of techniques prior to the intervention.<sup>19</sup> In spite of these dierences, (recalled) pre-intervention adoption rates among CFs in control and treated communities were similar, as were other farmers' (recalled) baseline SLM learning and adoption rates (Table 2).<sup>20</sup>

# 3.7 Measuring Information Diusion and Behavioral Change

Central to identifying variations in information diusion is measuring changes in learning and agricultural practices. Our study rests on the reliability of our markers of individual SLM knowledge and adoption. We focus on three outcomes: a knowledge score, the number of techniques the respondent identied by name, and the number of techniques the respondent reported having adopted on any plot.<sup>21</sup> The knowledge score is a continuous measure based on the number of correct responses provided in the 23-question exam, covering all SLM techniques. For CFs, the majority of the analysis rests on their self-reported adoption of techniques on any plot (demonstration or not).<sup>22</sup> Since the CFs were encouraged to choose the techniques most relevant to their local conditions, our main results focus on unweighted aggregate measures of knowledge and adoption. However, we create a second set of weighted knowledge and adoption outcomes as a robustness check. Prior to aggregation, we multiply the technique by a weight based on its relative importance to maize revenue. This is done as follows. First, we compute a vector of weights, based on a regression of maize revenue on adoption of the seven individual practices. Second, we compute adoption and knowledge

> 19Given that CFs in treatment villages spend more hours a week working as a CF at baseline, we will include the variable as a control in the regression analysis.

> 20Balance tests for the CFs' and other farmers' knowledge and adoption of individual SLM techniques at baseline are reported in Tables A.1 and A.5. Because these values are based on recalled data, the tests should be interpreted with caution. Even though mean comparisons indicate there are no statistically signicant dierences, recall bias may be present. We therefore do not exploit the recalled information beyond balance checks.

21Our decision to focus on the knowledge score and self-reported adoption outcomes is motivated by the conclusions in Kondylis et al. (2015). Using the midline survey data, they nd learning outcomes based on knowledge exams provide more precision than know-by-name questions, inasmuch as they reveal the true knowledge of those individuals less familiar with the name of the technique yet more familiar with its purpose and usage. Objective adoption measures were also collected for two plots per household and largely corroborate the self-reported outcomes. In our triangulation of the self-reported versus observed adoption, we nd that false reporting is negligible. Since objective measures of adoption are collected for only a subset of plots (one per respondent) at midline and a subset of the sample at endline, we instead focus on a more inclusive measure of adoption provided by self-reports of interviewed men and women.

> 22There are slight dierences between adoption measures which include and exclude the demonstration plot. This is due to the fact that some CFs demonstrate on communal land (29%). We verify the results are not driven by communal propriety of the demonstration plot (not reported). Since 71% of demonstration activities are carried out on CFs' own plots, we choose to use pooled adoption on and o demonstration plots as our main marker of adoption. This improves our precision but does not aect our conclusions.

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