abstract · web viewwater insecurity is becoming a growing global concern. it is estimated that...
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Subjective Well-being Effects of Coping Cost: Evidence from Household Water Supply in
Kathmandu Valley, Nepal
Abstract
Coping with unreliable water supply – in terms of quantity and quality – can impose significant
costs on households as they are required to spend more resources on coping strategies such as
purchasing, storing, treating, pumping, and collecting. Does increased coping cost affect people’s
subjective well-being? We answer this question using unique panel data on urban households in
the Kathmandu Valley in Nepal from 2001 and 2014. Using previously computed coping cost
estimates, we examine the association between total coping cost and both evaluative and hedonic
measures of subjective well-being. To understand the underlying mechanisms, we examine the
detailed composition of household coping cost and also the correlation between coping cost and
time use. We take necessary steps to address potential endogeneity in coping cost and subjective
well-being. Our main finding is that increased coping cost is positively correlated with evaluative
well-being but not with hedonic well-being. Exploration of mechanisms suggests that this may
be owing to spending on storage tanks and treatment systems, which are likely to be perceived as
long-term ‘investments’ and not ‘costs’. Further, increased coping cost significantly reduces time
spent on collecting water, which may also explain the positive correlation between coping cost
and evaluative well-being.
Keywords: urban water supply, coping cost, subjective well-being, Nepal
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Authorship statement
NC designed relevant modules of the survey, monitored data collection, performed the data
analysis, and wrote the full paper. YJC designed relevant modules of the survey, monitored data
collection, and provided inputs on data analysis and paper framing. YG provided the coping cost
estimates, provided inputs in designing the survey, and monitored data collection.
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Acknowledgments
We gratefully acknowledge the financial support provided by the Institute of Water Policy,
National University of Singapore. We are immensely thankful to Dale Whittington, Wu Xun,
Jane Zhao, and Aditi Raina for their valuable comments on an earlier version of the manuscript.
We also thank Bal Kumar K. C. and Bhim Suwal for their support in the data collection process.
We wish to acknowledge the excellent research assistance provided by Ms. Luu Diu Khue and
Mr. Venu Gopal Mothkoor. The findings, interpretations, conclusions, and any errors are entirely
those of the authors.
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1. Introduction
Water insecurity is becoming a growing global concern. It is estimated that nearly 5 billion
people, equivalent to two-thirds of world population, live in water insecure areas and projections
suggest that these numbers will only worsen with climate change (Rodell et al., 2018;
Vörösmarty et al., 2000; Vörösmarty et al., 2010). Climate change interacted with population
growth, economic changes, deteriorating infrastructure, and poor governance will further
exacerbate water insecurity both in terms of quantity and quality of water (Howard et al., 2016;
Jury & Vaux, 2007; Sadoff et al., 2015). Nepal, the context of this study, is among the countries
most vulnerable to water insecurity owing to increasing climate risks and poor water governance
(Howard et al., 2016; Immerzeel et al., 2010; Katuwal & Bohara, 2011; Udmale et al., 2016).
Negative spillovers from water insecurity include a rise in water-related disease burden, conflicts
between water users, and food insecurity (Jury & Vaux, 2007; World Health Organization
(WHO) & Department for International Development (DFID), 2009). Managing and coping with
water insecurity is therefore of immense economic, social, and policy significance as it concerns
the very survival of people.
Coping with water insecurity can impose substantial cost on households as they need to spend
more resources to obtain water, which are both monetary and non-monetary (Cook et al., 2016).
Households incur capital and financial costs in building storage facilities, installing filtration and
treatment systems to avert water-related diseases, purchasing water from private vendors and
also incur non-monetary costs in the form of increased time spent on collecting water (Cook et
al., 2016; Gurung et al., 2017; Pattanayak et al., 2005). It is estimated that water insecurity costs
the global economy around US$500 billion annually, which includes productivity loss owing to
increased water collection time and health costs owing to poor quality of water (Sadoff et al.,
2015).
Even though the economic implications of coping with water insecurity are significant, little
attention has been given to the subjective well-being effects of these coping costs. Limited
evidence suggests that economic transactions that people have to engage in to cope with
inadequate water supply or insufficient access to water distribution systems have negative
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psychological effects (Wutich & Ragsdale, 2008). This resonates with recent behavioural
literature which finds that scarcity more generally creates cognitive load, which means that
people allocate most of their attentional resources where scarcity is salient thus leaving less for
other tasks (Shah et al., 2012). An implication of this in the context of water insecurity is that
individuals might spend more monetary resources and time on coping mechanisms which takes
away from other tasks and investments such as productive work, human capital investments, or
simply leisure. These trade-offs can in turn affect individual subjective well-being.
This paper has two main empirical aims. First, we explicitly examine the association between
water-related household coping cost and subjective well-being. To do this we use coping cost
estimates from Gurung et al. (2017), which cover the full range of coping strategies that
households in the Kathmandu Valley engage in namely – purchasing, storage, treatment,
pumping, and collecting – and examine the effect of total coping cost on both evaluative and
hedonic aspects of well-being. An important contribution of this analysis is the data and context.
In particular, we use panel data from 2001 and 2014 on urban households in the Kathmandu
Valley, Nepal for our analysis. The context of Kathmandu is unique in the sense that urban water
supply between 2001 and 2014 worsened owing to poor governance, deteriorating infrastructure,
and significant population increase. Further, our dataset includes a rich set of variables allowing
us to control for confounding factors and address potential endogeneity associated with omitted
variables and reverse causality between subjective well-being and coping cost. And second, we
examine the pathways affecting changes in subjective well-being using the detailed composition
of household coping cost and a time-use component. This underscores the effects of water
insecurity and associated coping cost on allocation of time, which hitherto is under-explored in
the literature.
Our analysis departs from earlier literature in two important ways. First is a methodological
departure. Much of the earlier literature linking improvements in water supply or coping with
unreliable water supply and welfare has relied on stated preferences, mainly, people’s
willingness-to-pay (Casey et al., 2006; Pattanayak et al., 2005; Raje et al., 2002; Whittington et
al., 2002) and revealed preferences such as hedonic pricing (North & Griffin, 1993; Rosiers et
al., 1999) as the welfare criteria. Both stated and revealed preference methods rest on the
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assumption that each individual is endowed with stable and coherent preferences and satisfying
these preferences maximizes one’s decision utility and consequently enhances welfare. Thus,
choices made provide all the information required to infer utility outcomes (Frey & Stutzer,
2002). A limitation and criticism of preference-based methods in evaluating public policies is
that they require individuals to be able to predict their future utility. Studies by behavioural
scientists and economists have found that there is often a risk of mis-predicting one’s decision
utility, or in other words, under- or over-estimating gains from a particular decision (Kahneman
& Krueger, 2006; Kahneman & Sugden, 2005; Kahneman & Thaler, 2006). Further, the notion
of welfare as referring only to decision utility is also questioned. The underlying argument is that
preferences are not stable and tend to be influenced by past and day-to-day experiences.
