2319

Total 0 7468 7468

Children <24

months old

Kernel (bwidth

0.01)

Untreated 0 2097 2097

Treated 26 878 904

Total 26 2975 3001

NN (5) ( caliper

0.06)

Untreated 0 2097 2097

Treated 1 903 904

Total 1 3000 3001

Children =>24

months and <=59

months old

Kernel (bwidth

0.07)

Untreated 0 3060 3060

Treated 1 1415 1416

Total 1 4475 4476

NN (3) ( caliper

0.05)

Untreated 0 3060 3060

Treated 1 1415 1416

Total 1 4475 4476

Children in rural

area

Kernel (bwidth

0.005)

Untreated 0 3364 3364

Treated 19 1735 1754

Total 19 5099 5118

NN (5) ( caliper

0.06)

Untreated 0 3364 3364

Treated 262 1492 1754

Total 262 4865 5118

Children in urban

area

Kernel (bwidth

0.03)

Untreated 0 1796 1796

Treated 1 566 567

Total 1 2362 2363

NN (5) ( caliper

0.07)

Untreated 0 1796 1796

Treated 0 567 567

Total 0 2363 2363

59

Appendix 4: Heterogeneous treatment effects of IPL on childhood diarrhoea Table 7: Disaggregated PSM estimates ATT for probability of diarrhoea incidence

Matching Method Sample type Treatment Control Difference S.E.

T-

Stat

Children

<24

months

old

Kernel

(bwidth = 0.01)

Unmatched 0.050 0.074 -0.024 0.010 -2.43

Matched 0.050 0.084 -0.034*** 0.012 -2.91

Nearest Neighbour

(n = 5, caliper =

0.06)

Unmatched 0.050 0.074 -0.024 0.010 -2.43

Matched 0.050 0.087 -0.037*** 0.013 -2.96

Children

=>24 and

<=59

months

old

Kernel

(bwidth = 0.07)

Unmatched 0.029 0.041 -0.012 0.006 -1.96

Matched 0.029 0.033 -0.004 0.007 -0.53

Nearest Neighbour

(n = 3, caliper =

0.05)

Unmatched 0.029 0.041 -0.012 0.006 -1.96

Matched 0.029 0.032 -0.003 0.008 -0.43

Children

living in

rural area

Kernel

(bwidth = 0.005)

Unmatched 0.038 0.054 -0.017 0.006 -2.65

Matched 0.037 0.055 -0.017** 0.006 -2.13

Nearest Neighbour

(n = 5, caliper =

0.0005)

Unmatched 0.038 0.054 -0.017 0.006 -2.65

Matched 0.037 0.054 -0.018** 0.008 -2.11

Children

living in

urban

area

Kernel

(bwidth = 0.03)

Unmatched 0.037 0.055 -0.018 0.011 -1.66

Matched 0.037 0.047 -0.009 0.012 -0.81

Nearest Neighbour

(n = 5, caliper =

0.07)

Unmatched 0.037 0.055 -0.018 0.011 -1.66

Matched 0.037 0.045 -0.008 0.013 -0.64

Notes: Significance level: ***p<0.01, **p<0.05, *p<0.1

Figures may not add up due to rounding

60

Appendix 5: Post-matching covariate balance (individual t-test) for full sample

and stratified samples

Table 8.0: Covariate balance - individual t-test for full sample (matched), KM, bwidth 0.09

Control variables

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

% Bias

%

Reduction

bias

Differences

(1) - (2)

t-values

1 2 3 4 5

Pipe water (1=Yes) 0.026 0.029 -1.1 96.1 -0.56

Tubewell water

(1=Yes) 0.950 0.941 2.5 95.9 1.27

Age of household head 44.64 44.42 1.5 94.6 0.49

Squared age of

household head 2222.6 2200.1 1.6 94.6 0.49

Household size 6.461 6.463 -0.1 99.5 -0.03

Wealth status binary (Reference category: poorest wealth quintile)

