Working Paper: CEPR ID: DP3341
Authors: Oriana Bandiera; Imran Rasul
Abstract: Despite their potentially strong impact on poverty, agricultural innovations are often adopted slowly. Using a unique household dataset on sunflower adoption in Mozambique, we analyse whether and how individual adoption decisions depend upon the choices of others in the same social networks. Since farmers anticipate that they will share information with others, we expect farmers to be more likely to adopt when they know many other adopters. Dynamic considerations, however, suggest that farmers who know many adopters might strategically delay adoption to free-ride on the information gathered by others. We present empirical evidence that shows that the relationship between the probability of adoption and the number of known adopters is shaped as an inverse-U. In line with information sharing, the network effect is stronger for farmers who report discussing agriculture with others. The data contains information that is needed to ameliorate the identification issues that commonly arise in this context. In particular social networks are precisely identified, and in addition we can control for village hetereogeneity and for endogenous group formation.
Keywords: Information Sharing; Social Networks; Technology Adoption
JEL Codes: O12; O31
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Farmers' knowledge of adopters (Q16) | Probability of adoption (C25) |
| Probability of adoption (C25) | Information sharing (O36) |
| Network structure (D85) | Probability of adoption (C25) |
| Number of known adopters (D16) | Probability of individual adoption (C25) |
| Farmers discussing practices (Q15) | Network effects strength (D85) |
| Unaccounted nonlinearities (C51) | Downward bias in estimates (C51) |