| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28726 |
| 来源ID | Working Paper 28726 |
| Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization | |
| Abhijit Banerjee; Arun G. Chandrasekhar; Suresh Dalpath; Esther Duflo; John Floretta; Matthew O. Jackson; Harini Kannan; Francine N. Loza; Anirudh Sankar; Anna Schrimpf; Maheshwor Shrestha | |
| 发表日期 | 2021-05-03 |
| 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 | Econometrics ; Estimation Methods ; Experimental Design ; Microeconomics ; Economics of Information ; Health, Education, and Welfare ; Health ; Development and Growth ; Development |
| URL | https://www.nber.org/papers/w28726 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/586400 |
| 推荐引用方式 GB/T 7714 | Abhijit Banerjee,Arun G. Chandrasekhar,Suresh Dalpath,et al. Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w28726.pdf（1286KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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