Poorer quintile 0.192 0.202 -2.6 -110.7 -0.88

Middle quintile 0.249 0.248 0.2 99.0 0.07

Richer quintile 0.294 0.2690 6.1 80.8 1.91*

Richest quintile 0.152 0.165 -3.6 78.0 -1.28

Partner's occupation binary ( Reference category: unskilled laborer)

Farmer & small

business 0.368 0.360 1.8 85.4 0.61

Skilled worker 0.238 0.235 0.7 20.8 0.23

Professional & large

business 0.107 0.115 -2.8 49.9 -0.90

Main wall material binary (Reference Category: bamboo and mud)

Tin 0.446 0.435 2.4 87.2 0.80

Brick & cement 0.379 0.388 -1.8 87.7 -0.63

Type of cooking material binary (Reference category: gas)

Charcoal & kerosene 0.691 0.694 -0.7 98.7 -0.23

Crop residue & dung 0.250 0.245 1.0 96.8 0.37

Respondent's education binary (Reference category: below primary)

Completed primary 0.560 0.561 -0.3 98.1 -0.09

Completed secondary 0.079 0.076 1.1 85.8 0.36

61

Completed higher 0.114 0.116 -0.4 87.5 -0.15

Control variables

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

% Bias

%

Reduction

bias

Differences

(1) - (2)

t-values

1 2 3 4 5

Partner's education binary (Reference category: below primary)

Completed

primary 0.410 0.407 0.6 95.2 0.20

Completed

secondary 0.077 0.078 -0.5 93.3 -0.15

Completed higher 0.161 0.163 -0.6 89.6 -0.20

Additional variables (1 = Yes, 0 = No)

Own any

cultivable land 0.436 0.445 -1.7 68.5 -0.57

Division binary (Reference category: Dhaka)

Barisal 0.141 0.136 1.5 86.6 0.48

Chittagong 0.214 0.200 3.4 57.9 1.13

Khulna 0.119 0.122 -0.7 85.8 -0.23

Rajshahi 0.124 0.126 -0.7 46.3 -0.23

Rangpur 0.129 0.131 -0.6 80.5 -0.21

Sylhet 0.147 0.155 -2.3 64.6 -0.81

Region binary (1 = urban, 0 = rural)

Urban 0.244 0.250 -1.3 94.3 -0.47

Notes: Significance level ***p<0.01, **p<0.05, *p<0.1

62

Table 8.1: Covariate balance - individual t-test for households with children less than 24

months old (matched group), KM, bwidth 0.01

Control Variables

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

% Bias

%

Reduction

bias

Differences

(1) - (2)

t-values

1 2 3 4 5

Pipe water (1=Yes) 0.028 0.031 -1.3 95.5 -0.37

Tubewell water

(1=Yes) 0.949 0.946 1.0 98.5 0.35

Age of household head 44.59 44.50 0.6 97.8 0.11

Squared age of

household head 2227.2 2216.1 0.7 97.0 0.14

Household size 6.488 6.621 -4.7 78.1 -0.92

Wealth status binary (Reference category: poorest wealth quintile)

Poorer quintile 0.198 0.200 -0.5 44.2 -0.11

Middle quintile 0.250 0.247 0.7 95.3 0.15

Richer quintile 0.289 0.2750 3.3 90.3 0.64

Richest quintile 0.158 0.173 -4.0 73.8 -0.78

Main wall material binary (Reference category: bamboo & mud)

Tin 0.451 0.436 2.9 87.5 0.60

Brick & cement 0.384 0.397 -2.6 87.3 -0.55

Type of cooking material binary (Reference category: gas)

Charcoal & kerosene 0.686 0.685 0.2 99.7 -0.04

Crop residue & dung 0.251 0.252 -0.3 99.3 -0.06

Respondent's education binary (Reference category: below primary)

Completed primary 0.568 0.564 0.8 92.5 0.17

Completed secondary 0.093 0.089 1.4 86.4 0.28

Completed higher 0.125 0.138 -4.1 37.50 0.40

63

Control variables

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

% Bias

%

Reduction

bias

Differences

(1) - (2)

t-values

1 2 3 4 5

Partner's education binary (Reference category: below primary)

