Institutional Affiliation: University of Chicago
|Incentivized Peer Referrals for Tuberculosis Screening: Evidence from India|
with Jessica Goldberg, Mario Macis: w25279
We use a field experiment with 3,176 patients at 122 tuberculosis treatment clinics in India to test whether peer referrals increase screening and identification of patients with an infectious disease. Low-cost financial incentives considerably raise the probability that current patients refer prospective patients for screening and testing, resulting in the cost-effective identification of new tuberculosis cases. Incentivized referrals operate through two mechanisms: peers have private information about individuals in their social networks (beyond their immediate families) to target for outreach, and peers are more effective than traditional contact tracing by paid health workers in inducing these individuals to get tested.
|Information, Learning, and Drug Diffusion: the Case of Cox-2 Inhibitors |
with Renna Jiang, Ginger Z. Jin: w14252
The recent withdrawal of Cox-2 Inhibitors has generated debate on the role of information in drug diffusion: can the market learn the efficacy of new drugs, or does it depend solely on manufacturer advertising and FDA updates? In this study, we use a novel data set to study the diffusion of three Cox-2 Inhibitors ? Celebrex, Vioxx and Bextra ? before the Vioxx withdrawal. Our study has two unique features: first, we observe each patient?s reported satisfaction after consuming a drug. This patient level data set, together with market level data on FDA updates, media coverage, academic articles, and pharmaceutical advertising, allows us to model individual prescription decisions. Second, we distinguish across-patient learning of a drug?s general efficacy from the within-patient learning of t...
Published: Pradeep Chintagunta & Renna Jiang & Ginger Jin, 2009. "Information, learning, and drug diffusion: The case of Cox-2 inhibitors," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 399-443, December.