Abstract
Subjective Well-being Effects of Coping Cost: Evidence from Household Water Supply in Kathmandu Valley, NepalAbstractCoping 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 Authorship statementNC 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.AcknowledgmentsWe 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.1. IntroductionWater 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 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Sb2RlbGw8L0F1dGhvcj48WWVhcj4yMDE4PC9ZZWFyPjxS
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ADDIN EN.CITE.DATA (Rodell et al., 2018; V?r?smarty et al., 2000; V?r?smarty 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 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5KdXJ5PC9BdXRob3I+PFllYXI+MjAwNzwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA (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 ADDIN EN.CITE <EndNote><Cite><Author>Jury</Author><Year>2007</Year><RecNum>75</RecNum><DisplayText>(Jury & Vaux, 2007; World Health Organization (WHO) & Department for International Development (DFID), 2009)</DisplayText><record><rec-number>75</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1527595108">75</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jury, William</author><author>Vaux, Henry Jr.</author></authors></contributors><titles><title>The Emerging Global Water Crisis: Managing Scarcity and Conflict Between Water Users</title><secondary-title>Advances in Agronomy</secondary-title></titles><periodical><full-title>Advances in Agronomy</full-title></periodical><pages>1-76</pages><volume>95</volume><dates><year>2007</year></dates><urls></urls></record></Cite><Cite><Author>World Health Organization (WHO)</Author><Year>2009</Year><RecNum>63</RecNum><record><rec-number>63</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1526526180">63</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>World Health Organization (WHO),</author><author>Department for International Development (DFID),</author></authors></contributors><titles><title>Vision 2030: The Resilience of Water Supply and Sanitation in the Face of Climate Change</title></titles><dates><year>2009</year></dates><pub-location>Geneva</pub-location><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Cook</Author><Year>2016</Year><RecNum>2</RecNum><DisplayText>(Cook et al., 2016)</DisplayText><record><rec-number>2</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467865796">2</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Joseph Cook</author><author>Peter Kimuyu</author><author>Dale Whittington</author></authors></contributors><titles><title>The Costs of Coping with Poor Water Supply in Rural Kenya</title><secondary-title>Water Resources Research</secondary-title></titles><periodical><full-title>Water Resources Research</full-title></periodical><pages>841-859</pages><volume>52</volume><number>2</number><dates><year>2016</year></dates><urls></urls></record></Cite></EndNote>(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 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db29rPC9BdXRob3I+PFllYXI+MjAxNjwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA (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 ADDIN EN.CITE <EndNote><Cite><Author>Sadoff</Author><Year>2015</Year><RecNum>76</RecNum><DisplayText>(Sadoff et al., 2015)</DisplayText><record><rec-number>76</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1527595616">76</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Sadoff, C.W.</author><author>Hall, J.W.</author><author>Grey, D.</author><author>Aerts, J.C.J.H.</author><author>Ait-Kadi, M.</author><author>Brown, C.</author><author>Cox, A.</author><author>Dadson, S.</author><author>Garrick, D.</author><author>Kelman, J.</author><author>McCornick, P.</author><author>Ringler, C.</author><author>Rosegrant, M.</author><author>Whittington, D.</author><author>Wiberg, D.</author></authors></contributors><titles><title>Securing Water, Sustaining Growth: Report of the GWP/OECD Task Force on Water Security and Sustainable Growth</title></titles><dates><year>2015</year></dates><pub-location>University of Oxford</pub-location><publisher>Global Water Partnership</publisher><urls></urls></record></Cite></EndNote>(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 psychological effects ADDIN EN.CITE <EndNote><Cite><Author>Wutich</Author><Year>2008</Year><RecNum>4</RecNum><DisplayText>(Wutich & Ragsdale, 2008)</DisplayText><record><rec-number>4</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467874833">4</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Amber Wutich</author><author>Kathleen Ragsdale</author></authors></contributors><titles><title>Water Insecurity and Emotional Distress: Coping with Supply, Access, and Seasonal Variability of Water in a Bolivian Squatter Settlement</title><secondary-title>Social Science & Medicine</secondary-title></titles><periodical><full-title>Social Science & Medicine</full-title></periodical><pages>2116-2125</pages><volume>67</volume><number>12</number><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Shah</Author><Year>2012</Year><RecNum>10</RecNum><DisplayText>(Shah et al., 2012)</DisplayText><record><rec-number>10</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467875697">10</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Anuj Shah</author><author>Sendhil Mullainathan</author><author>Eldar Shafir</author></authors></contributors><titles><title>Some Consequences of Having Too Little</title><secondary-title>Science</secondary-title></titles><periodical><full-title>Science</full-title></periodical><pages>682-685</pages><volume>338</volume><number>6107</number><dates><year>2012</year></dates><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite AuthorYear="1"><Author>Gurung</Author><Year>2017</Year><RecNum>55</RecNum><DisplayText>Gurung et al. (2017)</DisplayText><record><rec-number>55</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1505737156">55</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Yogendra Gurung</author><author>Jane Zhao</author><author>Bal Kumar K.C.</author><author>Xun Wu</author><author>Bhim Suwal</author><author>Dale Whittington</author></authors></contributors><titles><title>The Costs of Delay in Infrastructure Investments: A Comparison of 2001 and 2014 Household Water Supply Coping Costs in the Kathmandu Valley, Nepal</title><secondary-title>Water Resources Research</secondary-title></titles><periodical><full-title>Water Resources Research</full-title></periodical><dates><year>2017</year></dates><urls></urls><electronic-resource-num>10.1002/2016WR019529</electronic-resource-num></record></Cite></EndNote>Gurung et al. 