where _i_ represents household; _α_ and _ɛ_ represent constant and error terms, respectively; and D1, D2, R1 and R2 are identifiers representing a household’s assignment to diary-visit, diary-phone, 6-month recall, and 12-month recall, respectively. D1 is the comparison category in Equations 1 and 2, and crop cutting is the comparison category in Equation 3. _C_ is a vector of household attributes that is included with the intention of capturing any remaining unobserved heterogeneity that may be correlated with these controls and that may jointly determine both the dependent variables and household survey treatment assignment.P29F<sup>30,</sup> 30F<sup>31</sup> As described in Section 3, annual cassava production (kilograms) and cassava yield (kilograms/hectare) are the dependent variables of interest. To limit the influence of potential outliers that were not deemed agronomically plausible upon extensive review of the data, both production and yield estimates were winsorized at the 95<sup>th</sup> percentile. The sample includes 1,218 households for Equations 1 and 2. To estimate Equation 3, we append the household-level crop cutting-based yield estimates available for 1,123 households onto the data set used for the estimation of Equations 1 and 2. As such, the standard errors are clustered at the household-level for Equation 3, and at the EA-level for Equations 1 and 2. The results from each regression are coupled with the full spectrum of tests of equality of coefficients for complete inter-arm comparisons.

# **5. Results**

Panels A, B, and C of Table 6 report the results from the estimations of Equations 1, 2 and 3, respectively.P31F<sup>32</sup> If true cassava production is assumed to be underestimated even by diary-keeping due to imperfect compliance, we find that compared to D1, the annual household cassava production was 295 kilograms higher, on average, under D2, corresponding to 28 percent of the 30 The control variables include: the binary variable identifying whether the household head is female; the household head age in years; the highest grade in school in the household; the binary variable identifying any household nonfarm employment in the last 12 months; the binary variable identifying any household paid employment in the last 12 months; household size; household dependency ratio; a principal components analysis based wealth index of dwelling attributes and ownership of consumer durables; the binary variables identifying district residence, with Nkhatabay being the comparison category. The plot-level variables were aggregated at the household-level and are represented as follows: total land area cultivated with cassava in acres; the binary variable identifying whether the household has sold any cassava harvested in the last 12 months. The findings are robust to excluding the control variables from the regressions - another indication that the randomization of households across treatment arms worked according to plan. 31 Heterogeneity of impact across different treatment arms was explored through the expansion of equations 1-3 to include interaction terms between survey treatment identifiers and selected control variables, including the length of the harvest period, the involvement in cassava sales, the land area under cassava cultivation, and the highest education level in the household. None of the interaction terms was associated with coefficients that were statistically significant at least at the 10 percent level.

> 32 Figure 3 provides a complementary, distributional comparison of annual household cassava production by survey treatment.

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