Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization
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%.
We are particularly grateful to the Haryana Department of Health and Family Welfare for taking the lead on this intervention and allowing the evaluation to take place. Among many others, we acknowledge the tireless support of Rajeev Arora, Amneet P. Kumar, Sonia Trikha, V.K. Bansal, Sube Singh, and Harish Bisht. We are also grateful to Isaiah Andrews and Karl Rohe for helpful discussions. We thank Emily Breza, Denis Chetvetrikov, Paul Goldsmith-Pinkham, Nargiz Kalantarova, Shane Lubold, Tyler McCormick, Francesca Molinari, Douglas Miller, Suresh Naidu, Eric Verhoogen, and participants at various seminars for suggestions. Financial support from USAID DIV, 3iE, J-PAL GPI, Givewell, and NSF grant SES-2018554 is gratefully acknowledged. Chandrasekhar is grateful to the Alfred P. Sloan foundation for support. We thank Chitra Balasubramanian, Tanmayta Bansal, Aicha Ben Dhia, Maaike Bijker, Rajdev Brar, Shreya Chaturvedi, Vasu Chaudhary, Shobitha Cherian, Rachna Nag Chowdhuri, Mohar Dey, Laure Heidmann, Mridul Joshi, Sanjana Malhotra, Deepak Pradhan, Diksha Radhakrishnan, Anoop Singh Rawat, Devinder Sharma, Vidhi Sharma, Niki Shrestha, Paul-Armand Veillonand Meghna Yadav for excellent research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.