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)
• Roughly 1300 HH’s in each
survey round (7-8 at each
spring of 184 springs)
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
• 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
Before WaterGuard distribution
After WaterGuard distribution
After – Before difference (s.e.)
% Change in use/contamination
Treatment
mean (s.d.)
Comparison
mean (s.d.)
T – C (s.e.)
0.03
0.02
0.01
(0.18)
(0.15)
(0.01)
0.59
0.07
0.52
(0.49)
(0.25)
(0.02)***
0.55
0.04
0.51
(0.02)***
(0.01)***
(0.02)***
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