In this article the authors examine the motivation behind the formation of risk pools. They do so by using as suitable study data survey information collected among the rural poor of the Philippines. They discuss the possibility that network formation comes as the result of an attempt to...

We conduct a clustered randomized controlled trial across 180 villages in Uttar Pradesh, India, to promote the take-up of a savings commitment product newly introduced to our study population. A random subset of participants was targeted through our promotional campaign to test whether the...

Many studies have used self-reported dyadic data without exploiting the pattern of discordant answers. In this paper we propose a maximum likelihood estimator that deals with mis-reporting in a systematic way. We illustrate the methodology using dyadic data on inter-household transfers (gifts...

We study how knowledge about the social network of an individual researcher, as embodied in his coauthor relations, helps us in developing a more accurate prediction of his or her future productivity. We find that incorporating information about coauthor networks leads to a modest improvement in...

Previous analyses of the formation and composition of community-based organizations (CBOs) have used cross section data. So, causal inference has been compromised. We obviate this problem by using data from a quasi-experiment in which villages were formed by government officials selecting and...

Many studies have used self-reported dyadic data without exploiting the pattern of discordant answers. In this paper we propose a maximum likelihood estimator that deals with mis-reporting in a systematic way. We illustrate the methodology using dyadic data on inter-household transfers (gifts...

Using village data from Tanzania, we test whether gifts and loans between households are voluntary while correcting for mis-reporting by the giving and receiving households. Two maintained assumptions underlie our analysis: answers to a question on who people would turn to for help are good...