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ADDIN EN.CITE.DATA (Casey et al., 2006; Pattanayak et al., 2005; Raje et al., 2002; Whittington et al., 2002) and revealed preferences such as hedonic pricing ADDIN EN.CITE <EndNote><Cite><Author>North</Author><Year>1993</Year><RecNum>39</RecNum><DisplayText>(North & Griffin, 1993; Rosiers et al., 1999)</DisplayText><record><rec-number>39</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1502170447">39</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>J. H. North</author><author>C.C. Griffin</author></authors></contributors><titles><title>Water Source as a Housing Characteristic: Hedonic Property Valuation and Willingness to Pay for Water</title><secondary-title>Water Resources Research</secondary-title></titles><periodical><full-title>Water Resources Research</full-title></periodical><pages>1923-1929</pages><volume>29</volume><number>7</number><dates><year>1993</year></dates><urls></urls></record></Cite><Cite><Author>Rosiers</Author><Year>1999</Year><RecNum>40</RecNum><record><rec-number>40</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1502170578">40</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Fran?ois Des Rosiers</author><author>Alain Bolduc</author><author>Marius Thériault</author></authors></contributors><titles><title>Environment and Value: Does Drinking Water Quality Affect House Prices?</title><secondary-title>Journal of Property Investment & Finance</secondary-title></titles><periodical><full-title>Journal of Property Investment & Finance</full-title></periodical><pages>444-463</pages><volume>17</volume><number>5</number><dates><year>1999</year></dates><urls></urls></record></Cite></EndNote>(North & Griffin, 1993; Rosiers et al., 1999) as the welfare criteria. Both stated and revealed preference methods rest on the 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 ADDIN EN.CITE <EndNote><Cite><Author>Frey</Author><Year>2002</Year><RecNum>41</RecNum><DisplayText>(Frey & Stutzer, 2002)</DisplayText><record><rec-number>41</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1502170714">41</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bruno S. Frey</author><author>Alois Stutzer</author></authors></contributors><titles><title>What Can Economists Learn from Happiness Research?</title><secondary-title>Journal of Economic Literature</secondary-title></titles><periodical><full-title>Journal of Economic Literature</full-title></periodical><pages>402-435</pages><volume>40</volume><dates><year>2002</year></dates><urls></urls></record></Cite></EndNote>(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 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LYWhuZW1hbjwvQXV0aG9yPjxZZWFyPjIwMDY8L1llYXI+
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ADDIN EN.CITE.DATA (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 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5EZWNpPC9BdXRob3I+PFllYXI+MjAwNjwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA (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 ADDIN EN.CITE <EndNote><Cite><Author>Diener</Author><Year>1984</Year><RecNum>20</RecNum><DisplayText>(Diener, 1984; Kahneman & Krueger, 2006)</DisplayText><record><rec-number>20</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467877072">20</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ed Diener</author></authors></contributors><titles><title>Subjective Well-Being</title><secondary-title>Psychological Bulletin</secondary-title></titles><periodical><full-title>Psychological Bulletin</full-title></periodical><pages>542-575</pages><volume>95</volume><number>3</number><dates><year>1984</year></dates><urls></urls></record></Cite><Cite><Author>Kahneman</Author><Year>2006</Year><RecNum>69</RecNum><record><rec-number>69</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1526798164">69</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Daniel Kahneman</author><author>Alan B. Krueger</author></authors></contributors><titles><title>Developments in the Measurement of Subjective Well-Being</title><secondary-title>Journal of Economic Perspectives</secondary-title></titles><periodical><full-title>Journal of Economic Perspectives</full-title></periodical><pages>3-24</pages><volume>20</volume><number>1</number><dates><year>2006</year></dates><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Tov</Author><Year>2013</Year><RecNum>22</RecNum><DisplayText>(Tov & Au, 2013)</DisplayText><record><rec-number>22</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467879695">22</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>William Tov</author><author>Evelyn Wing-Mun Au</author></authors><secondary-authors><author>Susan A. David</author><author>Ilona Boniwell</author><author>Amanda Conley Ayers</author></secondary-authors></contributors><titles><title>Comparing Well-being across Nations: Conceptual and Empirical Issues</title><secondary-title>Oxford Handbook of Happiness</secondary-title></titles><pages>448-464</pages><dates><year>2013</year></dates><pub-location>Oxford</pub-location><publisher>Oxford University Press</publisher><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Tov</Author><Year>2013</Year><RecNum>22</RecNum><DisplayText>(Tov & Au, 2013)</DisplayText><record><rec-number>22</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467879695">22</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>William Tov</author><author>Evelyn Wing-Mun Au</author></authors><secondary-authors><author>Susan A. David</author><author>Ilona Boniwell</author><author>Amanda Conley Ayers</author></secondary-authors></contributors><titles><title>Comparing Well-being across Nations: Conceptual and Empirical Issues</title><secondary-title>Oxford Handbook of Happiness</secondary-title></titles><pages>448-464</pages><dates><year>2013</year></dates><pub-location>Oxford</pub-location><publisher>Oxford University Press</publisher><urls></urls></record></Cite></EndNote>(Tov & Au, 2013). A study by ADDIN EN.CITE <EndNote><Cite AuthorYear="1"><Author>Fujita</Author><Year>2005</Year><RecNum>23</RecNum><DisplayText>Fujita and Diener (2005)</DisplayText><record><rec-number>23</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467879816">23</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Frank Fujita</author><author>Ed Diener</author></authors></contributors><titles><title>Life Satisfaction Set Point: Stability and Change</title><secondary-title>Journal of Personality and Social Psychology</secondary-title></titles><periodical><full-title>Journal of Personality and Social Psychology</full-title></periodical><pages>158-164</pages><volume>88</volume><number>1</number><dates><year>2005</year></dates><urls></urls></record></Cite></EndNote>Fujita and Diener (2005) finds life satisfaction to be quite stable over time and therefore argues that it is a reliable measure. Eudemonic well-being goes beyond happiness and is concerned with self-realization and purpose and meaning of one’s life ADDIN EN.CITE <EndNote><Cite><Author>Deci</Author><Year>2006</Year><RecNum>72</RecNum><DisplayText>(Deci & Ryan, 2006; Ryan & Deci, 2001)</DisplayText><record><rec-number>72</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1526799312">72</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Deci, Edward L.