Therefore, in addition to decision utility, there is also a need to base economic appraisal on
“experienced utility” (Kahneman & Krueger, 2006; Kahneman & Sugden, 2005; Kahneman &
Thaler, 2006). In this paper, we use subjective well-being as the welfare criterion, which includes
experienced utility.
Three aspects of subjective well-being are distinguished in the literature – hedonic, evaluative,
and eudemonic (Deci & Ryan, 2006; Ryan & Deci, 2001; Steptoe et al., 2014). Experienced
utility is equated with the hedonic aspect and refers to experiences of positive and negative
emotions such as happiness, smiles, depression, anxiety, pain, and pleasure (Diener, 1984;
Kahneman & Krueger, 2006). Hedonic measures of well-being are time-inclusive and generally
focus on shorter periods and momentary measures such as feelings in the past week, day, or
current feelings (Tov & Au, 2013). Evaluative well-being refers to global or overall evaluation of
one’s life. The most common measure used in the literature is an ordinal life satisfaction scale.
Temporal dimensions such as life satisfaction in the past week, month, or year are also used as
evaluative measures of well-being. Further, it can also be broken down into specific domains
such as satisfaction with one’s work, family, health and so on (Tov & Au, 2013). A study by
Fujita and Diener (2005) finds life satisfaction to be quite stable over time and therefore argues
that it is a reliable measure.1 Eudemonic well-being goes beyond happiness and is concerned
with self-realization and purpose and meaning of one’s life (Deci & Ryan, 2006; Ryan & Deci,
2001). Measures include ordinal scale questions on best possible life, sense of self-worth, social 1 In a 17-year longitudinal study, Fujita and Diener (2005) find that 76% of the respondents do not report any significant change in their life satisfaction from the first 5 years to the last 5 years.
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integration, autonomy and so on (Cooke et al., 2016). Our focus in this paper is on the evaluative
and hedonic subjective well-being driven largely by the data we collected. Lucas and Diener
(2008) find that hedonic measures are distinct from the evaluative measures. Emotions and
feelings are more likely to get influenced by immediate life events but evaluation of life
satisfaction is a combination of all experiences and long-term well-being.
The second departure is our focus on water insecurity in a less developed country resulting from
deteriorating water infrastructure, poor urban governance, and worsening access to water. Recent
years have seen growing literature on subjective well-being effects of climate change and
environmental pollution. While climate change affects both evaluative and hedonic well-being,
these studies do not delve deeper into the consequences of climate change such as water
insecurity that might explain the associations (Maddison & Rehdanz, 2011). Studies on
environmental pollution and subjective well-being almost exclusively focus on air pollution with
little or no attention given to water pollution or water quality (Ferrer-i-Carbonell & Gowdy,
2007; Welsch, 2006; Zhang et al., 2017). Evidence examining the effect of access to water on
subjective well-being is very limited. A recent study by Mahasuweerachai and Pangjai (2017)
finds that connecting rural households in Cambodia, China, and the Philippines to piped water
connections increases their happiness. The underlying mechanisms are convenience and time
saving. Devoto et al. (2012) find that in Morocco piped water connections enhance subjective
well-being of households because they increase time available for leisure and result in fewer
intra- and inter-household conflicts over water. Both studies examine the effect of improved
access to water. In contrast, the context of our study is increased water insecurity in the
Kathmandu Valley, which to our knowledge is the first study to do so.
The main empirical result of this study is that increased coping cost is positively correlated with
evaluative well-being measured as ordinal overall life satisfaction but not with hedonic well-
being operationalized using a mental health inventory (MHI-5) score. Exploration of underlying
mechanisms suggests that this may be owing to spending on storage tanks, treatment systems,
and pumps which are likely to be perceived as long-term ‘investments’ and not ‘costs’. Further,
increased coping cost significantly decreases time spent collecting water, which may also explain
the positive correlation between coping cost and life satisfaction.
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The rest of the paper is organised as follows. Section 2 presents the conceptual framework
linking coping cost and subjective well-being. Section 3 provides details on the data and
methodology including study setting, survey design, and data analysis. Results and discussion
are presented in Section 4 and Section 5 concludes.
2. Conceptual framework
The objective of our conceptual framework is two-fold. First, to establish a theoretical link
between coping cost and subjective well-being. And second, to motivate our data analysis.
Behavioural literature underscores that coping behaviour is a process (Lazarus, 1966, 1991,
1993, 1999; Lazarus & Folkman, 1984). Individuals undertake a process of cognitive appraisal of
the problem they need to cope with, which in the context of this study is water insecurity. The
primary aspect of this process is an evaluation of whether water insecurity is relevant to their
well-being and if it is, in what ways. And the secondary aspect is an evaluation of the actual
coping strategies such that either the harm from water insecurity is prevented or overcome, or,
benefits from coping strategies are increased. These coping strategies are in turn conditional on
individual capabilities and resources. Coping strategies may also differ if the problem (water
insecurity) is coupled with uncertainty (Monat, 1976; Monat et al., 1972). Uncertainty may arise
either when individuals cannot anticipate the timing of the problem or the nature of the problem.
For instance, individuals may not be able to anticipate exactly when they would receive water
from their taps or cannot anticipate the pressure and quality of water from the taps even if supply
is regular. Coping can take varying forms such as altering the environment or seeking more
information, which are problem-focused, or accepting the situation, which is emotion-focused
(Folkman et al., 1986; Lazarus & Folkman, 1984). Coping cost, that is, allocating more resources
to cope with water insecurity can be categorized as problem-focused coping behaviour.
When individuals perceive that they cannot adequately cope with the problem (water insecurity)
it can negatively affect their hedonic well-being (Folkman et al., 1986; Lazarus, 1966). On the
other hand, it has also been found that uncertainty makes individuals engage more in coping
behaviour and consequently assuage negative well-being effects (Monat, 1976). Studies done in
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other contexts such as poverty, health and disability, and conflict have found that over time
individuals learn to cope and adapt and exhibit lower stress and negative emotions and even
report positive overall life satisfaction (Graham, 2011; Steptoe et al., 2014). Therefore, the
evaluative and hedonic well-being responses to allocating more resources to cope with water
insecurity could indeed be positive.
Motivated by the behavioural literature, we examine the association between coping cost and
evaluative and hedonic aspects of subjective well-being. The exact direction of the association
between coping cost and subjective well-being is likely to be conditional on baseline coping
behaviour, household budget constraint, and other household characteristics. Households might
consciously decide to make certain coping decisions such as spending on water storage but not
others such as spending more time collecting water depending on their full budget constraint,
which includes both income and time. The objective function of the household is to optimize
coping cost such that it maximizes hedonic and evaluative well-being.2 Below, we express
subjective well-being (both hedonic and evaluative) as a function of its determinants.