Completed

primary 0.435 0.442 -1.5 85.4 -0.31

Completed

secondary 0.087 0.082 1.9 79.2 0.39

Completed higher 0.171 0.179 -2.1 74.0 -0.43

Additional variables (1 = Yes, 0 = No)

Own any

cultivable land 0.425 0.420 -1.1 73.3 0.23

Division binary (Reference category: Dhaka)

Barisal 0.144 0.138 1.9 84.9 0.38

Chittagong 0.216 0.209 1.7 84.7 0.35

Khulna 0.129 0.139 -2.9 44.3 -0.58

Rajshahi 0.120 0.122 -0.6 65.4 -0.13

Rangpur 0.124 0.131 -2.1 -35.5 -0.44

Sylhet 0.129 0.132 -0.6 92.5 -0.14

Region (1 = urban, 0 = rural)

Urban 0.252 0.254 -0.3 98.7 -0.06

64

Table 8.2: Covariate balance - individual t-test for households with children between 24

months and 59 months old (matched group), KM, bwidth 0.07

Control Variables

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

% Bias

%

Reduction

bias

Differences

(1) - (2)

t-values

1 2 3 4 5

Pipe water (1=Yes) 0.026 0.029 -1.3 95.7 -0.50

Tubewell water

(1=Yes) 0.949 0.942 2.0 96.1 0.8

Age of household head 44.38 44.37 0.1 99.8 0.02

Squared age of

household head 2191.0 2192.7 -0.1 99.6 -0.03

Household size 6.342 6.387 -1.6 92.4 -0.39

Wealth status binary (Reference category: poorest wealth quintile)

Poorer quintile 0.192 0.200 -2.2 -46.5 -0.57

Middle quintile 0.252 0.250 0.5 97.7 0.13

Richer quintile 0.285 0.266 4.6 85.0 1.12

Richest quintile 0.151 0.161 -2.6 84.6 -0.74

Partner's occupation binary ( Reference category: unskilled laborer)

Farmer & small

business 0.381 0.380 0.3 98.2 0.08

Skilled worker 0.238 0.235 0.7 20.8 0.23

Professional & large

business 0.231 0.225 1.5 -589.2 0.40

Main wall material binary (Reference category: bamboo & mud)

Tin 0.435 0.429 1.2 92.1 0.33

Brick & cement 0.383 0.386 -0.7 93.8 -0.19

Type of cooking material binary (Reference category: gas)

Charcoal & kerosene 0.689 0.699 -2.0 96.0 -0.55

Crop residue & dung 0.253 0.241 2.5 91.0 0.70

Respondent's education Binary (Reference category: below primary)

Completed primary 0.556 0.559 -0.6 96.2 -0.16

Completed secondary 0.070 0.070 0.0 99.8 0.00

Completed higher

65

Control Variables

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

% Bias

%

Reduction

bias

Differences

(1) - (2)

t-values

1 2 3 4 5

Additional variables (1 = Yes, 0 = No)

Own any

cultivable land 0.440 0.454 -2.8 53.9 -0.73

Division (Reference category: Dhaka)

Barisal 0.134 0.138 -1.2 87.9 -0.30

Chittagong 0.209 0.195 3.7 37.5 0.97

Khulna 0.115 0.114 0.4 92.4 0.09

Rajshahi 0.127 0.125 0.7 78.0 0.19

Rangpur 0.133 0.133 -0.1 98.2 -0.02

Sylhet 0.160 0.170 -2.7 53.3 -0.72

Region (1 = urban, 0 = rural)

Urban 0.242 0.247 -1.1 95.3 -0.31

Notes: Significance level ***p<0.01, **p<0.05, *p<0.1

66

Table 8.3: Covariate balance - individual t-tests for households in rural areas (matched

group), KM, bwidth 0.005

Control variables

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

% Bias

%

Reduction

bias

Differences

(1) - (2)

t-values

1 2 3 4 5

Pipe water (1=Yes) 0.0132 0.0128 0.4 -27.8 -0.11

Tubewell water

(1=Yes) 0.959 0.958 0 99.9 0.01

Age of household head 45.08 44.84 1.6 94.4 0.46

Squared age of

household head 2269.0 2244.1 1.7 93.8 0.47

Household size 6.479 6.422 2.0 89.7 -0.55

Wealth status binary (Reference category: poorest wealth quintile)