</author><author>Ryan, Richard M.</author></authors></contributors><titles><title>Hedonia, Eudaimonia, and Well-Being: An Introduction</title><secondary-title>Journal of Happiness Studies</secondary-title></titles><periodical><full-title>Journal of Happiness Studies</full-title></periodical><pages>1-11</pages><volume>9</volume><number>1</number><dates><year>2006</year></dates><urls></urls></record></Cite><Cite><Author>Ryan</Author><Year>2001</Year><RecNum>71</RecNum><record><rec-number>71</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1526799260">71</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Richard M. Ryan </author><author>Edward L. Deci</author></authors></contributors><titles><title>On Happiness and Human Potentials: A Review of Research on Hedonic and Eudaimonic Well-Being</title><secondary-title>Annual Review of Psychology</secondary-title></titles><periodical><full-title>Annual Review of Psychology</full-title></periodical><pages>141-166</pages><volume>52</volume><dates><year>2001</year></dates><urls></urls></record></Cite></EndNote>(Deci & Ryan, 2006; Ryan & Deci, 2001). Measures include ordinal scale questions on best possible life, sense of self-worth, social integration, autonomy and so on ADDIN EN.CITE <EndNote><Cite><Author>Cooke</Author><Year>2016</Year><RecNum>74</RecNum><DisplayText>(Cooke et al., 2016)</DisplayText><record><rec-number>74</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1526953051">74</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Philip J. Cooke</author><author>Timothy P. Melchert</author><author>Korey Connor</author></authors></contributors><titles><title>Measuring Well-Being: A Review of Instruments</title><secondary-title>The Counseling Psychologist</secondary-title></titles><periodical><full-title>The Counseling Psychologist</full-title></periodical><pages>730-757</pages><volume>44</volume><number>5</number><dates><year>2016</year></dates><urls></urls></record></Cite></EndNote>(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. ADDIN EN.CITE <EndNote><Cite AuthorYear="1"><Author>Lucas</Author><Year>2008</Year><RecNum>24</RecNum><DisplayText>Lucas and Diener (2008)</DisplayText><record><rec-number>24</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467880090">24</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>Richard E. Lucas</author><author>Ed Diener</author></authors><secondary-authors><author>Fons J.R. van de Vijver</author><author>Dianne A. Van Hemert</author><author>Ype H. Poortinga</author></secondary-authors></contributors><titles><title>Can We Learn About National Differences in Happiness from Individual Responses? A Multilevel Approach</title><secondary-title>Multilevel Analysis of Individuals and Cultures</secondary-title></titles><dates><year>2008</year></dates><pub-location>New York</pub-location><publisher>Psychology Press (Taylor & Francis Group)</publisher><urls></urls></record></Cite></EndNote>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 ADDIN EN.CITE <EndNote><Cite><Author>Maddison</Author><Year>2011</Year><RecNum>65</RecNum><DisplayText>(Maddison & Rehdanz, 2011)</DisplayText><record><rec-number>65</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1526793307">65</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>David Maddison</author><author>Katrin Rehdanz</author></authors></contributors><titles><title>The Impact of Climate on Life Satisfaction</title><secondary-title>Ecological Economics</secondary-title></titles><periodical><full-title>Ecological Economics</full-title></periodical><pages>2437-2445</pages><volume>70</volume><number>12</number><dates><year>2011</year></dates><urls></urls></record></Cite></EndNote>(Maddison & Rehdanz, 2011). 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ADDIN EN.CITE.DATA (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 ADDIN EN.CITE <EndNote><Cite AuthorYear="1"><Author>Mahasuweerachai</Author><Year>2017</Year><RecNum>44</RecNum><DisplayText>Mahasuweerachai and Pangjai (2017)</DisplayText><record><rec-number>44</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1502171172">44</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Phumsith Mahasuweerachai</author><author>Siwarut Pangjai</author></authors></contributors><titles><title>Does Piped Water Improve Happiness? A Case from Asian Rural Communities</title><secondary-title>Journal of Happiness Studies</secondary-title></titles><periodical><full-title>Journal of Happiness Studies</full-title></periodical><dates><year>2017</year></dates><urls></urls><electronic-resource-num>10.1007/s10902-017-9875-9</electronic-resource-num></record></Cite></EndNote>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. ADDIN EN.CITE <EndNote><Cite AuthorYear="1"><Author>Devoto</Author><Year>2012</Year><RecNum>45</RecNum><DisplayText>Devoto et al. (2012)</DisplayText><record><rec-number>45</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1502171532">45</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Florencia Devoto</author><author>Esther Duflo</author><author>Pascaline Dupas</author><author>William Pariente</author><author>Vincent Pons</author></authors></contributors><titles><title>Happiness on Tap: Piper Water Adoption in Urban Morocco</title><secondary-title>American Economic Journal: Economic Policy</secondary-title></titles><periodical><full-title>American Economic Journal: Economic Policy</full-title></periodical><pages>68-99</pages><volume>4</volume><number>4</number><dates><year>2012</year></dates><urls></urls></record></Cite></EndNote>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.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 frameworkThe 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 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MYXphcnVzPC9BdXRob3I+PFllYXI+MTk2NjwvWWVhcj48
