Table 3 presents results from estimating specification 13. Estimates are at the dyad level, and a dyad is composed by a farmer _i_ stating her valuation and a farmer _j_ for which the valuation is made. Out of 3,566 dyads for which the distance variables are not missing, the last line of column (1) shows that in 939 dyads farmer _i_ would potentially receive good news and in 2,435 dyads she would receive bad news (in 192 dyads she would receive no news because she has correct beliefs). The coefficients of interest are _Good News_ and _Bad News_ , which measure the change in willingness-to-pay of _i_ for _j_ for a 1-minute increase in absolute net distance, or _|d_<sup>_T_</sup> _ij_<sup>_−dP_</sup> _ij_<sup>_|_.Thecoefficienton</sup><sup>_GoodNews_incolumn</sup> (1) is negative as expected. It should be interpreted as the decrease in _i_ ’s willingness-topay for _j_ ’s information for every extra minute that _j_ ’s plot is surprisingly further away than _i_ believed. The coefficient size is 68 UGX and at the mean of _Potential Good News_ (equal to 8) it implies a decrease of 544 UGX, $0.14 or about 11% of the daily wage of a local agricultural laborer. The coefficient on _Bad News_ is surprisingly negative but small and insignificant at conventional levels. Overall, results in column (1) show that farmers reduce their willingness-to-pay for farmers that generate lower contagion externalities.

Theory does not predict the difference between the coefficient size of _Bad News_ and _Good News_ that column (1) in Table 3 displays. An experimental feature that would explain this difference is that a _Good News_ treatment is _actually_ good news, while a _Bad News_ treatment is not necessarily bad news, for the following reason. Farmers are asked about the walking distance between plots, so their answer may take into account rural paths, obstacles or differences in altitude. The distance the enumerator treats them with, _d_<sup>_T_</sup> _ij_<sup>,istheas-the-crow-flieswalkingdistance(betweenplotcentroids)assuminga</sup> farmer walks at three miles per hour (20 minutes per mile). As-the-crow-flies is the relevant distance metric for pest spread because the pest spreads by flying to other plots during the moth stage. Hence, the walking distance stated by farmers _d_<sup>_P_</sup> _ij_<sup>canonlybe</sup> larger than the true walking distance between plots if the farmer has perfect information and walks at a speed of three miles/hour. For this reason, when the true distance is larger than the perceived distance, _d_<sup>_T_</sup> _ij_<sup>_>dP_</sup> _ij_<sup>,thefarmerisundoubtedlyreceivinga</sup> _Good News_ type of shock. When instead _d_<sup>_T_</sup> _ij_<sup>_<dP_</sup> _ij_<sup>,itmaybethatthefarmerdoes</sup> not actually receive bad news because she knows that the walking path is actually much longer than the as-the-crow-flies path. I address this issue by making four different assumptions about the relationship between true and perceived distance in the data, and re-estimating specification 13. In columns (2)-(5) I replace the perceived distance _d_<sup>_P_</sup> _ij_<sup>, used</sup> to calculate _Bad News_ , _Good News_ , _Potential Bad News_ and _Potential Good News_ , with _λd_<sup>_P_where0</sup><sup>_≤λ ≤_1isascalingfactor.Incolumn(2)Iset</sup><sup>_λ_suchthatthemeans</sup> _ij_<sup>,</sup> of the distance variables equalize, _λ_ =<sup>_E_(</sup><sup>_dT_)</sup> _/E_ ( _d_<sup>_P_</sup> ), while in column (3) I set _λ_ so that medians equalize, _λ_ =<sup>_Med_(</sup><sup>_dT_)</sup> _/Med_ ( _d_<sup>_P_</sup> ). In column (4) _λ_ is set such that true distance is the hypothenuse of an isosceles right triangle and the perceived distance _d_<sup>_P_</sup> _ij_<sup>istwiceeach</sup> 26