Trickle Down: Diffusion of Chlorine for Drinking Water Treatment in Kenya

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

 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

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

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

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?

Intervention

Baseline survey (Aug 2004 – Feb 2005) 47 of 184 springs protected 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 Follow-up survey #3 (Jan – Mar 2007) Post-intervention social network data collected • 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

Data

 Water Quality   Tested for levels of fecal indicator bacteria

E. coli

home (all 4 survey rounds) at spring and in 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

Before WaterGuard distribution After WaterGuard distribution After – Before difference (s.e.) % Change in use/contamination Panel A: Dependent variable, Water tested positive for chlorine Treatment mean (s.d.) 0.03

Comparison mean (s.d.) 0.02

T – C (s.e.) 0.01

(0.18) 0.59

(0.49) 0.55

(0.02)*** 55% (0.15) 0.07

(0.25) 0.04

(0.01)*** 4% (0.01) 0.52

(0.02)*** 0.51

(0.02)*** 51%

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)  1.

Find a variable that is correlated with chlorine use 2.

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

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  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

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



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

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

 Why is take-up so low / high?



Who

isn’t using it?

 Can we say anything about

why

(externalities?) they don’t use it?  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