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ADDIN EN.CITE.DATA (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 ADDIN EN.CITE <EndNote><Cite><Author>Monat</Author><Year>1976</Year><RecNum>17</RecNum><DisplayText>(Monat, 1976; Monat et al., 1972)</DisplayText><record><rec-number>17</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467876648">17</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Alan Monat</author></authors></contributors><titles><title>Temporal Uncertainty, Anticipation Time, and Cognitive Coping under Threat</title><secondary-title>Journal of Human Stress</secondary-title></titles><periodical><full-title>Journal of Human Stress</full-title></periodical><pages>32-43</pages><volume>2</volume><number>2</number><dates><year>1976</year></dates><urls></urls></record></Cite><Cite><Author>Monat</Author><Year>1972</Year><RecNum>18</RecNum><record><rec-number>18</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467876761">18</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Alan Monat</author><author>James R. Averill</author><author>Richard S. Lazarus</author></authors></contributors><titles><title>Anticipatory Stress and Coping Reactions under Various Conditions of Uncertainty</title><secondary-title>Journal of Personality and Social Psychology</secondary-title></titles><periodical><full-title>Journal of Personality and Social Psychology</full-title></periodical><pages>237-253</pages><volume>24</volume><number>2</number><dates><year>1972</year></dates><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Folkman</Author><Year>1986</Year><RecNum>11</RecNum><DisplayText>(Folkman et al., 1986; Lazarus & Folkman, 1984)</DisplayText><record><rec-number>11</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467875831">11</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Susan Folkman</author><author>Richard S. Lazarus</author><author>Christine Dunkel-Schetter</author><author>Anita DeLongis</author><author>Rand J. Gruen</author></authors></contributors><titles><title>Dynamics of a Stressful Encounter: Cognitive Appraisal, Coping, and Encounter Outcomes</title><secondary-title>Journal of Personality and Social Psychology</secondary-title></titles><periodical><full-title>Journal of Personality and Social Psychology</full-title></periodical><pages>992-1003</pages><volume>50</volume><number>5</number><dates><year>1986</year></dates><urls></urls></record></Cite><Cite><Author>Lazarus</Author><Year>1984</Year><RecNum>16</RecNum><record><rec-number>16</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467876577">16</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Richard S. Lazarus</author><author>Susan Folkman</author></authors></contributors><titles><title>Stress, Appraisal, and Coping</title></titles><dates><year>1984</year></dates><pub-location>New York</pub-location><publisher>Springer</publisher><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Folkman</Author><Year>1986</Year><RecNum>11</RecNum><DisplayText>(Folkman et al., 1986; Lazarus, 1966)</DisplayText><record><rec-number>11</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467875831">11</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Susan Folkman</author><author>Richard S. Lazarus</author><author>Christine Dunkel-Schetter</author><author>Anita DeLongis</author><author>Rand J. Gruen</author></authors></contributors><titles><title>Dynamics of a Stressful Encounter: Cognitive Appraisal, Coping, and Encounter Outcomes</title><secondary-title>Journal of Personality and Social Psychology</secondary-title></titles><periodical><full-title>Journal of Personality and Social Psychology</full-title></periodical><pages>992-1003</pages><volume>50</volume><number>5</number><dates><year>1986</year></dates><urls></urls></record></Cite><Cite><Author>Lazarus</Author><Year>1966</Year><RecNum>12</RecNum><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467875884">12</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Richard S. Lazarus</author></authors></contributors><titles><title>Psychological Stress and the Coping Process</title></titles><dates><year>1966</year></dates><pub-location>New York</pub-location><publisher>McGraw-Hill</publisher><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Monat</Author><Year>1976</Year><RecNum>17</RecNum><DisplayText>(Monat, 1976)</DisplayText><record><rec-number>17</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467876648">17</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Alan Monat</author></authors></contributors><titles><title>Temporal Uncertainty, Anticipation Time, and Cognitive Coping under Threat</title><secondary-title>Journal of Human Stress</secondary-title></titles><periodical><full-title>Journal of Human Stress</full-title></periodical><pages>32-43</pages><volume>2</volume><number>2</number><dates><year>1976</year></dates><urls></urls></record></Cite></EndNote>(Monat, 1976). Studies done in 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 ADDIN EN.CITE <EndNote><Cite><Author>Graham</Author><Year>2011</Year><RecNum>73</RecNum><DisplayText>(Graham, 2011; Steptoe et al., 2014)</DisplayText><record><rec-number>73</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1526868678">73</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Carol Graham</author></authors></contributors><titles><title>Adaptation amidst Prosperity and Adversity: Insights from Happiness Studies from around the World</title><secondary-title>World Bank Research Observer</secondary-title></titles><periodical><full-title>World Bank Research Observer</full-title></periodical><pages>105-137</pages><volume>26</volume><number>1</number><dates><year>2011</year></dates><urls></urls></record></Cite><Cite><Author>Steptoe</Author><Year>2014</Year><RecNum>70</RecNum><record><rec-number>70</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1526799093">70</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Andrew Steptoe</author><author>Angus Deaton</author><author>Arthur Stone</author></authors></contributors><titles><title>Subjective Wellbeing, Health, and Ageing</title><secondary-title>Lancet</secondary-title></titles><periodical><full-title>Lancet</full-title></periodical><pages>640-648</pages><volume>385</volume><number>9968</number><dates><year>2014</year></dates><urls></urls></record></Cite></EndNote>(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. Below, we express subjective well-being (both hedonic and evaluative) as a function of its determinants.Subjective Wellbeing = fcoping costs, 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 3.3.3. Data and methods3.1 Study settingOur 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 ADDIN EN.CITE <EndNote><Cite><Author>Government of Nepal</Author><Year>2011</Year><RecNum>28</RecNum><DisplayText>(Government of Nepal, 2011)</DisplayText><record><rec-number>28</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467880827">28</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Government of Nepal,</author></authors></contributors><titles><title>National Population and Housing Census</title></titles><dates><year>2011</year></dates><publisher>Central Bureau of Statistics</publisher><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Asian Development Bank (ADB)</Author><Year>2015</Year><RecNum>29</RecNum><DisplayText>(Asian Development Bank (ADB), 2015)</DisplayText><record><rec-number>29</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467881131">29</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Asian Development Bank (ADB),</author></authors></contributors><titles><title>Proposed Loan for Additional