SubjectiveWellbeing= f (copingcosts ,budget , other household characteristics ) Eq(1)
There are at least three challenges in estimating the above relationship. First, coping cost could
be endogenous to household specific unobserved characteristics resulting in a biased estimate.
For example, a wealthier household might choose to live in an area where coping cost is lower
and they might also be happier. Second, there is potential reverse causality running from
subjective well-being to coping cost. For example, individuals with severe psychological stress
might have lower income and hence have less to spend on coping cost. And third, there might be
measurement errors in operationalizing subjective well-being. We resolve these issues by
controlling for a rich set of individual and household-level variables and fixed effects as is
explained in Section 3.3.
2 In comparison, the utility maximization framework based on revealed preferences considers subjective well-being to be in the utility function implying that people choose to spend on gaining more happiness versus consuming other goods subject to a budget constraint (Becker et al., 2008). A detailed discussion on the relationship between utility and subjective well-being is beyond the scope of this analysis.
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3. Data and methods
3.1 Study setting
Our study was conducted in the Kathmandu Valley, the most urbanized region of Nepal. The
total population of the valley is estimated to be 2.42 million. The percentage change in
population between 2001 and 2011 for the three largest districts of the valley – Kathmandu,
Bhaktapur, and Lalitpur – was 61.2%, 35.1%, and 38.6% respectively (Government of Nepal,
2011). The three districts have experienced an unprecedented increase in population owing
mainly to internal migration. Limited land area and resources in the valley has resulted in
unplanned and unsustainable urbanization and severe shortage of water. While 85% of the
population reports using an improved drinking water source, only 12% has access to high-quality
water supply (Asian Development Bank (ADB), 2015).
Between 2011 and 2016, the estimated demand for water in the Kathmandu Valley was expected
to increase from 327.1 million litres per day (MLD) to 415.5 MLD. As against this, the supply
capacity of Kathmandu Upatyaka Khanepani Limited (KUKL), the public utility responsible for
supplying potable water to the valley, was estimated to be 151.19 MLD in 2013. This also varied
by seasons and the supply capacity was estimated to be 115 MLD in the wet season and 69 MLD
in the dry season. In 2016, the supply deficit was estimated to be about 210 MLD (Udmale et al.,
2016). Yet another issue is old and deteriorated pipe network, which results in up to 40%
wastage before the water reaches the consumer (Katuwal & Bohara, 2011).
Households rely on groundwater to cope with the acute water shortage. Groundwater is also
extracted by private water tankers resulting in over-exploitation and depletion of groundwater
levels by up to 8 meters in some parts of the valley further aggravating water shortage (Tandan,
2016). Moreover, groundwater depletion in Nepal has been exacerbated by climate change (Xu
et al., 2009). Therefore, households have to cope using a variety of strategies such as purchasing
from private vendors, collecting from public taps, investing in water storage tanks and filtration
devices and so on (Gurung et al., 2017; Pattanayak et al., 2005). Kathmandu Valley thus suffers
from chronic water shortage, which has worsened in recent years, making it a unique setting to
study the effects of coping cost on subjective well-being.
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3.2 Survey design and implementation
The survey data for our study was collected in the Kathmandu Valley, Nepal in 2001 and 2014.
A total of 1500 households residing in five municipalities of the Kathmandu Valley –
Kathmandu, Lalitpur, Bhaktapur, Kirtipur, and Madhyapur – were interviewed in both waves.
Figure 1 shows a map of the study area with the dots representing surveyed households.
Households were selected using a multi-stage clustered sampling procedure. Clusters were
located using aerial maps provided by the Central Bureau of Statistics for the 1996/97 World
Bank Living Standard Measurement Survey for Kathmandu. In three of the five municipalities in
the Kathmandu Valley (Kathmandu, Lalitpur, and Bhaktapur), a previously conducted complete
enumeration of all households was used as the sample frame (SILT Consultants and
Development Research and Training Center, 1999). In Kirtipur and Madhyapur, the 1991
population census was used as the sampling frame.
Wards were then selected from the sampling frame on the basis of a probability-proportional-to-
size sampling approach that ensured households had an equal likelihood of being included in the
sample. After a ward was selected for inclusion in the sample, sub-wards were drawn randomly.
The final sample consisted of 60 clusters of 25 households each covering all five municipalities
in the Kathmandu Valley. If a cluster was selected for inclusion in the sample, then respondents
from all 25 households in that cluster were interviewed for the study. Because probability-
proportional-to-size sampling depends on the size of the population, some wards had more than
one cluster in the final sample.
The 2014 survey was a re-survey of the 2001 households. If it was not possible to locate the
original household, a nearby household in the same cluster as in 2001 was selected for the
interview. When the household head from 2001 was missing, the present head or a responsible
member of the house was interviewed instead. A total of 1500 households were surveyed in 2001
and the 2014 survey attempted to find all the original 1500 households. However, only 61.8% of
the households could be re-surveyed. Reasons for attrition were inability to find the household,
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sale of old household, or migration. Current residents or immediate neighbours were interviewed
as substitutes to make up for the attrition.
A sub-set of 850 households was surveyed for the health and time use module in the 2014 round
only. Sample households were selected from the same list of households as in the 2001 survey.
The survey strategy adopted, including handling of non-response or unavailability, was the same
as the one used for the main household survey. Therefore, we have panel data on all household
variables but only a cross-section for the health and time-use variables.
3.3. Data analysis
The aim of the data analysis is to study the effect of coping cost on subjective well-being of
urban households in the Kathmandu Valley. Subjective well-being in our survey is measured at
the individual-level for the main respondent and his/her spouse. Following the literature, we use
two outcomes to capture evaluative and hedonic well-being respectively – life satisfaction and
MHI-5 (mental health inventory) score. Life satisfaction is the response to the question
“Thinking about your overall life including income, job, health, family, and social contacts, how
would you classify your satisfaction with your life right now”? The responses are coded on a
scale of 1 (not satisfied at all) to 10 (very satisfied). MHI-5 score is computed using questions on
feelings of anxiety, depression, positive, and negative affects in the past one month.3 The score is
re-scaled such that a higher score means more positive psychological state.
We use coping cost estimates from (Gurung et al., 2017). Their total monthly coping cost
comprises of five coping strategies – purchasing cost, collecting cost, pumping cost, storage cost,
and treatment cost – and is computed at the household-level. They assume lifespan of all
equipment to be 20 years with a 10% real discount rate. To enable comparison, the 2001 coping
cost is inflated to 2014 rupees. A brief description of their coping cost components is provided
below. For further details see (Gurung et al., 2017).
