Sanitation Demand and Strategic Complementarities

Raymond Guiteras, Maryland Jim Levinsohn, Yale

A. Mushfiq Mobarak, Yale

Partners: Innovations for Poverty Action; Wateraid, Bangladesh; Bill and Melinda Gates Foundation; VERC, Bangladesh

Policy Issue

•  1 billion people (15% of world population) practice open defecation –  UNICEF spent $380 million on WSH programs for

children in 2012. –  World Bank WSP directed US $200 million for sanitation

for 50 million people in 2011-2015

•  Acute problem in high-density areas of South Asia

•  India’s Total Sanitation Campaign (NBA) produced mixed results. –  New emphasis on behavior change

Debates in the Sector

•  Supply-side strategies or Behavior Change? •  Both Supply and Demand constraints need to be

addressed? •  Within Demand strategies, should we subsidize?

– Causes dependencies, kills markets?

•  Part of the research design simply evaluates different strategies.

Theory Building: Interdependencies in Household Decisions

•  Latrine adoption decisions may be interdependent –  Epidemiological Complementarity –  Social spillovers – learning/shame/status

•  If these interdependencies are significant, how can we use them to improve interventions? –  e.g. what’s the threshold to push over to the “good

equilibrium”? –  Should we subsidize? –  How should subsidies be targeted?

Context

•  4 rural sub-districts (roughly comparable to U.S. counties) in Tanore district in Bangladesh

Sample and Methods •  Study Sample:

–  4 of 7 sub-districts (unions), 115 villages, 372 neighborhoods (paras), 18,000 households

–  32% open defecation at baseline •  Main outcomes of interest:

–  Investment in any latrines, –  Investments in hygienic latrines (cost US$ 30-60) –  Open defecation

•  Only near-landless (poorest 75%) eligible for subsidies –  Approximately half of unsubsidized cost –  Vary share of poor households offered subsidy (25%, 50%,

75%) •  Randomization at either village or neighborhood level

Experimental Design

Summary of Research Design •  2-by-2 of demand and supply:

– Control –  Supply only – Demand (LPP + Subsidy) only – Demand (LPP + Subsidy) + Supply

•  Plus: LPP only – Comparing LPP Only with LPP + Subsidy gives

marginal contribution of subsidies

•  Within Subsidy communities, randomize intensity (share of eligibles winning)

Identifying Mechanisms behind Complementarities in Demand

•  Collected detailed social network data at baseline – Useful for identifying demand spillovers from specific

types of social connections using IV methods: •  Children’s friends’ parents (epidemiological channel) •  Community leaders (social influence channel) •  Technically competent individuals (learning channel)

•  Experimentally varied targeting of subsidies to socially-connected or socially-marginal households

Are the latrines used?

Experimentally Identifying Demand Inter-dependencies

•  Holding a household’s own price constant, how does investment depend on the share of its neighbors receiving subsidies? – Designed random variation

•  Three samples: –  “Lucky” Eligibles who won subsidy vouchers –  “Unlucky” Eligibles who lost in the lottery –  Ineligibles

Mechanisms behind Social Multiplier •  Attempt to distinguish between

–  technical complementarity –  Social influence (shame, or changes in norms) – Learning

•  Using –  IV methods on detailed social network data – Experiments on the way subsidies were targeted

•  To socially connected versus marginal households •  Early adopter discounts

– Detailed data on sequence and dates of purchase

75% HHs

provided latrine

coverage

30% HHs

provided latrine

coverage

Random selection for flat subsidies

Random selection for early adopter discounts

Random selection for flat subsidies

Random selection for early adopter discounts

Random selection for flat subsidies

Random selection for early adopter discounts

Random selection for flat subsidies

Random selection for early adopter discounts

Treatment 2: Demand side

interventions only (LPP + subsidy)

Treatment 3: Demand (LPP+

subsidy) and supply side interventions

Random selection for flat subsidies

Random selection for early adopter discounts

Random selection for flat subsidies

Random selection for early adopter discounts

18% HHs

provided latrine

coverage

Treatment 4: Only

supply side interventions

Control

~115 clusters ~115 clusters ~35 clusters ~35 clusters

Treatment 1: Only LPP

interventions

~35 clusters

Design is even more Complicated!

