Presentation on theme: "SPRING CLEANING NOTES Trickle Down: Diffusion of Chlorine for Drinking Water Treatment in Kenya -- This work is joint with Michael Kremer of Harvard, Ted."— Presentation transcript:
1SPRING CLEANING NOTESTrickle Down: Diffusion of Chlorine for Drinking Water Treatment in Kenya-- This work is joint with Michael Kremer of Harvard, Ted Miguel & Clair Null of U.C. Berkeley, and Alix Zwane of Google.Org.Michael Kremer, Harvard University and NBEREdward Miguel, U.C. Berkeley and NBERClair Null, U.C. BerkeleyAlix Zwane, google.org
2The Economics of Rural Water SPRING CLEANING NOTESThe Economics of Rural WaterSource water improvements vs point-of-use (POU)Source water improvements serve many households simultaneously, thus require cooperation; POU is private decision by HHPossibility of recontamination during storage & transportChild death is one of the great health problems in the world, and water-borne disease is one of the main causes.[The others are malaria, respiratory infections](2) In rural areas of Africa, improved water service almost always means some kind of communal site that is some distance from the home. Piped water to dispersed rural households is usually expensive, infeasible.
3The Rural Water Project (RWP) SPRING CLEANING NOTESThe Rural Water Project (RWP)Randomized evaluation of alternative water interventions in rural western KenyaSource water quality improvementPoint-of-use water treatment (chlorination)Increased water quantityAlternative institutions for community maintenance of water sourcesThis paper: we study distribution of 6-month supply of free sodium hypochlorite (WaterGuard) to a subset of households in 184 rural Kenyan communitiesSpring protection does not create a new water point so the distance that a household must walk to get water is unchanged.-- In contrast, well construction changes both the quality and quantity of water, so an evaluation of well provision cannot be used to help resolve this debate on whether it is more useful to have higher quality drinking water versus lots of water (for washing and bathing) that could be of lower quality. Distinction: water-borne disease versus water-washed disease
4SPRING CLEANING NOTESProject BackgroundChild mortality in Kenya is high at 120 per 1000 live births (2005), and even higher in rural areasDiarrheal disease is a leading causeLack of knowledge about diarrhea & POU’s doesn’t seem to be a major problem:72% of study households volunteer that “dirty water” is a cause of diarrhea87% of study households have previously heard of WaterGuardBut take-up is low: only 3% of study households have chlorine in water prior to interventionThe majority of people in Kenya live in rural areas, without household piped water connections, and they get their drinking water from sources where there is a high risk of contamination because of environmental exposure – meaning basically that fecal matter and associated pathogens can be washed into the water.
5SPRING CLEANING NOTESResearch Questions1) What are the impacts of free chlorine distribution on:-- Home water quality?-- Child health?-- Household behaviors?2) What is the relationship between clean water & diarrhea?3) How does information about chlorine spread through a community?-- Is there a “tipping point” for network effects?-- What sorts of relationships are relevant?-- What types of people are influential?4) How does the distribution of free chlorine affect social networks & conversation patterns in the community?Close ties between this project and Spring Cleaning for cost-benefit analysis of source water quality improvements versus point of use.First goals similar to spring cleaning – household & child level effects.Novel aspect of this project is the ability to directly observe how information diffuses throughout the community, from those who got WG to those who didn’t. Will hopefully also be able to distinguish informational network effects versus spreading of the free chlorine by sharing (so far, not quite there).
