Can Network Theory-based Targeting Increase Technology Adoption?

Abstract

We thank the CEGA/JPAL Agricultural Technology Adoption Initiative (ATAI) and 3ie for financial support. Beaman acknowledges support by the National Science Foundation under grant no. 1254380. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

The role of interactive radio programming in advancing women’s empowerment and crop and dietary diversity: Mixed methods evidence from Malawi

“…Other sources of advice (such as government and NGO extension) affect the outcome variables, and therefore combining radio messaging and other sources matters. This is consistent with Beaman et al (2015), who note that technology diffusion and behavior change are characterized by a complex learning environment in which most farmers need to learn from multiple people and sources before they adopt themselves.…”

Compliance Behavior in Networks: Evidence from a Field Experiment

“…High concentration resolves the latter issue at the cost of reaching fewer households. High concentration should hence be particularly desirable if households need to hear sufficiently often about a mailing in order to change behavior, as is the case in, e.g., threshold models (Granovetter 1978, Centola and Macy 2007, Beaman et al 2015.…”

“…Geographic networks have been shown to matter in such diverse domains as households' energy consumption (Allcott 2011), blood donations (Bruhin et al 2014), or the diffusion of knowledge of the tax code (Chetty, Friedman, and Saez 2013). Beaman et al (2015), who study technology adoption, show that seeding based on geographic networks works fairly well. While seeding based on a complex model of elicited social networks increases spillovers, the geographic network approach is cheaper and easier to implement.…”

Show and Tell: Causal Impacts of Field Days on Farmer Learning for Organic Inputs in Kenya

“…However, because FFS are also expensive, there has been a search for alternative methods of information diffusion. This has ranged from using mobile phones to disseminate information (Aker, 2011), which many in rural areas of SSA now own, to using network analysis to target optimal entry points of information into social networks in order to maximize information diffusion from key farmers (Banerjee et al, 2013;Beaman et al, 2015). A significant literature has emerged analyzing the impacts of learning from fellow farmers in developing countries (Foster and Rosenzweig, 1995;Bandiera and Rasul, 2006;Conley and Udry, 2010;BenYishay and Mobarak, 2018).…”