Financing Nepal: Kathmandu Valley Water Supply Improvement Project</title></titles><dates><year>2015</year></dates><pub-location>Manila, Philippines</pub-location><publisher>Asian Development Bank (ADB)</publisher><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Udmale</Author><Year>2016</Year><RecNum>30</RecNum><DisplayText>(Udmale et al., 2016)</DisplayText><record><rec-number>30</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467881294">30</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Parmeshwar Udmale</author><author>Hiroshi Ishidaira</author><author>Bhesh Raj Thapa</author><author>Narendra Man Shakya</author></authors></contributors><titles><title>The Status of DomesticWater Demand: Supply Deficit in the Kathmandu Valley, Nepal</title><secondary-title>Water</secondary-title></titles><periodical><full-title>Water</full-title></periodical><pages>1-9</pages><volume>8</volume><number>5</number><dates><year>2016</year></dates><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Katuwal</Author><Year>2011</Year><RecNum>46</RecNum><DisplayText>(Katuwal & Bohara, 2011)</DisplayText><record><rec-number>46</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1502171865">46</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Hari Katuwal</author><author>Alok K. Bohara</author></authors></contributors><titles><title>Coping with Poor Water Supplies: Empirical Evidence from Kathmandu, Nepal</title><secondary-title>Journal of Water and Health</secondary-title></titles><periodical><full-title>Journal of Water and Health</full-title></periodical><pages>143-158</pages><volume>9</volume><number>1</number><dates><year>2011</year></dates><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Tandan</Author><Year>2016</Year><RecNum>31</RecNum><DisplayText>(Tandan, 2016)</DisplayText><record><rec-number>31</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467881420">31</key></foreign-keys><ref-type name="Newspaper Article">23</ref-type><contributors><authors><author>Pramod Kumar Tandan</author></authors></contributors><titles><title>Valley Groundwater Level Drastically Low</title><secondary-title>The Himalayan Times</secondary-title></titles><dates><year>2016</year><pub-dates><date>May 12, 2016</date></pub-dates></dates><pub-location>Kathmandu</pub-location><urls><related-urls><url>;(Tandan, 2016). Moreover, groundwater depletion in Nepal has been exacerbated by climate change ADDIN EN.CITE <EndNote><Cite><Author>Xu</Author><Year>2009</Year><RecNum>78</RecNum><DisplayText>(Xu et al., 2009)</DisplayText><record><rec-number>78</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1527858973">78</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jianchu Xu</author><author>R. Edward Grumbine</author><author>Arun Shrestha</author><author>Mats Eriksson</author><author>Xuefei Yang</author><author>Yun Wang</author><author>Andreas Wilkes</author></authors></contributors><titles><title>The Melting Himalayas: Cascading Effects of Climate Change on Water, Biodiversity, and Livelihoods</title><secondary-title>Conservation Biology</secondary-title></titles><periodical><full-title>Conservation Biology</full-title></periodical><pages>520-530</pages><volume>23</volume><number>3</number><dates><year>2009</year></dates><urls></urls></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Gurung</Author><Year>2017</Year><RecNum>55</RecNum><DisplayText>(Gurung et al., 2017; Pattanayak et al., 2005)</DisplayText><record><rec-number>55</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1505737156">55</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Yogendra Gurung</author><author>Jane Zhao</author><author>Bal Kumar K.C.</author><author>Xun Wu</author><author>Bhim Suwal</author><author>Dale Whittington</author></authors></contributors><titles><title>The Costs of Delay in Infrastructure Investments: A Comparison of 2001 and 2014 Household Water Supply Coping Costs in the Kathmandu Valley, Nepal</title><secondary-title>Water Resources Research</secondary-title></titles><periodical><full-title>Water Resources Research</full-title></periodical><dates><year>2017</year></dates><urls></urls><electronic-resource-num>10.1002/2016WR019529</electronic-resource-num></record></Cite><Cite><Author>Pattanayak</Author><Year>2005</Year><RecNum>3</RecNum><record><rec-number>3</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1467874727">3</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Subhrendu K. Pattanayak </author><author>Jui-Chen Yang</author><author>Dale Whittington</author><author>K. C. Bal Kumar</author></authors></contributors><titles><title>Coping with Unreliable Public Water Supplies: Averting Expenditures by Households in Kathmandu, Nepal</title><secondary-title>Water Resources Research</secondary-title></titles><periodical><full-title>Water Resources Research</full-title></periodical><pages>1-11</pages><volume>41</volume><number>2</number><dates><year>2005</year></dates><urls></urls></record></Cite></EndNote>(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.3.2 Survey design and implementationThe 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 ADDIN EN.CITE <EndNote><Cite><Author>SILT Consultants and Development Research and Training Center</Author><Year>1999</Year><RecNum>53</RecNum><DisplayText>(SILT Consultants and Development Research and Training Center, 1999)</DisplayText><record><rec-number>53</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1503037480">53</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>SILT Consultants and Development Research and Training Center,</author></authors></contributors><titles><title>Consumer Survey for Project on Urban Water Supply and Sanitation Rehabilitation</title></titles><dates><year>1999</year></dates><pub-location>Kathmandu</pub-location><urls></urls></record></Cite></EndNote>(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, 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 analysisThe 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. The score is re-scaled such that a higher score means more positive psychological state. We use coping cost estimates from ADDIN EN.CITE <EndNote><Cite><Author>Gurung</Author><Year>2017</Year><RecNum>55</RecNum><DisplayText>(Gurung et al., 2017)</DisplayText><record><rec-number>55</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1505737156">55</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Yogendra Gurung</author><author>Jane Zhao</author><author>Bal Kumar K.C.