6Intervention Baseline survey (Aug 2004 – Feb 2005) SPRING CLEANING NOTESInterventionBaseline survey (Aug 2004 – Feb 2005)47 of 184 springs protectedRoughly 1300 HH’s in each survey round (7-8 at each spring of 184 springs)695 HH’s given mL bottles of WaterGuard (approx. 6 month supply); 673 HH’s in comparison groupTwo “intensity” levels of WaterGuard intervention:at 92 springs, 6 of 8 HH in treatment groupat 92 springs, 2 of 8 HH in treatment groupFollow-up survey #1 (Apr – Aug 2005)Pre-intervention social network data collected93 of 184 springs protectedFollow-up survey #2 (Aug – Nov 2006)WaterGuard intervention conductedTiming:WG intervention cross-cut with spring protection (springs protected between survey rounds, WG intervention conducted as part of survey round)Networks data from 2nd survey roundOne bottle of WG lasts approx. 1 month, depending on HH size, using only for drinking/kids, etc.; knew there would be pressure to share so gave sufficient to last until return visit, between 2-7 months after interventionBottle costs less than ½ day’s wage (about as much as a bottle of soda)Follow-up survey #3 (Jan – Mar 2007)Post-intervention social network data collected
7SPRING CLEANING NOTESDataWater QualityTested for levels of fecal indicator bacteria E. coli at spring and in home (all 4 survey rounds)Tested for residual chlorine in home water (last 2 survey rounds)Household SurveyWater collection (source choice, number of trips, walking distance) and water-related behaviorsHygiene knowledge, sanitationChild health (diarrhea), anthropometricsHousehold demographic, socioeconomic variablesSocial networks dataall pair-wise combinations of study households within spring communityfrequency of conversations about children’s health problems, drinking water, & chlorine
8Take-Up Panel A: Dependent variable, SPRING CLEANING NOTESTake-UpPanel A: Dependent variable,Water tested positive for chlorineTreatmentmean (s.d.)ComparisonT – C (s.e.)Before WaterGuard distribution0.030.020.01(0.18)(0.15)(0.01)After WaterGuard distribution0.590.070.52(0.49)(0.25)(0.02)***After – Before difference (s.e.)0.550.040.51(0.01)***% Change in use/contamination55%4%51%Low baseline usage (randomization successful)Suggestive evidence of spillover effects from T to C HH’sHuge increase in use among T groupStill 40% of HH’s that didn’t adopt – why? Doesn’t seem to be for lack of free chlorine; not correlated with # of bottles left from HH accounting or with time between survey rounds. From baseline data, among those who had previously used WG (around 42% of sample), very favorable impressions of product. 94% able to volunteer at least one valid health benefit of WG. Taste often hypothesized as potential impediment to take-up but only 10% said tasted bad with the rest saying tasted good, and “sweetening” often volunteered as reason to use WG.Usage data corroborate chlorine tests; according to HH accounting, seems that ½ of HH’s were chlorinating consistently & appropriately based on elapsed time & number of bottles usedNot seeing any synergies with hygiene or HH characteristics (mother’s education, # kids)
9Household Water Quality 70% reduction in contamination (intention to treat effect)Improvements even for households at springs with low pre-intervention contaminationBut not all treatment households had evidence of chlorine in their waterHow much did water quality improve among households who actually used the chlorine? (effect of the treatment on the treated)
10Estimating the ToTChoice to use free chlorine could be related to other decisions that affect water qualityNeed to separate effect of chlorine from effects of other decisionsCan use instrumental variable technique – estimate causal effect of chlorine on water quality by using some source of exogenous variation in chlorine use (not related to other decisions)Find a variable that iscorrelated with chlorine usebut has no effect on water quality other than through its relationship with chlorine use
11Assignment to Treatment as an Instrument Probability that a household uses chlorine is affected by assignment to treatment groupBut assignment to treatment doesn’t affect water quality other than through its effect on probability that a household uses chlorine (thanks to randomization)Focus on variation in chlorine use induced by intervention in order to estimate the effect of chlorine on water quality (specifically for those who actually used the chlorine because of the intervention)Since roughly half of treatment households used chlorine, we would expect water quality improvements for these households to be twice as large as the intention to treat effectStill don’t know how chlorine would have affected water quality for treated households who didn’t use it
12SPRING CLEANING NOTESChild EffectsDiarrhea prevalence of 20% among kids 3 or younger in control householdsPre-intervention difference in diarrhea between treatment & control children of 4 percentage points (22% versus 18%, respectively; significant at 95%)Treatment associated with ~8 percentage point reduction in diarrhea on average (significant at 95%)No differential treatment effects for boys versus girls or on the basis of other household characteristics (latrines, hygiene knowledge, mother’s education, etc.)
