  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%.