</author><author>Xun Wu</author><author>Bhim Suwal</author><author>Dale Whittington</author></authors></contributors><titles><title>The Costs of Delay in Infrastructure Investments: A Comparison of 2001 and 2014 Household Water Supply Coping Costs in the Kathmandu Valley, Nepal</title><secondary-title>Water Resources Research</secondary-title></titles><periodical><full-title>Water Resources Research</full-title></periodical><dates><year>2017</year></dates><urls></urls><electronic-resource-num>10.1002/2016WR019529</electronic-resource-num></record></Cite></EndNote>(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 ADDIN EN.CITE <EndNote><Cite><Author>Gurung</Author><Year>2017</Year><RecNum>55</RecNum><DisplayText>(Gurung et al., 2017)</DisplayText><record><rec-number>55</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1505737156">55</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Yogendra Gurung</author><author>Jane Zhao</author><author>Bal Kumar K.C.</author><author>Xun Wu</author><author>Bhim Suwal</author><author>Dale Whittington</author></authors></contributors><titles><title>The Costs of Delay in Infrastructure Investments: A Comparison of 2001 and 2014 Household Water Supply Coping Costs in the Kathmandu Valley, Nepal</title><secondary-title>Water Resources Research</secondary-title></titles><periodical><full-title>Water Resources Research</full-title></periodical><dates><year>2017</year></dates><urls></urls><electronic-resource-num>10.1002/2016WR019529</electronic-resource-num></record></Cite></EndNote>(Gurung et al., 2017). 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. 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.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.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.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 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,Yihjt=β0+ φlnCopingCosth+?lnCopingCost2001h+γX'i+ρZ'h+ωj+δt +εi Eq(2)where, Yi 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; lnCopingCosth is the log monthly total coping cost in 2014; lnCopingCost2001h 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, log monthly household income in 2001 (inflated to 2014 rupees), household size, household water sources, and whether the household experienced any negative shock such as loss of job, death of family member, or divorce; ωj are ward fixed effects; and δt 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 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. 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. Time use data were collected using a 24-hour table that the respondent and spouse filled out as per a normal day. 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. 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. Results4.1. Descriptive statistics 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. 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.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 ADDIN EN.CITE <EndNote><Cite><Author>Suh</Author><Year>1996</Year><RecNum>77</RecNum><DisplayText>(Suh et al., 1996)</DisplayText><record><rec-number>77</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1527842878">77</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Suh, E.</author><author>Diener, E.</author><author>Fujita, F.</author></authors></contributors><titles><title>Events and Subjective Well-Being: Only Recent Events Matter</title><secondary-title>Journal of Personality and Social Psychology</secondary-title></titles><periodical><full-title>Journal of Personality and Social Psychology</full-title></periodical><pages>1091-1102</pages><volume>70</volume><number>5</number><dates><year>1996</year></dates><urls></urls></record></Cite></EndNote>(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 mechanismsAt 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. 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ADDIN EN.CITE.DATA (Pattanayak & Pfaff, 2009; Whittington & Pattanayak, 2014; World Health Organization (WHO), 2002). ADDIN EN.CITE <EndNote><Cite AuthorYear="1"><Author>Pattanayak</Author><Year>2009</Year><RecNum>49</RecNum><DisplayText>Pattanayak and Pfaff (2009)</DisplayText><record><rec-number>49</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1502172212">49</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Subhrendu K. Pattanayak</author><author>Alexander Pfaff</author></authors></contributors><titles><title>Behavior, Environment, and Health in Developing Countries: Evaluation and Valuation</title><secondary-title>Annual Review of Resource Economics</secondary-title></titles><periodical><full-title>Annual Review of Resource Economics</full-title></periodical><volume>1</volume><number>183-217</number><dates><year>2009</year></dates><urls></urls></record></Cite></EndNote>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 ADDIN EN.CITE <EndNote><Cite><Author>Devoto</Author><Year>2012</Year><RecNum>45</RecNum><DisplayText>(Devoto et al., 2012; Mahasuweerachai & Pangjai, 2017)</DisplayText><record><rec-number>45</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1502171532">45</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Florencia Devoto</author><author>Esther Duflo</author><author>Pascaline Dupas</author><author>William Pariente</author><author>Vincent Pons</author></authors></contributors><titles><title>Happiness on Tap: Piper Water Adoption in Urban Morocco</title><secondary-title>American Economic Journal: Economic Policy</secondary-title></titles><periodical><full-title>American Economic Journal: Economic Policy</full-title></periodical><pages>68-99</pages><volume>4</volume><number>4</number><dates><year>2012</year></dates><urls></urls></record></Cite><Cite><Author>Mahasuweerachai</Author><Year>2017</Year><RecNum>44</RecNum><record><rec-number>44</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1502171172">44</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Phumsith Mahasuweerachai</author><author>Siwarut Pangjai</author></authors></contributors><titles><title>Does Piped Water Improve Happiness? A Case from Asian Rural Communities</title><secondary-title>Journal of Happiness Studies</secondary-title></titles><periodical><full-title>Journal of Happiness Studies</full-title></periodical><dates><year>2017</year></dates><urls></urls><electronic-resource-num>10.1007/s10902-017-9875-9</electronic-resource-num></record></Cite></EndNote>(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. 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 ADDIN EN.CITE <EndNote><Cite AuthorYear="1"><Author>Gurung</Author><Year>2017</Year><RecNum>55</RecNum><DisplayText>Gurung et al. (2017)</DisplayText><record><rec-number>55</rec-number><foreign-keys><key app="EN" db-id="0efax2ztxepa2fer9t450p5nedttzft2p2e5" timestamp="1505737156">55</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Yogendra Gurung</author><author>Jane Zhao</author><author>Bal Kumar K.C.