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

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Trickle DownDiffusion 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

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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

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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

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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 POUs doesnt
  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

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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?

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Intervention
Baseline survey (Aug 2004 Feb 2005)
47 of 184 springs protected

• Roughly 1300 HHs in each survey round (7-8 at
  each spring of 184 springs)
• 695 HHs given 7 150 mL bottles of WaterGuard
  (approx. 6 month supply) 673 HHs 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-intervent
ion 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-interven
tion social network data collected
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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 childrens
  health problems, drinking water, chlorine

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Take-Up
Panel A Dependent variable, Water tested positive for chlorine Panel A Dependent variable, Water tested positive for chlorine Panel A Dependent variable, Water tested positive for chlorine
Treatment mean (s.d.) Comparison mean (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
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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)

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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

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Assignment to Treatment as an Instrument

• Probability that a household uses chlorine is
  affected by assignment to treatment group
• But assignment to treatment doesnt 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 dont know how chlorine would have affected
  water quality for treated households who didnt
  use it

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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,
  mothers education, etc.)

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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

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• 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

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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 households 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)

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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 households 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

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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

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Questions for Future Work

• Why is take-up so low / high?
• Who isnt using it?
• Can we say anything about why they dont use it?
  (externalities?)
• What is the binding constraint to reducing
  diarrhea?
• Chlorine doesnt kill everything
• Hygiene practices
• What will happen in the long(er)-run? Adoption
  of free chlorine versus adoption of purchased
  chlorine
• Coupon study

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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

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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

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Conclusion

• Understanding leakage of intervention is explicit
  goal of study
• Still dont 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 dont
• In this case, test for presence of chlorine in
  the water