SPRING CLEANING NOTES Trickle 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 NBER Edward Miguel, U.C. Berkeley and NBER Clair Null, U.C. Berkeley Alix Zwane, google.org

The Economics of Rural Water SPRING CLEANING NOTES The Economics of Rural Water Source water improvements vs point-of-use (POU) Source water improvements serve many households simultaneously, thus require cooperation; POU is private decision by HH Possibility of recontamination during storage & transport Child 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.

The Rural Water Project (RWP) SPRING CLEANING NOTES The Rural Water Project (RWP) Randomized evaluation of alternative water interventions in rural western Kenya Source water quality improvement Point-of-use water treatment (chlorination) Increased water quantity Alternative institutions for community maintenance of water sources This paper: we study distribution of 6-month supply of free sodium hypochlorite (WaterGuard) to a subset of households in 184 rural Kenyan communities Spring 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

SPRING CLEANING NOTES Project Background Child mortality in Kenya is high at 120 per 1000 live births (2005), and even higher in rural areas Diarrheal disease is a leading cause Lack 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 diarrhea 87% of study households have previously heard of WaterGuard But take-up is low: only 3% of study households have chlorine in water prior to intervention The 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.

SPRING CLEANING NOTES Research Questions 1) 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).

Intervention Baseline survey (Aug 2004 – Feb 2005) SPRING CLEANING NOTES Intervention Baseline survey (Aug 2004 – Feb 2005) 47 of 184 springs protected Roughly 1300 HH’s in each survey round (7-8 at each spring of 184 springs) 695 HH’s given 7 150 mL bottles of WaterGuard (approx. 6 month supply); 673 HH’s in comparison group Two “intensity” levels of WaterGuard intervention: at 92 springs, 6 of 8 HH in treatment group at 92 springs, 2 of 8 HH in treatment group Follow-up survey #1 (Apr – Aug 2005) Pre-intervention social network data collected 93 of 184 springs protected Follow-up survey #2 (Aug – Nov 2006) WaterGuard intervention conducted Timing: 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 round One 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 intervention Bottle 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

SPRING CLEANING NOTES Data Water Quality Tested 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 Survey Water collection (source choice, number of trips, walking distance) and water-related behaviors Hygiene knowledge, sanitation Child health (diarrhea), anthropometrics Household demographic, socioeconomic variables Social networks data all pair-wise combinations of study households within spring community frequency of conversations about children’s health problems, drinking water, & chlorine

Take-Up Panel A: Dependent variable, SPRING CLEANING NOTES Take-Up Panel A: Dependent variable, Water tested positive for chlorine Treatment mean (s.d.) Comparison T – C (s.e.) Before WaterGuard distribution 0.03 0.02 0.01 (0.18) (0.15) (0.01) After WaterGuard distribution 0.59 0.07 0.52 (0.49) (0.25) (0.02)*** After – Before difference (s.e.) 0.55 0.04 0.51 (0.01)*** % Change in use/contamination 55% 4% 51% Low baseline usage (randomization successful) Suggestive evidence of spillover effects from T to C HH’s Huge increase in use among T group Still 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 used Not seeing any synergies with hygiene or HH characteristics (mother’s education, # kids)

Household Water Quality 70% reduction in contamination (intention to treat effect) Improvements even for households at springs with low pre-intervention contamination But not all treatment households had evidence of chlorine in their water How much did water quality improve among households who actually used the chlorine? (effect of the treatment on the treated)

Estimating the ToT Choice to use free chlorine could be related to other decisions that affect water quality Need to separate effect of chlorine from effects of other decisions Can 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 is correlated with chlorine use but has no effect on water quality other than through its relationship with chlorine use

Assignment to Treatment as an Instrument Probability that a household uses chlorine is affected by assignment to treatment group But 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 effect Still don’t know how chlorine would have affected water quality for treated households who didn’t use it

SPRING CLEANING NOTES Child Effects Diarrhea prevalence of 20% among kids 3 or younger in control households Pre-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.)

Social Networks 75% of relationships same-tribe Types of relationships SPRING CLEANING NOTES Social Networks 75% of relationships same-tribe Types of relationships 65% of relationships are familial Non-familial relationships all categorized as neighbors Frequency of contact: “close” if talk 2-3 times per week or more 60% of relationships are close 14% of pairs are with a household the respondent does not know 1.8 close contacts to treatment households on average 20% of households had no close contacts to treatment Relatively ethnically homogenous (given recent political developments, important statistic) Let HH’s volunteer descriptions of their relationships with one another Common 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

33 had at least one close contact in treatment group SPRING CLEANING NOTES Assuming 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 matter Potentially necessary to have multiple contacts For 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 it Among 43 comparison households with chlorine in their water at follow-up: 33 had at least one close contact in treatment group 35 reported purchasing chlorine in past six months 14 reported receiving WaterGuard as a gift

Take-Up Related to Networks For each close contact in treatment group, household is 2 percentage points more likely to have chlorine in water Regardless of the household’s own treatment status Small 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 group 35 reported purchasing chlorine in past six months 14 reported receiving WaterGuard as a gift Suggestive of non-linearities (imprecisely estimated) Community leaders particularly influential (households without latrines particularly non-compelling)

Changes in Conversation Patterns SPRING CLEANING NOTES Changes in Conversation Patterns Treatment households are Roughly 30% more likely to report talking about drinking water Almost three times as likely to report talking about WaterGuard If a household’s conversation partner was in treatment group, respondent was Around 20% more likely to report talking about drinking water Slightly more than twice as likely to report talking about WaterGuard Treatment 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.

Summary Intervention was successful (at least in the short run) at: increasing water chlorination reducing water contamination preventing diarrhea prompting conversations about WaterGuard & drinking water more generally Social networks in the community do seem to influence take-up of the product Possibly non-linear effects (low power to estimate) Community leaders are key

Questions for Future Work SPRING CLEANING NOTES Questions for Future Work Why 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 everything Hygiene practices What will happen in the long(er)-run? Adoption of free chlorine versus adoption of purchased chlorine Coupon study

External Validity Take-up rates would likely vary according to local perceptions Water quality effects might be more stable Scientific, rather than behavioral Child health depends on many factors, including sanitation Network effects likely context specific Finding that community leaders are influential might be generalizable

Scaling Up Intervention conducted in order to: Facilitate cost-benefit comparisons between alternate technologies Track how information spreads through a community Not designed with scale in mind Related project examining potentially scale-able means of encouraging chlorine adoption Infrastructure Monitoring

Conclusion Understanding leakage of intervention is explicit goal of study Still don’t know exact channels for social network effects Clear example of the differences between the: intention to treat effect averaging over all treated households, including both those who did and did not use the chlorine effect of the treatment on the treated using assignment to treatment as an instrument for chlorine use Not always as easy to distinguish those who “take” the treatment from those who don’t In this case, test for presence of chlorine in the water