3 Questions include: (i) During the past month, how much of the time were you a happy person? (ii) During the past month, how much of the time have you felt calm and peaceful? (iii) During the past month, how much of the time have you been a very nervous person? (iv) During the past month, how much of the time have you felt sad, low or depressed? (v) Overall in the last month, how much bodily aches or pains did you experience? The responses are coded on scale of 1 (all of the time) to 5 (none of the time).
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i. Purchasing cost is the total amount a household has to spend on buying water from
KUKL tankers, bottled water vendors, private water tankers, or neighbours.
ii. Storage cost is the total amount spent on installing both underground or overhead storage
tanks plus the depreciated value of the tanks due to usage from the day they have been
installed. Monthly capital costs of water storage assets are calculated based on reported
replacement costs. If a storage tank is shared amongst households, only a proportion of
the total cost is assigned to the respondent’s household.
iii. Treatment cost is the total cost of boiling, filtering or adding additives such as chlorine,
alum, or potash to the water. Annual operation and maintenance (O&M) costs are based
on self-reported monetary expenditures.
iv. Pumping cost includes is the total of fuel or utility expenses to run electric pumps plus
the depreciated value of all kinds of pumps (hand pump or electric pump) the household
owns.
v. Collection cost is the monetized value of time the households report they spend collecting
water from outside the home. It is computed as the average of collection cost during rainy
season and dry season. The cost of time spent collecting water is estimated by
multiplying the self-reported amount of time by an assumed shadow price of time, which
is inferred from the average hourly wage of individuals in the neighborhood. A
distinction is made between households with and without domestic help.
As previously mentioned, our key independent variable, coping cost, can be endogenous due to
omitted variables bias. We therefore estimate an ordinary least square (OLS) regression
controlling for a rich set of observed individual and household characteristics and fixed effects
that might correlate with coping cost and subjective well-being simultaneously. We only retain
households that were interviewed both in 2001 and 2014. To address potential endogeneity
associated with household coping cost in 2014, we control for coping cost in 2001 under the
assumption that current consumption follows past consumption pattern. As current level of
subjective well-being is likely to be correlated with past choices and events, including lagged
coping cost also captures some of the unobserved heterogeneity in the outcome variables. We
control for a wide range observed individual and household characteristics to further reduce the
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bias in the error term. In addition, we include ward and day of interview fixed effects to capture
any potential omitted variables bias arising from time invariant characteristics at the ward-level
and from timing of interview. Specifically, we estimate the following baseline regression
equation,
Y ihjt=β0+φlnCopingCost h+ϑlnCopingCost 2001h+γX 'i+ρZ ' h+ω j+δ t+ε i Eq(2)
where, Y i is the outcome variable life satisfaction or MHI-5 score for individual i from
household h residing in ward j and interviewed on day of week t ; ln CopingCost h is the log
monthly total coping cost in 2014; ln CopingCost 2001h is the log monthly total coping cost in
2001 (inflated to 2014 rupees); X 'i is a vector of respondent characteristics including age, age
squared, gender, caste, education, employment status, and whether the respondent is the person
most responsible for collecting water for the household; Z ' h is a vector of household
characteristics including log monthly household income in 2014,4 log monthly household income
in 2001 (inflated to 2014 rupees),5 household size, household water sources,6 and whether the
household experienced any negative shock such as loss of job, death of family member, or
divorce; ω j are ward fixed effects; and δ t are day of interview fixed effects.
None of the observed control variables included in our model exceed the variance inflation factor
(VIF) threshold of 10 (except for the age quadratic term) and most are in fact below 4 (see
appendix Tables A1 and A2). Multicollinearity is therefore not of concern and we include the
full set of observed controls in our OLS regression model. As a robustness check, we also
estimate ordered logit regressions for the ordinal life satisfaction outcome variable.
As both subjective well-being and coping cost are likely to be determined by a common set of
variables such as age, gender, caste, education, employment, and income, we decompose the 4 Following discussions in (Kahneman & Deaton, 2010) and (Diener et al., 2010) income is included in its logarithmic functional form. 5 2014 is chosen as the base year. The CPI index for 2014 is 141.33 and CPI index for 2001 is 55.39 (World Bank, 2016). The 2001 income is therefore inflated by multiplying the observed income in 2001 by 141.33/55.39=2.55.6 Owing to multiple water sources that households depend on we categorize the water sources into four dummy variables – private water connection; public sources – includes public taps, public wells, stone taps; private sources – includes private well, vendor tankers, bottled water, water jar; and other sources – includes neighbors, surface water, and rain water.
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direct, indirect, and total effects between the determinants of life satisfaction and determinants of
coping cost using path analysis that applies the structural equation modelling (SEM) approach.7
Figure A1 illustrates the path diagram.
To examine the mechanisms through which coping cost may affect subjective well-being, we
estimate Equation (2) with the five coping cost components and their lagged values in 2001. Our
hypothesis is that depending on the perceived net benefit of individual coping cost components,
the relationship between these components and subjective well-being will vary. We also
separately test the correlation between coping cost and time use to bring out the nuances of how
coping cost alters time allocation within the household.8 Time use data were collected using a 24-
hour table that the respondent and spouse filled out as per a normal day.9 The three time use
outcome variables we examine are – time spent on collecting water, time spent on productive
activities, and time spent on leisure – all measured in hours per day.10 Time to collect water is a
separate category in itself. Productive activities include commuting to/from work/school/college,
working (includes self-employment/own business), and professional training. Leisure time
includes reading newspaper; spending time with persons in the household; socializing with
friends, neighbours, and the community; entertainment such as TV, DVD, radio, internet, and
movies; making phone calls; writing letters or emails; exercising including walking, jogging,
gym, sports, and playing (indoors or outdoors); and pursuing hobbies such as reading, writing,
drawing, music, and dance.
4. Results
4.1. Descriptive statistics
7 Note that estimating a measurement model to describe latent factors (life satisfaction and mental health) is not the objective of the SEM. Rather, we apply the SEM approach and use a system of linear regressions to estimate the relationship between subjective well-being and coping cost and their (overlapping) determinants in a path analysis framework. All variables are assumed to be observed. 8 Coping cost and time use are not perfectly collinear. The correlation between monthly total coping cost and time spent on collecting water is -0.123.9 Respondents were asked to imagine their normal 24-hour routine, that is, not an exceptional day such as weekend or public holiday.10 These time use categories are not exhaustive and therefore all three are included in our regressions.
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The average life satisfaction reported is 6.7 (on a 10-point scale) and average MHI-5 is 16.928
(out of 23) suggesting that respondents in the sample report higher subjective well-being on
average. Adjusting for inflation, average monthly total coping cost between 2001 and 2014 has
increased from 524 NPR to 1206 NPR.11 In comparison, average monthly income per household
has increased from 15854 NPR to 36951 NPR during the same period. Households spend about
0.727 hours/day collecting water compared to 2.513 hours/day and 4.007 hours/day spent on
productive activities and leisure respectively.
