Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials
Review of Economic Studies · 2019 · Abhijit Banerjee、Arun G. Chandrasekhar、Esther Duflo、Matthew O. Jackson
中文摘要
我们能否在不收集网络数据的情况下，仅仅通过询问社区成员来识别网络中的高中心性个体？与随机选择的人甚至受尊敬的人相比，通过此类被提名的个体播种信息能否带来显著更广的扩散？在印度进行的两项独立的大规模现场实验中，我们对这两个问题均给出了肯定的回答。具体而言，在哈里亚纳邦的521个村庄中，我们将有关每月免疫接种营的信息提供给随机选择的个体（在部分村庄），或提供给被村民提名为擅长传播信息者的个体（在其他村庄）。我们发现，在被提名者收到信息的村庄中，每月接种的儿童数量高出22%。我们表明，人们对谁是高中心性个体和优秀播种者的了解，可以用一个模型来解释：在该模型中，社区成员仅仅是追踪他们听到关于他人的闲话的频率。事实上，我们在第三个数据集中发现，被提名的播种者在网络意义上是中心化的，而不仅仅是拥有许多朋友或身处有权势的位置。
Abstract
Abstract 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.