Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials

• Abhijit Banerjee ^[2] ; Arun G. Chandrasekhar ^[1] ; Esther Duflo ^[2] ; Matthew O. Jackson ^[1]
  1. [1] Stanford University

     Stanford University

     Estados Unidos

     
  2. [2] MIT
• Localización: Review of economic studies, ISSN 0034-6527, Vol. 86, Nº 6, 2019, págs. 2453-2490
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Can we identify highly central individuals in a network without collecting network data, simply by asking community members? Can seeding information via such nominated individuals lead to significantly wider diffusion than via randomly chosen people, or even respected ones? In two separate large field experiments in India, we answer both questions in the affirmative. In particular, in 521 villages in Haryana, we provided information on monthly immunization camps to either randomly selected individuals (in some villages) or to individuals nominated by villagers as people who would be good at transmitting information (in other villages). We find that the number of children vaccinated every month is 22% higher in villages in which nominees received the information. We show that people’s knowledge of who are highly central individuals and good seeds can be explained by a model in which community members simply track how often they hear gossip about others. Indeed, we find in a third data set that nominated seeds are central in a network sense, and are not just those with many friends or in powerful positions.