13Social Networks 75% of relationships same-tribe Types of relationships SPRING CLEANING NOTESSocial Networks75% of relationships same-tribeTypes of relationships65% of relationships are familialNon-familial relationships all categorized as neighborsFrequency of contact: “close” if talk 2-3 times per week or more60% of relationships are close14% of pairs are with a household the respondent does not know1.8 close contacts to treatment households on average20% of households had no close contacts to treatmentRelatively ethnically homogenous (given recent political developments, important statistic)Let HH’s volunteer descriptions of their relationships with one anotherCommon familial relationship types reflect survey protocol (mother of youngest child) and cultural tradition of moving to husband’s village: 20% are mother/daughter-in-law and 25% are wife of brother-in-law
1433 had at least one close contact in treatment group SPRING CLEANING NOTESAssuming linear effects, each close contact increases probability of take-up by 2%pts (from base of only 3%, so this is big)Network effects aren’t significantly different for T HH’s (maybe positive reinforcement important for them, too)Neither 2nd degree (close contacts of my close contacts) nor acquaintences (not shown) seem to matterPotentially necessary to have multiple contactsFor T HH’s, network effects likely positive reinforcement (they already have the stuff, just need to use it)For C HH’s, what is the mechanism for network effects?(4 didn’t have network data)35 is 81% - in Hm2 only 20% of HH’s report having purchased chlorine, and only 15% of comparison HH’s who didn’t test + for chlorine reported purchasing itAmong 43 comparison households with chlorine in their water at follow-up:33 had at least one close contact in treatment group35 reported purchasing chlorine in past six months14 reported receiving WaterGuard as a gift
15Take-Up Related to Networks For each close contact in treatment group, household is 2 percentage points more likely to have chlorine in waterRegardless of the household’s own treatment statusSmall effect relative to increase in take-up due to treatment, but huge for control households (50% increase)Among 43 comparison households with chlorine in water at follow-up:33 had at least one close contact in treatment group35 reported purchasing chlorine in past six months14 reported receiving WaterGuard as a giftSuggestive of non-linearities (imprecisely estimated)Community leaders particularly influential (households without latrines particularly non-compelling)
16Changes in Conversation Patterns SPRING CLEANING NOTESChanges in Conversation PatternsTreatment households areRoughly 30% more likely to report talking about drinking waterAlmost three times as likely to report talking about WaterGuardIf a household’s conversation partner was in treatment group, respondent wasAround 20% more likely to report talking about drinking waterSlightly more than twice as likely to report talking about WaterGuardTreatment didn’t seem to affect close relationships, though slight evidence that if either of HH’s were treated, they were more likely to list one another as being at least acquaintances at follow-up. Statistically sig at 90% but not economically relevant.Clearly very effective at prompting conversations about WG specifically and drinking water more generally.
17Summary Intervention was successful (at least in the short run) at: increasing water chlorinationreducing water contaminationpreventing diarrheaprompting conversations about WaterGuard & drinking water more generallySocial networks in the community do seem to influence take-up of the productPossibly non-linear effects (low power to estimate)Community leaders are key
18Questions for Future Work SPRING CLEANING NOTESQuestions for Future WorkWhy is take-up so low / high?Who isn’t using it?Can we say anything about why they don’t use it? (externalities?)What is the binding constraint to reducing diarrhea?Chlorine doesn’t kill everythingHygiene practicesWhat will happen in the long(er)-run? Adoption of free chlorine versus adoption of purchased chlorineCoupon study
19External ValidityTake-up rates would likely vary according to local perceptionsWater quality effects might be more stableScientific, rather than behavioralChild health depends on many factors, including sanitationNetwork effects likely context specificFinding that community leaders are influential might be generalizable
20Scaling Up Intervention conducted in order to: Facilitate cost-benefit comparisons between alternate technologiesTrack how information spreads through a communityNot designed with scale in mindRelated project examining potentially scale-able means of encouraging chlorine adoptionInfrastructureMonitoring
21ConclusionUnderstanding leakage of intervention is explicit goal of studyStill don’t know exact channels for social network effectsClear example of the differences between the:intention to treat effectaveraging over all treated households, including both those who did and did not use the chlorineeffect of the treatment on the treatedusing assignment to treatment as an instrument for chlorine useNot always as easy to distinguish those who “take” the treatment from those who don’tIn this case, test for presence of chlorine in the water