<Table 1 here>
4.2. Main results
Table 2 presents OLS estimates from the association between coping cost and subjective well-
being. We present the estimates in two panels. Panel A reports results of coping cost on life
satisfaction and Panel B reports results of coping cost on MHI-5. We present models with
different sets of controls and model (4) with the full set of controls is our preferred specification.
Panel A column (4) shows that, on average, if the household increases monthly coping cost by
1% it increases respondent life satisfaction by approximately 0.003 units on a 10-point scale.
Panel B column (4) suggests that increase in coping cost is uncorrelated with MHI-5. The sign
and significance of this result is robust to alternate specification using an ordered logit model
(Table A1).
<Table 2 here>
To put these results into perspective, mean monthly coping cost in 2014 is NPR 1206. A 1%
increase is therefore equivalent to an increase of NPR 12.06. In comparison, mean income in
2014 is NPR 36951 and a 1% increase in income (NPR 369.51) has no statistically significant
correlation with life satisfaction. Thus, in absolute terms, a relatively small amount spent on
coping significantly improves evaluation of life but the same is not true for an increase in
income.
11 1 USD ≈ 110 NPR.
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It is important to re-emphasize that our analysis is able to address endogeneity in coping cost by
controlling for lagged coping cost. In the absence of panel data on well-being outcomes, we
include aggregate-level (day of week and ward) fixed effects to control for some of the
endogeneity in hedonic and evaluative well-being assuming that neighbourhood and timing of
interview are correlated with individual well-being. We also include negative life events in the
past six months such a job loss, death, and divorce to control for individual-level heterogeneity
associated with subjective well-being. Such recent negative life events have been found to be
highly correlated with current levels of well-being (Suh et al., 1996). We do however
acknowledge that unobserved heterogeneity such as that arising from individual personality traits
might still remain in the error term.
To further buttress our OLS results, we examine direct and indirect pathways that are correlated
with subjective well-being through the path analysis. Results presented in Table A4 show that
the total effect (direct plus indirect effect) of monthly coping cost on life satisfaction from the
SEM is consistent with the main OLS result and shows that a 1% increase in coping cost
increases life satisfaction by approximately 0.002 units. For MHI-5, SEM results in Table A5
show a marginally significant (p<0.10) positive total effect. Goodness-of-fit tests show that both
path analysis models fit well. The path analysis results thus add credence to the OLS estimations.
4.3. Examining underlying mechanisms
At first glance, the positive correlation between coping cost and life satisfaction seems
counterintuitive as one would not expect spending more money and time on different coping
strategies to enhance subjective well-being. To find plausible explanations for this relationship,
we explore two mechanisms. First, we use the decomposed coping costs, that is, the five
components, to identify whether specific coping strategies might drive the result. And second,
using detailed time use variables, we examine whether change in daily time use might explain
the result. Table 3 reports breaks down the effect of coping cost on life satisfaction by individual
cost components. We present estimates only from the full model. We find that on average, if a
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household increases its monthly storage cost and treatment cost by 1% it increases respondent
life satisfaction by 0.002 (p<0.01) and 0.001 (p<0.10) units respectively.
<Table 3 here>
Indeed, literature on household water management supports the positive effects of water storage
and treatment cost. As urban water supply in most developing countries continues to be plagued
by quantity and quality issues, households make conscious decisions to obtain regular supply of
potable water by storing water in tanks or other storage vessels and treating for safe domestic
consumption. These practices often entail capital costs by households made to improve health or
avoid health risks, which have direct welfare implications (Pattanayak & Pfaff, 2009;
Whittington & Pattanayak, 2014; World Health Organization (WHO), 2002). Pattanayak and
Pfaff (2009) argue that these capital costs are perceived by the households as ‘investments’
rather than ‘costs’ because the long-term perceived benefits from these expenditures outweigh
the short-term perceived costs. This positive perception of the capital costs on storage and
treatment is a plausible explanation for the positive correlation between coping cost and life
satisfaction.
Table 4 reports the correlation between coping cost and time use. Here again, we control for all
possible observed covariates and aggregate-level fixed effects to address some of the
endogeneity associated with time-use and overcome the lack of panel data on time-use variables.
In column (1) we find that if the household increases monthly coping cost by 1% it decreases
time spent on collecting water by 0.001 hours or approximately 0.06 minutes per day and this is
statistically significant. As theoretically expected, subject to the full budget constraint,
households seem to make trade-offs between spending more money on coping versus spending
more time on collecting water. This time saved or convenience may also drive the positive
association between coping cost and life satisfaction as has been observed in previous studies
conducted in developing countries (Devoto et al., 2012; Mahasuweerachai & Pangjai, 2017).
However, a simultaneous significant increase in productive or leisure time is not observed
though both coefficients are positive.
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We try to get a sense of the magnitude of time saved in relation to coping cost and income. A
back-of-the-envelope calculation shows that a household spending on average 0.40 NPR/day
(12.05 NPR/30 days) on coping would save approximately 0.06 minutes per day collecting
water. Therefore, to gain 1 minute/day a household would need to spend 6.7 NPR/day
(0.40/0.06). In comparison, the average hourly wage per person in 2014 is 89 NPR or 890
NPR/day (estimates from Gurung et al. (2017) assuming a six day work week and 10 hour
working day). Thus, it seems possible for households to spend a portion of their wages to buy
time or convenience.
<Table 4 here>
5. Conclusion
This paper presents new evidence on the association between coping cost and subjective well-
being of urban households in a developing country context. Using large-scale household panel
data around the unique context of water supply in the Kathmandu Valley, Nepal, we examine the
association between households’ cognitive appraisal process of spending on coping with water
insecurity and both evaluative and hedonic aspects of subjective well-being, that is, overall life
satisfaction and short-term psychological responses.
Our main finding is that increased spending on coping is positively correlated with overall life
evaluation but not with hedonic well-being. Exploring further, we posit that this result may be
driven by spending on storage and treatment. Households are likely to perceive these capital
costs as ‘investments’ owing to their perceived long-term benefits (such as health risk avoidance)
exceeding perceived short-term costs. Thus, the seemingly counterintuitive result seems
plausible. Increased coping cost also significantly reduces time spent by households on
collecting water on a daily basis. The resulting time saved or convenience is also likely to
enhance life satisfaction.
The findings from this study are not meant to imply that it is desirable for households to spend
more on coping as it enhances their subjective well-being. Instead, the policy takeaway is that
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households in the Kathmandu Valley seem to spend a significant amount of money and time on
coping with unreliable water supply to sustain their well-being levels. In the longer term, this
may have critical implications for overall economic development as households divert limited
monetary resources and time away from other potentially more productive purposes. Further, our
analysis provides avenues for policymakers to intervene such as improving reliability and quality
of household water supply, which may result in significant improvements in well-being of
residents of the Kathmandu Valley.
