Abstract: How social and economic networks govern many real-life phenomena has been in the focus of a range of studies in the past decade. Estimation of such effects is, however, hindered by a range of issues not present in traditional data structures. This paper proposes an approach to address the problem of omitted variable bias caused by correlated social networks and disentangle peer effects in more complex social situations. The difference between the traditional approach and the multiplex network estimation is illustrated with data from a technology adoption field experiment in Uganda.
Wednesday, May 12, 2021, 12:00 pm – 1:00 pm