Poorer quintile 0.227 0.247 -4.7 15.7 -1.40

Middle quintile 0.281 0.273 1.8 91.3 0.49

Richer quintile 0.263 0.2580 1.3 96.8 0.34

Richest quintile 0.105 0.103 0.6 91.6 0.17

Main wall material binary (Reference category: bamboo and mud)

Tin 0.458 0.473 2.9 74.1 -0.85

Brick & cement 0.341 0.330 2.3 11.4 0.67

Type of cooking material binary (Reference category: gas)

Charcoal & kerosene 0.707 0.718 -2.3 95.3 -0.72

Crop residue & dung 0.268 0.257 2.3 95.2 0.74

Respondent's education binary (Reference category: below primary)

Completed primary 0.565 0.577 -2.4 76.0 -0.70

Completed secondary 0.076 0.066 4.3 67.2 1.19

Completed higher 0.091 0.091 -0.2 98.1 -0.07

67

Control variables

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

% Bias

%

Reduction

bias

Differences

(1) - (2)

t-values

1 2 3 4 5

Partner's education binary (Reference category: below primary)

Completed

primary 0.411 0.414 -0.6 94.9 -0.18

Completed

secondary 0.070 0.070 0.3 96.2 0.09

Completed higher 0.141 0.144 -1.0 94.7 -0.27

Additional variables (1 = Yes, 0 = No)

Own any

cultivable land 0.427 0.437 -2.1 38.3 -0.61

Division binary (Reference category: Dhaka)

Barisal 0.134 0.123 3.2 54.8 0.93

Chittagong 0.219 0.206 3.3 73.9 0.93

Khulna 0.111 0.108 1.1 59.6 0.33

Rajshahi 0.112 0.119 -1.9 64.2 -0.58

Rangpur 0.134 0.145 -3.4 -733.7 -0.97

Sylhet 0.165 0.174 -2.3 56.3 -0.68

68

Table 8.4: Covariate balance - individual t-tests for households in urban areas (matched

group), KM, bwidth 0.03

Control Variables

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

% Bias

%

Reduction

bias

Differences

(1) - (2)

t-values

1 2 3 4 5

Pipe water (1=Yes) 0.067 0.068 -0.4 99.3 -0.09

Tubewell water

(1=Yes) 0.922 0.917 1.1 98.6 0.27

Age of household head 42.73 43.27 -3.9 80.1 -0.63

Squared age of

household head 2025.6 2084.8 -4.4 75.5 -0.70

Household size 6.249 6.265 -0.6 97.2 -0.10

Partner's occupation binary ( Reference category: unskilled laborer)

Farmer & small

business 0.298 0.287 2.5 74.1 0.42

Skilled worker 0.298 0.303 -1.2 70.8 -0.19

Professional & large

business 0.181 0.199 -4.6 30.9 -0.75

Wealth status binary (Reference category: poorest wealth quintile)

Poorer quintile 0.093 0.088 1.7 77.9 0.28

Middle quintile 0.159 0.159 -0.2 98.3 -0.03

Richer quintile 0.363 0.325 8.3 65.7 1.36

Richest quintile 0.300 0.333 -7.0 79.3 -1.20

Type of cooking material binary (Reference category: gas)

Charcoal & kerosene 0.636 0.633 0.5 99.0 0.09

Crop residue & dung 0.199 0.201 -0.5 82.7 -0.09

Respondent's education binary (Reference category: below primary)

Completed primary 0.547 0.542 1.0 94.1 0.18

Completed secondary 0.083 0.093 -3.6 -14306.7 -0.60

Completed higher 0.180 0.18 0.20 84.10 0.04

69

Control Variables

Improved

pit latrine

(Mean)

Unimproved

pit latrine

(Mean)

% Bias

%

Reduction

bias

Differences

(1) - (2)

t-values

1 2 3 4 5

Partner's education binary (Reference category: below primary)