Our analysis opens up an interesting opportunity for connecting well-being research with policies
that currently dominate the developing world, that is, improvement in access to basic services
and infrastructure. Subjective well-being effects can provide insightful information on welfare
effects beyond those captured by conventional criteria based on stated and revealed preferences.
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Tables and figures
Figure 1. Map of study area
Source: Gurung et al. (2017)
Notes: Dots represent surveyed households.
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Table 1. Summary statistics
Panel A: Subjective well-being indicators Obs. Mean S.D. Min MaxLife satisfaction 846 6.700 1.777 1 10MHI-5 780 16.928 1.914 11 23Panel B: Monthly coping costYear 2014 Total coping cost (NPR) 1500 1205.569 1041.255 0.000 9846.024Purchasing cost 1500 349.619 608.808 0.000 7500.000Storage cost 1500 350.476 496.794 0.000 4495.870Treatment cost 1500 117.320 139.410 0.000 1608.118Pumping cost 1500 224.000 360.280 0.000 4201.377Collection cost 1500 164.155 568.482 0.000 9433.594Year 2001Total coping cost 2001 (in 2014 NPR) 1500 523.730 903.541 0.000 17584.200Purchasing cost 1500 43.167 470.287 0.000 9185.557Storage cost 1500 94.164 183.226 0.000 1677.693Treatment cost 1500 106.459 277.399 0.000 8857.260Pumping cost 1500 31.293 97.467 0.000 1672.914Collection cost 1500 248.646 657.188 0.000 17442.190Panel C: Time use (hours/day)Time spent collecting water 419 0.727 0.695 0 4Time spent on productive activities 419 2.513 3.642 0 12.5Time spent on leisure 419 4.007 2.587 0 12Panel D: Household VariablesMonthly income 2014 1475 36951.070 42512.070 0 703000Monthly income 2001 (in 2014 NPR) 1475 15854.020 20500.990 300 309000Household size 1475 5.197 2.190 1 15Household has experienced negative shock 846 0.046 0.210 0 1Panel E: Individual variablesRespondent age 1475 49.618 13.207 18 86Respondent gender 1475 0.491 0.500 0 1Respondent education 1475 8.506 6.252 0 18Respondent employment status 1475 0.496 0.500 0 1Respondent is responsible for collecting water 1475 0.261 0.439 0 1Respondent caste Chhetri 1475 0.084 0 1 Bhamhin – Hill 1475 0.159 0 1 Newari 1475 0.675 0 1 Others 1475 0.082 0 1
Notes: Reported means are raw sample means. If households did not report spending on any of the five coping strategies it is assumed that they spend 0 NPR and the means are averaged over the full sample.
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Table 2. Coping cost and subjective well-being
Panel A: Life satisfaction Life Satisfaction
Life Satisfaction
Life Satisfaction
Life Satisfaction
(1) (2) (3) (4)Log monthly total coping cost 0.188*** 0.185*** 0.281*** 0.298***
(0.064) (0.064) (0.069) (0.069)N = 812 812 812 812Panel B: MHI-5 MHI-5 MHI-5 MHI-5 MHI-5Log monthly total coping cost 0.131 0.130 0.033 0.136
(0.083) (0.083) (0.093) (0.101)N = 749 749 749 749Total monthly coping cost in 2001 Y Y Y YIndividual controlsa Y Y Y YHousehold controlsb Y Y Y YNegative shockc N Y Y YWater sourced N N Y YFixed effectse N N N Y
Notes:OLS regression models. Robust standard errors reported in parentheses. (*) p<0.1 (**) p<0.05 (***) p<0.001.
a. Individual controls include age, age squared, gender, caste, respondent education level, respondent employment status, and dummy variable to indicate whether the respondent is the person most responsible for collecting water for the household.
b. Household controls include log monthly household income in 2014, log monthly household income in 2001, and household size.
c. Negative shock is a dummy indicating whether the household experienced death of any family member, divorce in family or any job loss in the last 6 months.
d. Water source indicates which of the four water source groups the household has access to. Group 1 is private water connection; Group 2 is public sources, including public taps, public wells, free KUKL tankers, and stone taps; Group 3 includes private well, private tanker, bottled water, and jar water; and Group 4 includes water from neighbors, KUKL tanker which is charged, surface water sources, and rainwater.
e. Fixed effects include dummies to indicate the household ward and the day of week when the interview was conducted.
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Table 3. Mechanism: Coping cost components and life satisfactionLife Satisfaction
Log monthly purchase cost 0.034(0.027)
Log monthly storage cost 0.209***(0.039)
Log monthly treatment cost 0.059*(0.032)
Log monthly pump cost 0.021(0.038)
Log monthly collection cost -0.004(0.039)
N = 812Monthly coping costs in 2001a YIndividual controlsb YHousehold controlsc YNegative shockd YWater sourcee YFixed effectsf Y
Notes:OLS regression models. Robust standard errors reported in parentheses. (*) p<0.1 (**) p<0.05 (***) p<0.001.
a. Monthly coping costs in 2001 controls for all five coping costs in 2001.b. Individual controls include age, age squared, gender, caste, respondent education level, respondent
employment status, and dummy variable to indicate whether the respondent is the person most responsible for collecting water for the household.
c. Household controls include log monthly household income in 2014, log monthly household income in 2001, and household size.
d. Negative shock is a dummy indicating whether the household experienced death of any family member, divorce in family or any job loss in the last 6 months.
e. Water source indicates which of the four water source groups the household has access to. Group 1 is private water connection; Group 2 is public sources, including public taps, public wells, free KUKL tankers, and stone taps; Group 3 includes private well, private tanker, bottled water, and jar water; and Group 4 includes water from neighbors, KUKL tanker which is charged, surface water sources, and rainwater.
f. Fixed effects include dummies to indicate the household ward and the day of week when the interview was conducted.
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Table 4. Mechanism: Coping cost and time use
DV: Time spent daily in hours on Collecting water Productive activities Leisure
(1) (2) (3)Log monthly total coping cost -0.102** 0.059 0.146
(0.044) (0.130) (0.126)N = 403 403 403Total monthly coping cost in 2001 Y Y YIndividual controlsa Y Y YHousehold controlsb Y Y YNegative shockc Y Y YWater sourced Y Y YFixed effectse Y Y Y
Notes:OLS regression models. Robust standard errors reported in parentheses. (*) p<0.1 (**) p<0.05 (***) p<0.001.
a. Individual controls include age, age squared, gender, caste, respondent education level, respondent employment status, and dummy variable to indicate whether the respondent is the person most responsible for collecting water for the household.
b. Household controls include log monthly household income in 2014, log monthly household income in 2001, and household size.
c. Negative shock is a dummy indicating whether the household experienced death of any family member, divorce in family or any job loss in the last 6 months.
d. Water source indicates which of the four water source groups the household has access to. Group 1 is private water connection; Group 2 is public sources, including public taps, public wells, free KUKL tankers, and stone taps; Group 3 includes private well, private tanker, bottled water, and jar water; and Group 4 includes water from neighbors, KUKL tanker which is charged, surface water sources, and rainwater.
e. Fixed effects include dummies to indicate the household ward and the day of week when the interview was conducted.