</author><author>Xun Wu</author><author>Bhim Suwal</author><author>Dale Whittington</author></authors></contributors><titles><title>The Costs of Delay in Infrastructure Investments: A Comparison of 2001 and 2014 Household Water Supply Coping Costs in the Kathmandu Valley, Nepal</title><secondary-title>Water Resources Research</secondary-title></titles><periodical><full-title>Water Resources Research</full-title></periodical><dates><year>2017</year></dates><urls></urls><electronic-resource-num>10.1002/2016WR019529</electronic-resource-num></record></Cite></EndNote>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. ConclusionThis 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 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. Tables and figuresFigure 1. Map of study areaSource: Gurung et al. (2017)Notes: Dots represent surveyed households.Table 1. Summary statisticsPanel A: Subjective well-being indicatorsObs.MeanS.D.MinMaxLife satisfaction8466.7001.777110MHI-578016.9281.9141123Panel B: Monthly coping costYear 2014 Total coping cost (NPR)15001205.5691041.2550.0009846.024Purchasing cost1500349.619608.8080.0007500.000Storage cost1500350.476496.7940.0004495.870Treatment cost1500117.320139.4100.0001608.118Pumping cost1500224.000360.2800.0004201.377Collection cost1500164.155568.4820.0009433.594Year 2001Total coping cost 2001 (in 2014 NPR)1500523.730903.5410.00017584.200Purchasing cost150043.167470.2870.0009185.557Storage cost150094.164183.2260.0001677.693Treatment cost1500106.459277.3990.0008857.260Pumping cost150031.29397.4670.0001672.914Collection cost1500248.646657.1880.00017442.190Panel C: Time use (hours/day)Time spent collecting water4190.7270.69504Time spent on productive activities4192.5133.642012.5Time spent on leisure4194.0072.587012Panel D: Household VariablesMonthly income 2014147536951.07042512.0700703000Monthly income 2001 (in 2014 NPR)147515854.02020500.990300309000Household size14755.1972.190115Household has experienced negative shock8460.0460.21001Panel E: Individual variablesRespondent age147549.61813.2071886Respondent gender14750.4910.50001Respondent education14758.5066.252018Respondent employment status14750.4960.50001Respondent is responsible for collecting water14750.2610.43901Respondent caste Chhetri14750.08401 Bhamhin – Hill 14750.15901 Newari14750.67501 Others14750.08201Notes: 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. Table 2. Coping cost and subjective well-beingPanel A: Life satisfactionLife SatisfactionLife SatisfactionLife SatisfactionLife Satisfaction(1)(2)(3)(4)Log monthly total coping cost0.188***0.185***0.281***0.298***(0.064)(0.064)(0.069)(0.069)N = 812812812812Panel B: MHI-5MHI-5MHI-5MHI-5MHI-5Log monthly total coping cost0.1310.1300.0330.136(0.083)(0.083)(0.093)(0.101)N = 749749749749Total monthly coping cost in 2001YYYYIndividual controlsaYYYYHousehold controlsbYYYYNegative shockcNYYYWater sourcedNNYYFixed effectseNNNYNotes:OLS regression models. Robust standard errors reported in parentheses. (*) p<0.1 (**) p<0.05 (***) p<0.001.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.Household controls include log monthly household income in 2014, log monthly household income in 2001, and household size. 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.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.Fixed effects include dummies to indicate the household ward and the day of week when the interview was conducted.Table 3. Mechanism: Coping cost components and life satisfactionLife SatisfactionLog monthly purchase cost0.034(0.027)Log monthly storage cost0.209***(0.039)Log monthly treatment cost0.059*(0.032)Log monthly pump cost0.021(0.038)Log monthly collection cost-0.004(0.039)N = 812Monthly coping costs in 2001aYIndividual controlsbYHousehold controlscYNegative shockdYWater sourceeYFixed effectsfYNotes:OLS regression models. Robust standard errors reported in parentheses. (*) p<0.1 (**) p<0.05 (***) p<0.001.Monthly coping costs in 2001 controls for all five coping costs in 2001.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.Household controls include log monthly household income in 2014, log monthly household income in 2001, and household size. 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.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.Fixed effects include dummies to indicate the household ward and the day of week when the interview was conducted.Table 4. Mechanism: Coping cost and time useDV: Time spent daily in hours onCollecting waterProductive activitiesLeisure(1)(2)(3)Log monthly total coping cost-0.102**0.0590.146(0.044)(0.130)(0.126)N = 403403403Total monthly coping cost in 2001YYYIndividual controlsaYYYHousehold controlsbYYYNegative shockcYYYWater sourcedYYYFixed effectseYYYNotes:OLS regression models. Robust standard errors reported in parentheses. (*) p<0.1 (**) p<0.05 (***) p<0.001.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.Household controls include log monthly household income in 2014, log monthly household income in 2001, and household size. 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.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.Fixed effects include dummies to indicate the household ward and the day of week when the interview was conducted.AppendicesTable A1. Variance inflation factor (VIF): Post-estimation after coping cost and life satisfaction OLS regressionVariablesVIFAge squared46.19Age45.26Caste 33.67Caste 22.58Caste 42.02Respondent education1.71Log total monthly coping cost1.62Water source 31.59Respondent employment1.57Water source 21.53Respondent gender1.51Log monthly household income 20011.26Log total monthly coping cost 20011.22Respondent responsible for collecting water1.18Household size1.13Water source 41.09Log monthly household income1.08Dummy negative shock1.03Mean VIF6.51Notes: 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. Table A2. Variance inflation factor (VIF): Post-estimation after coping cost and MHI-5 OLS regressionVariablesVIFAge squared45.86Age44.94Caste 33.76Caste 22.66Caste 42.04Respondent education1.73Log total monthly coping cost1.60Water source 31.58Respondent employment1.55Respondent gender1.51Water source 21.50Log monthly household income 20011.24Log total monthly coping cost 20011.21Dummy respondent responsible for collecting water1.18Household size1.14Log monthly household income1.10Water source 41.09Dummy negative shock1.04Mean VIF6.48Notes: 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.Table A3. Coping cost and subjective well-being – Ordered logit regressionsLife SatisfactionLife SatisfactionLife SatisfactionLife Satisfaction(1)(2)(3)(4)Log monthly total coping cost0.253***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 40.1300.2120.043-0.692(1.215)(1.212)(1.223)(1.330)Cut point 52.015*2.103*1.9801.416(1.221)(1.219)(1.232)(1.341)Cut point 62.703**2.793**2.688**2.221*(1.224)(1.221)(1.234)(1.339)Cut point 73.511***3.606***3.527***3.171**(1.226)(1.224)(1.236)(1.339)Cut point 84.790***4.888***4.852***4.603***(1.230)(1.228)(1.240)(1.338)Cut point 96.004***6.102***6.099***5.901***(1.250)(1.248)(1.263)(1.361)N =812812812812Total monthly coping cost in 2001YYYYIndividual controlsaYYYYHousehold controlsbYYYYNegative shockcNYYYWater sourcedNNYYFixed effectseNNNYNotes: Reported coefficients are from ordered logit models. Robust standard errors reported in parentheses. (*) p<0.1 (**) p<0.05 (***) p<0.001.