Trickle Down:Diffusion of Chlorine for Drinking

Water Treatment in Kenya

Michael Kremer, Harvard University and NBEREdward Miguel, U.C. Berkeley and NBER

Clair Null, U.C. BerkeleyAlix 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)

Follow-up survey #1 (Apr – Aug 2005)

Pre-intervention social network data collected

Follow-up survey #2 (Aug – Nov 2006)

WaterGuard intervention conducted

Follow-up survey #3 (Jan – Mar 2007)

Post-intervention social network data collected

47 of 184 springs protected

93 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

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,Water tested positive for chlorine

Treatmentmean (s.d.)

Comparisonmean (s.d.) 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

(0.02)*** (0.01)*** (0.02)***

% Change in use/contamination 55% 4% 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)

Find a variable that is

1. 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 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