decision maker’s choices (or expectations over them). Obviously, if information is made available outside of a controlled experimental setting, more than one person per village can obtain it and demand complementarities or substitutabilities kick in. This makes the decision over the value of others’ learning much more complex. While my experiment cannot speak to this by design, it also provides a scenario in which adoption is bounded from below and the valuation decision has high stakes. If none of the participants buys the training, no one in the village will adopt the techniques.

# **5.2 Belief Update Intervention**

My willingness-to-pay elicitation survey included a belief update experiment to understand how concerns for _i)_ knowledge externalities and _ii)_ contagion externalities affect how much a farmer values training one other farmer.

Knowledge externalities between two farmers depend on the probability that the two farmers share agricultural information with each other _p_<sup>_L_</sup> (I restrict my analysis to direct communication between farmers and exclude indirect communication that involves other members of the community). The enumerator induces the participant to update her belief over _p_<sup>_L_</sup> by informing her that the research team would organize a meeting between the participant and that specific farmer after training had taken place, if the participant is the decision maker and their valuation for that specific farmer is selected by the choice lottery. I call this the “meeting treatment”.

Contagion externalities between two farmers depend on the physical distance between their plots, because the pest spreads by proximity. The enumerator induces the participant to update her belief over _p_<sup>_T_</sup> _ij_<sup>followingatwo-stepprocedure.Firsttheenumeratorasks</sup> the participant how long it would take to walk from her largest maize plot to the closest plot of a specific farmer _j_ , _d_<sup>_P_</sup> _ij_<sup>.Thentheenumeratorinformstheparticipant</sup><sup>_i_ofthe</sup> true as-the-crow-flies walking distance between those plots, _d_<sup>_T_</sup> _ij_<sup>.Icallthisthe“distance”</sup> treatment. Appendix G reports the exact wording used in the experiment. Figure A.4 shows the joint distribution of perceived distance _d_<sup>_P_</sup> _ij_<sup>andtruedistance</sup><sup>_dT_</sup> _ij_<sup>inwalking</sup> minutes. From the figure one can see that in most cases the true and perceived distances are within 20 walking minutes, and never above 100. This means that the plots considered lay within a distance of five miles or eight kilometers from each other. Figure A.5 shows the distribution of the difference between true and perceived distance in the control group, _d_<sup>_T_The median difference is close to zero (equal to -4), so farmers’ beliefs are roughly</sup> _ij_<sup>_−dP_</sup> _ij_<sup>.</sup> correct on average. In addition, the distribution is well-behaved. This is encouraging, as it suggests that farmers are familiar with plot distances. It also suggests that farmers are putting effort into answering plot distance questions that were not incentivized.

Both types of belief update interventions are a within-individual experiment; I generate random variation in contagion and knowledge externalities _within-participant_ at the _valuation level_ . For each farmer, I assign each of her five to eight valuations for others 13