Official websites use .gov
Secure .gov websites use HTTPS
Selecting the most effective nudge: evidence from a large scale experiment on immunization
Selecting the most effective nudge: evidence from a large scale experiment on immunization
- Collection:
- Health Policy and Services Research
- Series Title(s):
- Working paper (Stanford Institute for Economic Policy Research)
- Author(s):
- Banerjee, Abhijit, author
Chandrasekhar, Arun G., author
Dalpath, Suresh, author
Duflo, Esther, author
Floretta, John, author
Jackson, Matthew O., author
Kannan, Harini, author
Loza, Francine N., author
Sankar, Anirudh, author
Schrimpf, Anna, author
Shrestha, Maheshwor, author - Contributor(s):
- Stanford Institute for Economic Policy Research, issuing body.
- Publication:
- [Stanford, CA] : Stanford Institute for Economic Policy Research (SIEPR), April 2021
- Language(s):
- English
- Format:
- Text
- Subject(s):
- Health Promotion -- methods
Immunization
Immunization Programs
Motivation
India
United States - Genre(s):
- Technical Report
- Abstract:
- 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%.
- Copyright:
- Reproduced with permission of the copyright holder. Further use of the material is subject to CC BY-NC-ND license. (More information)
- Extent:
- 1 online resource (1 PDF file (61 pages))
- Illustrations:
- Illustrations
- NLM Unique ID:
- 9918383971506676 (See catalog record)
- Permanent Link:
- http://resource.nlm.nih.gov/9918383971506676