Social networks and agricultural performance: A multiplex analysis of interactions among indian rice farmers

Most network studies in agriculture examine uni-dimensional connections between individuals to understand the effect of social networks on outcomes. However, in most real-world scenarios, network members' exchanges happen through multiple relationships and not accounting for such multi-dimensional interconnections may lead to biased estimate of social network effects. This study aims to unravel the consequences of not accounting such multidimensional networks by investigating the individual and joint effects of multiple connections (relationships) that exist among households on agricultural output. We use census data from three villages of Odisha, India that enables us to account for three types of relationships viz. information networks (knowledge sharing), credit networks (resource sharing) and friendship (social bonding) between households. We estimate the social network effect by combining both econometric (IV regression) and network (directed networks) techniques to address the problems of endogeneity. The joint effect of multiple networks is estimated using the multiplex network framework. We find that information flows are crucial to improve agricultural output when networks are accounted individually. However, the joint effect of all three networks using multiplex shows a significantly positive influence, indicating complementarity across relationships. In addition, we found evidence for the mediating role of interpersonal relationships (friendship network) in enhancing gains from the information flow.