Completed

primary 0.402 0.381 4.5 64.6 0.74

Completed

secondary 0.097 0.113 -5.9 9.7 -0.91

Completed higher 0.219 0.227 -1.9 71.8 -0.33

Additional variables (1 = Yes, 0 = No)

Own any

cultivable land 0.459 0.476 -3.4 70.0 -0.56

Division binary(Reference category: Dhaka)

Barisal 0.159 0.150 2.5 87.9 0.38

Chittagong 0.190 0.188 0.5 73.6 0.09

Khulna 0.148 0.158 -3.2 73.3 -0.49

Rajshahi 0.160 0.166 -1.6 90.3 -0.25

Rangpur 0.116 0.118 -0.7 90.8 -0.12

Sylhet 0.095 0.089 1.9 88.1 0.36

70

Appendix 6: Summary statistics of matching quality for stratified samples

Table 9: Summary statistics of matching quality for stratified samples

Stratifications

Matching

algorithm Sample

Pseudo-

R2 LR X2 p>X2

Mean

bias

Median

bias

Children <24

months' old

Kernel

(bwidth

=0.01)

Unmatched 0.175 641.19 0.000 18.3 12.6

Matched 0.002

4.32

1.000

1.7

1.4

Nearest

Neighbour

( n=5,

caliper

0.06)

Unmatched 0.175 641.19 0.000 18.3 12.6

Matched 0.005 13.23

0.988 3.2 2.9

Children

=>24 and

<=59 months

olds

Kernel

(bwidth

0.09)

Unmatched 0.143 799.03 0.000 16.5 15.5

Matched 0.002 5.94 1.000 1.5 1.2

Nearest

Neighbour

( n=3,

caliper

0.05)

Unmatched 0.143 799.03 0.000 16.5 15.5

Matched 0.003 12.86 0.990 2.1 1.7

Children

living in rural

area

Kernel

(bwidth

=0.005)

Unmatched 0.157 1036.26 0.000 16.2 11.7

Matched 0.002 10.93 0.996 1.9 2.0

Nearest

Neighbour

( n=5,

caliper

0.0005)

Unmatched 0.157 1036.26 0.000 16.2 11.7

Matched 0.004 16.32 0.928 2.5 1.7

Children

living in

urban area

Kernel

(bwidth

=0.03)

Unmatched 0.158 412.44 0.000 17.5 11.8

Matched 0.004 5.92 1.000 2.5 1.9

Nearest

Neighbour

( n=5,

caliper

0.07)

Unmatched 0.158 412.44 0.000 17.5 11.8

Matched 0.004 6.22 1.000 2.6 2.5

71

Appendix 7: Sensitivity analysis for heterogeneous effects

Table 10: Sensitivity analysis: Wilcoxon’s signed rank test for stratified samples

Gamma Range of significance level

Lower bound Upper bound

Children < 24

months of age

1 0.998 0.998

1.1 0.988 0.999

1.2 0.954 0.999

1.3 0.879 0.999

1.4 0.753 0.999

1.5 0.593 0.999

1.6 0.427 0.999

1.7 0.282 1.000

1.8 0.171 1.000

1.9 0.090 1.000

2 0.050 1.000

Children =>24 and

<=59 months of age

1 0.611 0.611

1.1 0.400 0.793

1.2 0.229 0.904

1.3 0.116 0.960

1.4 0.053 0.985

1.5 0.022 0.995

1.6 0.008 0.998

1.7 0.003 0.999

1.8 0.001 0.999

1.9 0.000 0.999

2 0.000 0.999

Notes: Gamma denotes log odds of different assignment due to unobserved factors

72

Gamma

Range of significance level

Lower bound Upper bound

Children living in

urban area

1 0.753 0.753

1.1 0.604 0.865

1.2 0.453 0.932

1.3 0.319 0.967

1.4 0.213 0.985

1.5 0.135 0.993

1.6 0.080 0.997

1.7 0.040 0.998

1.8 0.020 0.995

1.9 0.010 0.999

2 0.008 0.999

Notes: Gamma denotes log odds of different assignment due to unobserved factors

Sensitivity analysis for the samples of rural area did not reveal any range of significance level.