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Learning from experts and peer farmers about rice production: Experimental evidence from Cote d’Ivoire

Kazushi Takahashi, Yukichi Mano () and Keijiro Otsuka ()

World Development, 2019, vol. 122, issue C, 157-169

Abstract: Technological innovation is vital to economic growth and food security in sub-Saharan Africa where agricultural productivity has been stagnant for a long time. Extension services and learning from peer farmers are two common approaches to facilitate the diffusion of new technologies, but little is known about their relative effectiveness. Selection bias, whereby well-motivated training participants would perform better even without extension services, as well as knowledge spillovers, where non-participants can indirectly benefit from extension services, are among the major threats to causal inference. Using a unique sequential randomized experiment on agricultural training, this study attempts to meet the dual objectives of executing rigorous impact evaluation of extension services and subsequent spillovers on rice production in Cote d’Ivoire. Specifically, to reduce selection bias, we randomly assigned eligibility for training participation; and to satisfy the stable unit treatment value assumption, control-group farmers were initially restricted from exchanging information with treated-group farmers who had received rice management training. Once some positive impacts were confirmed, information exchange between the treated and control farmers was encouraged. We found that the initial performance gaps created by the randomized assignment disappeared over time, due presumably to social learning from peer farmers. A detailed analysis concerning the information network and peer effects provided suggestive evidence that there were information and technology spillovers from treated to control farmers after removing the information exchange restriction. Overall, our study demonstrates that information dissemination by farmers can be as effective in improving practices as the initial training provided by extension services.

Keywords: Network analysis; Program evaluation; Randomized experiment; Spillovers (search for similar items in EconPapers)
JEL-codes: O12 O13 O31 Q12 (search for similar items in EconPapers)
Date: 2019
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