Modelling technology adoption in a social interaction framework evidence on improved Cassava Varieties in Colombia

Abstract

This study analyzes the impact of social interactions on the adoption of a new technology. Based on the basic model of social interactions with non-cooperative choices, we provide empirical evidence of spillovers or neighborhood effects on the individual probability of adoption of improved cassava varieties in the State of Cauca, Colombia. We use a sample of socio-economic characteristics at the household level and a set of covariates at the municipal levelforcassavasmall-holder farmers to test peer effects on individual choices. Weimplement a machine learning clustering algorithm that allows us to measure the information channel and the degree of interaction between adopters and non-adopters by using the household’s geographical coordinates to calculate physical distance using the Global Position System (GPS). The regression results show that the average village adoption rate has a significant effect on the household’s decision to use modern, improved varieties of cassava seeds. The resultsalsosuggestthat,astheminimumdistanceinkilometersbyroadfromthenon-adopter to an adopter increases, the probability of adoption decreases. Our results are robust to the so-called correlated non-observables identification problem widely recognized in literature on social interactions models. Finally, this work leads to highlight the role of simultaneity or reflection problem also widely identified in theory. The empirical evidence does not support the presence of simultaneity. However, the future analysis requires detailed information at agent’s network level. We also provide suggestive evidence of potential learning effects by conditioningonadopterswhoaretechnologiesspecific-userssuchasweedcontrol,fertilizers, etc. This is in accord with stylized facts that highlight the importance of knowing more about techniques in the process of adoption. The results show no difference in the estimated coefficients between the neighborhood effects and the suggestive evidence of learning. Future research needs to build better identification mechanism as part of the analysis on social networks, diffusion and learning. This study represents a first step for understanding the role of peer effects on the improvement of living conditions for rural population via technological enhancements.