Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization
Abhijit Banerjee,
Arun Chandrasekhar,
Suresh Dalpath,
Esther Duflo,
John Floretta,
Matthew Jackson,
Harini Kannan,
Francine N. Loza,
Anirudh Sankar,
Anna Schrimpf and
Maheshwor Shrestha
No 28726, NBER Working Papers from National Bureau of Economic Research, Inc
Abstract:
Policymakers often choose a policy bundle that is a combination of different interventions in different dosages. We develop a new technique—treatment variant aggregation (TVA)—to select a policy from a large factorial design. TVA pools together policy variants that are not meaningfully different and prunes those deemed ineffective. This allows us to restrict attention to aggregated policy variants, consistently estimate their effects on the outcome, and estimate the best policy effect adjusting for the winner’s curse. We apply TVA to a large randomized controlled trial that tests interventions to stimulate demand for immunization in Haryana, India. The policies under consideration include reminders, incentives, and local ambassadors for community mobilization. Cross-randomizing these interventions, with different dosages or types of each intervention, yields 75 combinations. The policy with the largest impact (which combines incentives, ambassadors who are information hubs, and reminders) increases the number of immunizations by 44% relative to the status quo. The most cost-effective policy (information hubs, ambassadors, and SMS reminders but no incentives) increases the number of immunizations per dollar by 9.1% relative to status quo.
JEL-codes: C18 C93 D83 I15 O12 O15 (search for similar items in EconPapers)
Date: 2021-04
New Economics Papers: this item is included in nep-net
Note: CH DAE DEV EH LS PE POL
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Citations: View citations in EconPapers (24)
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Working Paper: Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization (2021) 
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