SELECTING THE MOST EFFECTIVE NUDGE: EVIDENCE FROM A LARGE-SCALE EXPERIMENT ON IMMUNIZATION

• 成果类型: Article
• 署名作者: Banerjee, Abhijit; Chandrasekhar, Arun G.; Dalpath, Suresh; Duflo, Esther; Floretta, John; Jackson, Matthew O.; Kannan, Harini; Loza, Francine; Sankar, Anirudh; Schrimpf, Anna; Shrestha, Maheshwor
• 署名单位: Massachusetts Institute of Technology (MIT); National Bureau of Economic Research; Stanford University; The Santa Fe Institute; The World Bank
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA19739
• 发表日期: 2025
• 页码: 1183-1223
• 关键词: middle-income countries; TEXT MESSAGE REMINDERS; VACCINATION COVERAGE; confidence-intervals; controlled-trial; model selection; inference; Lasso; strategies; parameters

摘要

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 the status quo.