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Appendices
Table A1. Variance inflation factor (VIF): Post-estimation after coping cost and life satisfaction OLS regressionVariables VIFAge squared 46.19Age 45.26Caste 3 3.67Caste 2 2.58Caste 4 2.02Respondent education 1.71Log total monthly coping cost 1.62Water source 3 1.59Respondent employment 1.57Water source 2 1.53Respondent gender 1.51Log monthly household income 2001 1.26Log total monthly coping cost 2001 1.22Respondent responsible for collecting water 1.18Household size 1.13Water source 4 1.09Log monthly household income 1.08Dummy negative shock 1.03Mean VIF 6.51
Notes: Fixed effects VIF suppressed due to space constraints. Water source indicates which of the four water source groups the household has access to. Group 1 is private water connection; Group 2 is public sources, including public taps, public wells, free KUKL tankers, and stone taps; Group 3 includes private well, private tanker, bottled water, and jar water; and Group 4 includes water from neighbors, KUKL tanker which is charged, surface water sources, and rainwater.
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Table A2. Variance inflation factor (VIF): Post-estimation after coping cost and MHI-5 OLS regressionVariables VIFAge squared 45.86Age 44.94Caste 3 3.76Caste 2 2.66Caste 4 2.04Respondent education 1.73Log total monthly coping cost 1.60Water source 3 1.58Respondent employment 1.55Respondent gender 1.51Water source 2 1.50Log monthly household income 2001 1.24Log total monthly coping cost 2001 1.21Dummy respondent responsible for collecting water 1.18Household size 1.14Log monthly household income 1.10Water source 4 1.09Dummy negative shock 1.04Mean VIF 6.48
Notes: Fixed effects VIF suppressed due to space constraints. Water source indicates which of the four water source groups the household has access to. Group 1 is private water connection; Group 2 is public sources, including public taps, public wells, free KUKL tankers, and stone taps; Group 3 includes private well, private tanker, bottled water, and jar water; and Group 4 includes water from neighbors, KUKL tanker which is charged, surface water sources, and rainwater.
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Table A3. Coping cost and subjective well-being – Ordered logit regressionsLife
SatisfactionLife
SatisfactionLife
SatisfactionLife
Satisfaction(1) (2) (3) (4)
Log monthly total coping cost 0.253*** 0.250*** 0.373*** 0.427***(0.077) (0.077) (0.084) (0.083)
Cut point 1 -2.454* -2.375* -2.562** -3.382**(1.257) (1.255) (1.277) (1.405)
Cut point 2 -1.488 -1.409 -1.594 -2.404*(1.216) (1.212) (1.230) (1.348)
Cut point 3 -0.775 -0.696 -0.878 -1.662(1.210) (1.206) (1.219) (1.334)
Cut point 4 0.130 0.212 0.043 -0.692(1.215) (1.212) (1.223) (1.330)
Cut point 5 2.015* 2.103* 1.980 1.416(1.221) (1.219) (1.232) (1.341)
Cut point 6 2.703** 2.793** 2.688** 2.221*(1.224) (1.221) (1.234) (1.339)
Cut point 7 3.511*** 3.606*** 3.527*** 3.171**(1.226) (1.224) (1.236) (1.339)
Cut point 8 4.790*** 4.888*** 4.852*** 4.603***(1.230) (1.228) (1.240) (1.338)
Cut point 9 6.004*** 6.102*** 6.099*** 5.901***(1.250) (1.248) (1.263) (1.361)
N = 812 812 812 812Total monthly coping cost in 2001 Y Y Y YIndividual controlsa Y Y Y YHousehold controlsb Y Y Y YNegative shockc N Y Y YWater sourced N N Y YFixed effectse N N N Y
Notes: Reported coefficients are from ordered logit models. Robust standard errors reported in parentheses. (*) p<0.1 (**) p<0.05 (***) p<0.001.
a. Individual controls include age, age squared, gender, caste, respondent education level, respondent employment status, and dummy variable to indicate whether the respondent is the person most responsible for collecting water for the household.
b. Household controls include log monthly household income in 2014, log monthly household income in 2001, and household size.
c. Negative shock is a dummy indicating whether the household experienced death of any family member, divorce in family or any job loss in the last 6 months.
d. Water source indicates which of the four water source groups the household has access to. Group 1 is private water connection; Group 2 is public sources, including public taps, public wells, free KUKL tankers, and stone taps; Group 3 includes private well, private tanker, bottled water, and jar water; and Group 4 includes water from neighbors, KUKL tanker which is charged, surface water sources, and rainwater.
e. Fixed effects include dummies to indicate the household ward and the day of week when the interview was conducted.
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Figure A1. Path diagram
Notes:
a. Figure A1 represents theoretical path diagram.b. All variables are assumed to be observed.c. Arrows point to direct effects in the shown direction.d. Model estimates are presented in Tables A4 and A5.e.