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.Household controls include log monthly household income in 2014, log monthly household income in 2001, and household size. 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.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.Fixed effects include dummies to indicate the household ward and the day of week when the interview was conducted.Figure A1. Path diagramNotes:Figure A1 represents theoretical path diagram.All variables are assumed to be observed.Arrows point to direct effects in the shown direction.Model estimates are presented in Tables A4 and A5.Table A4. Direct, indirect, and total effects of coping cost on life satisfaction from SEMDirect effectCoefficientS.E.Life satisfactionLog monthly total coping cost0.173***0.065Age-0.0120.029Age squared0.0000.000Gender-0.0410.143Caste 20.524**0.258Caste 3-0.534**0.232Caste 4-0.1460.294Respondent education0.026**0.012Respondent employment0.2140.145Dummy respondent responsible for collecting water0.216*0.125Log monthly household income0.0230.030Log monthly household income 20010.151*0.082Dummy household experienced negative shock-0.548*0.285Log monthly total coping costAge0.043***0.014Age squared0.000***0.000Gender-0.0130.069Caste 2-0.0380.125Caste 3-0.385***0.115Caste 4-0.1840.144Respondent education0.039***0.006Respondent employment-0.131*0.071Dummy respondent responsible for collecting water0.112*0.061Log monthly household income0.0230.015Log monthly household income 20010.153***0.041Log monthly total coping cost 20010.041*0.023Household size0.034**0.014Water source 20.222***0.075Water source 30.880***0.068Water source 40.0250.061Indirect effectLife satisfactionAge0.007**0.004Age squared0.000**0.000Gender-0.0020.012Caste 2-0.0070.022Caste 3-0.067**0.032Caste 4-0.0320.028Respondent education0.007**0.003Respondent employment-0.0230.015Dummy respondent responsible for collecting water0.0190.013Log monthly household income0.0040.003Log monthly household income 20010.027**0.012Log monthly total coping cost 20010.0070.005Household size0.006*0.003Water source 20.039**0.020Water source 30.153***0.059Water source 40.0040.011Total effectLife satisfactionLog monthly total coping cost0.173***0.065Age-0.0050.029Age squared0.0000.000Gender-0.0430.143Caste 20.518**0.259Caste 3-0.601***0.231Caste 4-0.1780.295Respondent education0.033***0.011Respondent employment0.1920.146Dummy respondent responsible for collecting water0.235*0.126Log monthly household income0.0270.030Log monthly household income 20010.178**0.081Dummy household experienced negative shock-0.548*0.285Log monthly total coping cost 20010.0070.005Household size0.006*0.003Water source 20.038**0.019Water source 30.152***0.058Water source 40.0040.011Log monthly total coping costAge0.043***0.014Age squared0.000***0.000Gender-0.0130.069Caste 2-0.0380.125Caste 3-0.385***0.115Caste 4-0.1840.144Respondent education0.039***0.006Respondent employment-0.131*0.071Dummy respondent responsible for collecting water0.112*0.061Log monthly household income0.0230.015Log monthly household income 20010.153***0.041Log monthly total coping cost 20010.041*0.023Household size0.034**0.014Water source 20.222***0.075Water source 30.880***0.068Water source 40.0250.061Goodness-of-fit testsRoot mean square error of approximation (RMSEA)0.084Standardized root mean square residual (SRMR)0.014Comparative fit index (CFI)0.932Notes:Reported coefficients are from linear structural equation models. Robust standard errors reported in parentheses. (*) p<0.1 (**) p<0.05 (***) p<0.001.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.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.Goodness-of-fit RMSEA test indicates that the model fits adequately well. SRMR and CFI indicate that the model fits well. Table A4. Direct, indirect, and total effects of coping cost on MHI-5 from SEMDirect effectCoefficientS.E.MHI-5Log monthly total coping cost0.140*0.078Age-0.078**0.035Age squared0.001**0.000Gender-0.1280.171Caste 20.0150.308Caste 30.0060.280Caste 40.0830.355Respondent education-0.0010.014Respondent employment0.2810.173Dummy respondent responsible for collecting water-0.2410.148Log monthly household income0.0350.035Log monthly household income 2001-0.0370.097Dummy household experienced negative shock-0.0860.332Log monthly total coping costAge0.042***0.015Age squared0.000***0.000Gender0.0150.072Caste 2-0.0630.130Caste 3-0.422***0.121Caste 4-0.259*0.150Respondent education0.036***0.006Respondent employment-0.130*0.073Dummy respondent responsible for collecting water0.0800.064Log monthly household income0.029*0.015Log monthly household income 20010.138***0.042Log monthly total coping cost 20010.038*0.023Household size0.029*0.015Water source 20.244***0.078Water source 30.903***0.070Water source 40.0050.063Indirect effectMHI-5Age0.0060.004Age squared0.0000.000Gender0.0020.010Caste 2-0.0090.019Caste 3-0.0590.037Caste 4-0.0360.029Respondent education0.005*0.003Respondent employment-0.0180.014Dummy respondent responsible for collecting water0.0110.011Log monthly household income0.0040.003Log monthly household income 20010.0190.012Log monthly total coping cost 20010.0050.004Household size0.0040.003Water source 20.0340.022Water source 30.126*0.071Water source 40.0010.009Total effectMHI-5Log monthly total coping cost0.140*0.078Age-0.072**0.034Age squared0.001**0.000Gender-0.1260.171Caste 20.0060.309Caste 3-0.0530.278Caste 40.0460.355Respondent education0.0040.014Respondent employment0.2630.173Dummy respondent responsible for collecting water-0.2300.149Log monthly household income0.0390.035Log monthly household income 2001-0.0170.096Dummy household experienced negative shock-0.0860.332Log monthly total coping cost 20010.0050.004Household size0.0040.003Water source 20.0340.022Water source 30.126*0.071Water source 40.0010.009Log monthly total coping costAge0.042***0.015Age squared0.000***0.000Gender0.0150.072Caste 2-0.0630.130Caste 3-0.422***0.121Caste 4-0.259*0.150Respondent education0.036***0.006Respondent employment-0.130*0.073Dummy respondent responsible for collecting water0.0800.064Log monthly household income0.029*0.015Log monthly household income 20010.138***0.042Log monthly total coping cost 20010.038*0.023Household size0.029*0.015Water source 20.244***0.078Water source 30.903***0.070Water source 40.0050.063Goodness-of-fit testsRoot mean square error of approximation (RMSEA)0.041Standardized root mean square residual (SRMR)0.007Comparative fit index (CFI)0.978Notes:Reported coefficients are from linear structural equation models. 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