Log In Sign Up

Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization

by   Abhijit Banerjee, et al.

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 status quo. The most cost-effective policy (information hubs, SMS reminders, no incentives) increases the number of immunizations per dollar by 9.1


page 36

page 37


Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health

With the recent advancements in wearables and sensing technology, health...

Evidence-Based Policy Learning

The past years have seen seen the development and deployment of machine-...

Spillover Effects in Cluster Randomized Trials with Noncompliance

Clustered randomized trials (CRTs) are popular in the social sciences to...

Data-driven Optimization Model for Global Covid-19 Intervention Plans

In the wake of COVID-19, every government huddles to find the best inter...

Policy Learning with Competing Agents

Decision makers often aim to learn a treatment assignment policy under a...

Interference, Bias, and Variance in Two-Sided Marketplace Experimentation: Guidance for Platforms

Two-sided marketplace platforms often run experiments to test the effect...

Modelling the impact of repeat asymptomatic testing policies for staff on SARS-CoV-2 transmission potential

Repeat asymptomatic testing in order to identify and quarantine infectio...