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Table A4. Direct, indirect, and total effects of coping cost on life satisfaction from SEMDirect effect Coefficient S.E.Life satisfactionLog monthly total coping cost 0.173*** 0.065Age -0.012 0.029Age squared 0.000 0.000Gender -0.041 0.143Caste 2 0.524** 0.258Caste 3 -0.534** 0.232Caste 4 -0.146 0.294Respondent education 0.026** 0.012Respondent employment 0.214 0.145Dummy respondent responsible for collecting water 0.216* 0.125Log monthly household income 0.023 0.030Log monthly household income 2001 0.151* 0.082Dummy household experienced negative shock -0.548* 0.285Log monthly total coping costAge 0.043*** 0.014Age squared 0.000*** 0.000Gender -0.013 0.069Caste 2 -0.038 0.125Caste 3 -0.385*** 0.115Caste 4 -0.184 0.144Respondent education 0.039*** 0.006Respondent employment -0.131* 0.071Dummy respondent responsible for collecting water 0.112* 0.061Log monthly household income 0.023 0.015Log monthly household income 2001 0.153*** 0.041Log monthly total coping cost 2001 0.041* 0.023Household size 0.034** 0.014Water source 2 0.222*** 0.075Water source 3 0.880*** 0.068Water source 4 0.025 0.061Indirect effectLife satisfactionAge 0.007** 0.004Age squared 0.000** 0.000Gender -0.002 0.012Caste 2 -0.007 0.022Caste 3 -0.067** 0.032Caste 4 -0.032 0.028Respondent education 0.007** 0.003Respondent employment -0.023 0.015Dummy respondent responsible for collecting water 0.019 0.013
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Log monthly household income 0.004 0.003Log monthly household income 2001 0.027** 0.012Log monthly total coping cost 2001 0.007 0.005Household size 0.006* 0.003Water source 2 0.039** 0.020Water source 3 0.153*** 0.059Water source 4 0.004 0.011Total effectLife satisfactionLog monthly total coping cost 0.173*** 0.065Age -0.005 0.029Age squared 0.000 0.000Gender -0.043 0.143Caste 2 0.518** 0.259Caste 3 -0.601*** 0.231Caste 4 -0.178 0.295Respondent education 0.033*** 0.011Respondent employment 0.192 0.146Dummy respondent responsible for collecting water 0.235* 0.126Log monthly household income 0.027 0.030Log monthly household income 2001 0.178** 0.081Dummy household experienced negative shock -0.548* 0.285Log monthly total coping cost 2001 0.007 0.005Household size 0.006* 0.003Water source 2 0.038** 0.019Water source 3 0.152*** 0.058Water source 4 0.004 0.011Log monthly total coping costAge 0.043*** 0.014Age squared 0.000*** 0.000Gender -0.013 0.069Caste 2 -0.038 0.125Caste 3 -0.385*** 0.115Caste 4 -0.184 0.144Respondent education 0.039*** 0.006Respondent employment -0.131* 0.071Dummy respondent responsible for collecting water 0.112* 0.061Log monthly household income 0.023 0.015Log monthly household income 2001 0.153*** 0.041Log monthly total coping cost 2001 0.041* 0.023Household size 0.034** 0.014Water source 2 0.222*** 0.075Water source 3 0.880*** 0.068Water source 4 0.025 0.061
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Goodness-of-fit testsRoot mean square error of approximation (RMSEA) 0.084Standardized root mean square residual (SRMR) 0.014Comparative fit index (CFI) 0.932
Notes:
f. Reported coefficients are from linear structural equation models. Robust standard errors reported in parentheses. (*) p<0.1 (**) p<0.05 (***) p<0.001.
g. Caste dummies indicate which caste the respondent belongs to. Caste 1 is Chhetri; Caste 2 is Bhamin (hill); Caste 3 is Newari; and Caste 4 is all other castes.
h. Water source indicates which of the four water source groups the household has access to. Group 1 is private water connection; Group 2 is public sources, including public taps, public wells, free KUKL tankers, and stone taps; Group 3 includes private well, private tanker, bottled water, and jar water; and Group 4 includes water from neighbors, KUKL tanker which is charged, surface water sources, and rainwater.
i. Goodness-of-fit RMSEA test indicates that the model fits adequately well. SRMR and CFI indicate that the model fits well.
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Table A4. Direct, indirect, and total effects of coping cost on MHI-5 from SEMDirect effect Coefficient S.E.MHI-5Log monthly total coping cost 0.140* 0.078Age -0.078** 0.035Age squared 0.001** 0.000Gender -0.128 0.171Caste 2 0.015 0.308Caste 3 0.006 0.280Caste 4 0.083 0.355Respondent education -0.001 0.014Respondent employment 0.281 0.173Dummy respondent responsible for collecting water -0.241 0.148Log monthly household income 0.035 0.035Log monthly household income 2001 -0.037 0.097Dummy household experienced negative shock -0.086 0.332Log monthly total coping costAge 0.042*** 0.015Age squared 0.000*** 0.000Gender 0.015 0.072Caste 2 -0.063 0.130Caste 3 -0.422*** 0.121Caste 4 -0.259* 0.150Respondent education 0.036*** 0.006Respondent employment -0.130* 0.073Dummy respondent responsible for collecting water 0.080 0.064Log monthly household income 0.029* 0.015Log monthly household income 2001 0.138*** 0.042Log monthly total coping cost 2001 0.038* 0.023Household size 0.029* 0.015Water source 2 0.244*** 0.078Water source 3 0.903*** 0.070Water source 4 0.005 0.063Indirect effectMHI-5Age 0.006 0.004Age squared 0.000 0.000Gender 0.002 0.010Caste 2 -0.009 0.019Caste 3 -0.059 0.037Caste 4 -0.036 0.029Respondent education 0.005* 0.003Respondent employment -0.018 0.014Dummy respondent responsible for collecting water 0.011 0.011
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Log monthly household income 0.004 0.003Log monthly household income 2001 0.019 0.012Log monthly total coping cost 2001 0.005 0.004Household size 0.004 0.003Water source 2 0.034 0.022Water source 3 0.126* 0.071Water source 4 0.001 0.009Total effectMHI-5Log monthly total coping cost 0.140* 0.078Age -0.072** 0.034Age squared 0.001** 0.000Gender -0.126 0.171Caste 2 0.006 0.309Caste 3 -0.053 0.278Caste 4 0.046 0.355Respondent education 0.004 0.014Respondent employment 0.263 0.173Dummy respondent responsible for collecting water -0.230 0.149Log monthly household income 0.039 0.035Log monthly household income 2001 -0.017 0.096Dummy household experienced negative shock -0.086 0.332Log monthly total coping cost 2001 0.005 0.004Household size 0.004 0.003Water source 2 0.034 0.022Water source 3 0.126* 0.071Water source 4 0.001 0.009Log monthly total coping costAge 0.042*** 0.015Age squared 0.000*** 0.000Gender 0.015 0.072Caste 2 -0.063 0.130Caste 3 -0.422*** 0.121Caste 4 -0.259* 0.150Respondent education 0.036*** 0.006Respondent employment -0.130* 0.073Dummy respondent responsible for collecting water 0.080 0.064Log monthly household income 0.029* 0.015Log monthly household income 2001 0.138*** 0.042Log monthly total coping cost 2001 0.038* 0.023Household size 0.029* 0.015Water source 2 0.244*** 0.078Water source 3 0.903*** 0.070Water source 4 0.005 0.063
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Goodness-of-fit testsRoot mean square error of approximation (RMSEA) 0.041Standardized root mean square residual (SRMR) 0.007Comparative fit index (CFI) 0.978
Notes:
a. Reported coefficients are from linear structural equation models. Robust standard errors reported in parentheses. (*) p<0.1 (**) p<0.05 (***) p<0.001.
b. Caste dummies indicate which caste the respondent belongs to. Caste 1 is Chhetri; Caste 2 is Bhamin (hill); Caste 3 is Newari; and Caste 4 is all other castes.
c. Water source indicates which of the four water source groups the household has access to. Group 1 is private water connection; Group 2 is public sources, including public taps, public wells, free KUKL tankers, and stone taps; Group 3 includes private well, private tanker, bottled water, and jar water; and Group 4 includes water from neighbors, KUKL tanker which is charged, surface water sources, and rainwater.
d. All goodness-of-fit tests indicate that the model fits well.
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