_T_ takes the value 1 for each community j with a trained CF. Individual i, household h, and community characteristics are included in the vector _X_ to improve the precision of the estimated coecients. An indicator for the second follow-up survey, _νt_ , is also included to capture the eect of time-specic events on behavior.<sup>26</sup> We estimate all main regression models on the pooled sample, controlling for survey-year xed eects. We also use the Huber-White heteroskedasticity-robust estimator to calculate the standard errors when using the sample of CFs. For the other farmer regressions, we cluster the standard errors at the community level to allow for arbitrary correlation of treatment eects within the community.<sup>27,28</sup>

# 4 Results

## 4.1 CF Learning and Adoption

We rst examine the ITT estimates of a direct training on unweighted aggregate measures of CFs' knowledge and adoption (Panel A, Table 4). While control CFs adopted on average 3.74 techniques, we detect that CFs adopt on average a 0.73 additional technique in response to the training (statistically signicant at the 5 percent level). The associated eect size is large at 0.39 standard deviations in the control group, or a 19.6% increase relative to the mean in the control. Next, we run similar specications with the weighted versions of the outcome.<sup>29</sup> Control CFs adopt on average

> 26We include variables that reect CF (or other farmer) demographic characteristics: age, primary school completion, whether the individual is single (and a separate widow dummy for the other farmer sample), number of children, total landholdings, the number of rooms in the house, the number of hours worked by the CF at baseline, an indicator for a missing response for the baseline CF variable, district indicators, and indicators for treatment arms not analyzed in the present study. Our results are robust to specications that omit the demographic characteristics (Tables A.6 and A.7) or replace district with administrative post xed eects (Tables A.8 and A.9).

> 27Attrition rates at the household and CF level are not statistically dierent (Table A.10) nor correlated across treatment groups (Table A.11). Household attrition rates appear consistent with those of other studies in the same region (De Brauw, 2014). A probit regression reveals that the greater the percentage of household members away in 2012 and the incidence of being single produces a greater probability of the household moving out of the sample (Table A.11). Age, the number of children of the household head, and exposure to a precipitation shock reduce the probability of moving out of the sample.

> 28We perform two additional robustness checks to examine the sensitivity of our results to attrition (not reported). The rst diagnostic estimates (1) using the balanced panel. We show that the inclusion of individuals present in both rounds aects the precision of our point estimates rather than their magnitude and sign. The second check bounds the treatment eect for selective attrition using a method proposed by Lee (2009). This check conrms that selective attrition is unlikely to aect our conclusions.

> 29The linear model of maize revenue controls for adoption of each SLM technique, as well as household demographics, as in Table 4, and production inputs. The regression estimates are presented in Table A.12. The vector of weights

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