Making networks work for policy: evidence from agricultural technology adoption in Malawi

Abstract

The International Initiative for Impact Evaluation (3ie) is an international grant-making NGO promoting evidence-informed development policies and programmes. We are the global leader in funding, producing and synthesising high-quality evidence of what works, how, why and at what cost. We believe that better and policy-relevant evidence will make development more effective and improve people's lives. 3ie impact evaluations3ie-supported impact evaluations assess the difference a development intervention has made to social and economic outcomes. 3ie is committed to funding rigorous evaluations that include a theory-based design, use the most appropriate mix of methods to capture outcomes and are useful in complex development contexts. About this report SummaryThe slow adoption of agricultural technologies, particularly in Africa, is a persistent puzzle. Given that subsistence agriculture serves as the main income source for the majority of Africa's rural poor, interventions which increase the use of profitable agricultural technologies can have a major impact on development. One potential constraint to adoption of such technologies is lack of credible information. New technologies can diffuse through interpersonal ties; as social network members are often the most credible source of information. In order to increase adoption, we apply models of network theory on rich social network data from 200 villages in Malawi to identify seed farmers who would maximize technology adoption in theory. A randomized controlled trial, in collaboration with the Ministry of Agriculture and Food Security, compares theory-driven network targeting approaches to simpler, scalable strategies that either rely on a government extension worker or an easily measurable proxy for the network (geographic distance) to identify seed farmers. Adoption rates over three years are greater in villages that received the theory-based data intensive treatments.To assess whether the theoretical predictions in diffusion models can help inform and improve public policy, we test whether training 'theoretically optimal' diffusion partners leads to greater adoption of a new technology. Selecting these partners required a social network census which we collected in 200 villages across three districts in Malawi. On those 200 networks, we simulated the optimal partners under different assumptions about the median threshold, determined who would be the best choices for that diffusion model, and gave their names to the Malawi Ministry of Agriculture and Food Security (MoAFS) extension workers for training on two types of conservation agriculture. The first, pit planting, is a practice largely unknown at base line, the second technique is crop residue management (CRM). We then trace adoption patterns in these villages over the next two to three seasons to test which sets of partners are most effective at getting farmers to adopt the new technology.We benchmark the adoption in villages with our theoretically-optimal seeds against the status quo treatment where exten...

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