IV:  Effect  Social  Contact's  ownership/access  in  r4  on  household's  ownership/access    

Latrine  Ownership  Hygienic  Latrine  

Ownership  Eligible  Sample   Eligible  Sample  

VARIABLES  

R4  Ownership  Rate  among    :  Playmate  Contacts   0.0655*   0.103**  (0.0387)   (0.0399)  

R4  Ownership  Rate  among    :  Interact  Contacts   0.211**   0.222***  (0.0878)   (0.0793)  

R4  Ownership  Rate  among    :  Resolve  Contacts   -­‐0.0688*   -­‐0.121***  (0.0377)   (0.0406)  

R4  Ownership  Rate  among    :  Technical  Contacts   0.0409   0.0679**  (0.0296)   (0.0330)  

Won  Latrine  only   0.0924***   0.127***  (0.0141)   (0.0146)  

Won  Tin  only   0.0677***   0.0229  (0.0171)   (0.0170)  

Won  both   0.175***   0.224***  (0.0142)   (0.0153)  

ObservaRons   12,948   12,948  R-­‐squared   0.081   0.100  Union  Fixed  Effects?   Yes   Yes  1st  parRal  R2   0.137   0.132  1st  F-­‐test   38.61   31.60  

*  Controlled  subsidy  eligibility  of  contacts  and  baseline  latrine/hygienic  latrine  ownership  rate  

IV:  Effect  Social  Contact's  ownership/access  in  r4  on  household's  ownership/access     HCI   Non-­‐HCI  

Latrine  Ownership  

Hygienic  Latrine  Ownership  

Latrine  Ownership  

Hygienic  Latrine  Ownership  

Eligible  Sample   Eligible  Sample   Eligible  Sample   Eligible  Sample  VARIABLES  

R4  Ownership  Rate  :  Playmate  Contacts   0.0314   0.126**   0.0541   0.0126  (0.0474)   (0.0556)   (0.0513)   (0.0620)  

R4  Ownership  Rate    :  Interact  Contacts   0.0586   0.104   0.464***   0.360***  (0.0957)   (0.0940)   (0.122)   (0.119)  

R4  Ownership  Rate    :  Resolve  Contacts   -­‐0.101**   -­‐0.149***   -­‐0.0886   -­‐0.125**  (0.0478)   (0.0511)   (0.0571)   (0.0506)  

R4  Ownership  Rate    :  Technical  Contacts   0.00483   0.0364   0.0308   0.0456  (0.0443)   (0.0458)   (0.0368)   (0.0446)  

Won  Latrine  only   0.0870***   0.123***   0.0761***   0.122***  (0.0257)   (0.0239)   (0.0187)   (0.0205)  

Won  Tin  only   0.0515**   0.0271   0.0770***   0.00895  (0.0238)   (0.0269)   (0.0272)   (0.0231)  

Won  both   0.166***   0.222***   0.169***   0.218***  (0.0252)   (0.0245)   (0.0193)   (0.0199)  

ObservaRons   4,255   4,255   8,674   8,674  R-­‐squared   0.057   0.081   0.056   0.088  Union  Fixed  Effects?   Yes   Yes   Yes   Yes  1st  parRal  R2   0.296   0.242   0.103   0.0947  1st  F-­‐test   39.74   31.39   19.89   12.61  

*  Controlled  subsidy  eligibility  of  contacts  and  baseline  latrine/hygienic  latrine  ownership  rate  

Targeting Subsidies to the Socially Connected

Eligible Eligible HCI HCI Non-HCI Non-HCI

VARIABLES HCI Village

Non-HCI Village Won Latrine Lost Latrine Won Latrine Lost Latrine

Won Latrine only 0.0695*** 0.0498* (0.0260) (0.0265)

Won Tin only 0.0481* 0.0526 (0.0248) (0.0353)

Won both 0.156*** 0.136*** (0.0245) (0.0287)

Treatment: Medium Intensity

0.0385 0.0807** -0.00124 0.0895** 0.120*** 0.0250

(0.0297) (0.0344) (0.0362) (0.0371) (0.0373) (0.0465) Treatment: High Intensity

0.0446 0.0724* 0.0184 0.0667* 0.106** 0.0253

(0.0307) (0.0417) (0.0364) (0.0354) (0.0468) (0.0511)

Structural Demand Estimation

•  Specify a random utility model, and estimate parameters:

•  Leads to BLP-style two step estimation: 1. Cluster fixed effects regression:

2. Experiments provide instruments for prices:

Why?

•  What is the optimal way to allocate subsidies? – Tradeoff between subsidizing more people

versus increasing intensity of subsidies

•  Who should we subsidize? •  Identify thresholds using variation within

clusters (experiment only tried 3 thresholds)

2nd step

END Thank you

Sample of “Ineligibles” (richest 25%)