Do Egocentric information networks influence technical efficiency of farmers? Empirical evidence from Ghana

Abstract

We investigate the impact of farmers’ egocentric information network on technical efficiency and its distribution in the network, using observational data of 600 farmers from northern Ghana. We exploit community detection algorithms to endogenously identify homogeneous network communities with known structures to account for spatial heterogeneity, in a spatial stochastic frontier model that controls for social selection bias. The empirical results reveal that at the global network level, farmers’ technical efficiency strongly correlate with that of farmers in their egocentric networks. Our findings also show that farmers who are technically less efficient tend to depend on the more efficient farmers in their networks to improve efficiency. We further find that estimating spatial dependence of technical efficiency without accounting for spatial heterogeneity can lead to underestimation of technical efficiency of high (efficiency score >0.6) performing farmers, while overestimating that of medium (efficiency scores between 0.36–0.5) and low (efficiency scores between 0.1–0.35) performing farmers. The findings suggest that identifying central farmers in egocentric networks and improving their technical knowledge in a farmer-to-farmer extension organization, can contribute to improving the productivity of many farmers.

Information acquisition and the adoption of improved crop varieties

“…The recent literature tends to indicate that even if policies relax the mediating constraints like markets, infrastructure, and institutions, but constraints on supply‐side factors, such as local availability and access to information about the new technologies, exist, low adoption is still likely to persist (Bandiera & Rasul, 2006; Conley & Udry, 2010; De Janvry et al, 2016; Mohammed & Abdulai, 2022). As argued by Beaman and Dillon (2018), lack of reliable and persuasive sources of information about new technologies, their relevance to local agronomic conditions, and details on how to apply them are potential deterrents to adoption.…”

“…The insufficient availability and limited access to public learning has led to considerable interest in social learning. Given the significance of social learning, the impact of farmers' information networks and extension services on technology adoption has received considerable attention in the theoretical and empirical literature (e.g., Bandiera & Rasul, 2006; Conley & Udry, 2010; Foster & Rosenzweig, 1995; Krishnan & Patnam, 2013; Maertens & Barrett, 2012; Mohammed & Abdulai, 2022), with mixed findings, as studies by Bandiera and Rasul (2006), Conley and Udry (2010), and Beaman et al (2021) reported positive effects of social learning on adoption, whereas a few others (e.g., Duflo et al, 2011) found no effects. An issue that has become clear with observational data is that unless the social networks are exogenously formed, they suffer from the problem of identifying the network effects (Chandrasekhar & Lewis, 2016; Manski, 1993).…”

Impacts of extension dissemination and technology adoption on farmers' efficiency and welfare in Ghana: Evidence from legume inoculant technology

“…In agriculture, new production technologies such as high yielding varieties and complementary inputs like fertilizer (or as in our case, the legume inoculant technology) have the potential to shift the production frontier upwards (Huang and Liu, 1994;Kumbhakar and Tsionas, 2009;Triebs and Kumbhakar, 2018). Also, farmers who receive extension services or technical training on the new technology may experience further shift in the production frontier upwards by reducing production inefficiencies (Mohammed and Abdulai, 2022a). The two shifts involve two potentially endogenous treatments in a single agricultural development intervention that incorporates dissemination of new production technologies and training of farmers.…”