DP16084 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%.
