全球智库信息集成服务系统(G2TT): DP16084 Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP16084
	DP16084 Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization
	Esther Duflo; Abhijit Banerjee; John Floretta; Anna Schrimpf; Anirudh Sankar; Francine Loza; Harini Kannan; Matthew O. Jackson; Arun G. Chandrasekhar; Maheshwor Shrestha; Suresh Dalpath
发表日期	2021-04-28
出版年	2021
语种	英语
摘要	We evaluate a large-scale set of interventions to increase demand for immunization in Haryana, India. The policies under consideration include the two most frequently discussed tools—reminders and incentives—as well as an intervention inspired by the networks literature. We cross-randomize whether (a) individuals receive SMS reminders about upcoming vaccination drives; (b) individuals receive incentives for vaccinating their children; (c) influential individuals (information hubs, trusted individuals, or both) are asked to act as “ambassadors” receiving regular reminders to spread the word about immunization in their community. By taking into account different versions (or “dosages”) of each intervention, we obtain 75 unique policy combinations. We develop a new statistical technique—a smart pooling and pruning procedure—for finding a best policy from a large set, which also determines which policies are effective and the effect of the best policy. We proceed in two steps. First, we use a LASSO technique to collapse the data: we pool dosages of the same treatment if the data cannot reject that they had the same impact, and prune policies deemed ineffective. Second, using the remaining (pooled) policies, we estimate the effect of the best policy, accounting for the winner’s curse. The key outcomes are (i) the number of measles immunizations and (ii) the number of immunizations per dollar spent. The policy that has the largest impact (information hubs, SMS reminders, incentives that increase with each immunization) increases the number of immunizations by 44 % relative to the status quo. The most cost-effective policy (information hubs, SMS reminders, no incentives) increases the number of immunizations per dollar by 9.1%.
主题	Development Economics
关键词	Development economics Immunization India Reminders Incentives Smart pooling
URL	https://cepr.org/publications/dp16084
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/545062
推荐引用方式 GB/T 7714	Esther Duflo,Abhijit Banerjee,John Floretta,et al. DP16084 Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization. 2021.