applied to longitudinal data, where variables from different survey rounds can be strong predictors of missing observations, and it is thought to be at “its weakest in cross-section surveys. In cross-section surveys, seldom are there strong predictors present” (Scheuren, 2005: 318). We posit that the presence of the self-reported land area makes our dataset an exception to this rule.<sup>10</sup> Other independent variables in the imputation model include the distance of the plot from the household in minutes (Uganda) or kilometers (Tanzania). As noted earlier, these covariates are major predictors of missingness in accordance with the survey protocols. The variables on plot tenure status and farmer-assessed plot slope and soil quality are incorporated since, besides being control variables in the analytical model, they are possible predictors of missingness as well. The imputation model also includes variables on household characteristics related to (i) demographics (household size, dependency ratio, and the age, sex and education of the head of household), and (ii) whether the household is a (intact) mover original or a split-off household. The inclusion of the latter set of variables attempts to accommodate the possibility that the additional workload associated with tracking shifted households and individuals in their new locations may increase the likelihood of missing data. The demographics are deemed to influence the accuracy of the self-reporting, as well as being likely control variables for the analysis. Furthermore, the inclusion of enumerator fixed effects in the imputation model accounts for unobserved heterogeneity across enumerators, such as differences in effort, which might be correlated with the missingness in GPS-based plot area measures. Lastly, we use information from earlier survey rounds on household wealth to accommodate the possibility of higher likelihood of missing plot area measurements among richer households with higher opportunity costs of time.

The first step in our multiple imputation effort is to fit a plot-level Ordinary Least Squares (OLS) regression model with the GPS-based plot area as the dependent variable, to then obtain linear predictions for all plots in the dataset. Under the partially parametric method of predictive mean matching (PMM) (Rubin, 1987; Little, 1988; Schenker and Taylor, 1996), we use the linear prediction as a distance measure to form a set of 5 nearest neighbors chosen from the plot sample with GPS-based area measures, and randomly pick one of the neighbors whose observed GPS-based plot area value replaces the missing value for the incomplete case at hand.<sup>11</sup> The imputation is carried out 50 times to reduce the

> 10 We cannot apply a panel data model because of issues with the survey design that do not allow matching plots across survey years. It is likely that future surveys in the LSMS-ISA program will allow us to overcome this limitation. Another aforementioned limitation in the case of the TZNPS is that only 25% of the plots were measured in the most recent survey round prior to the 2010/11 wave that informs our analysis.

> 11 The results are robust to using linear regression, as opposed to PMM. The number of nearest neighbors in the PMM framework is inversely related to the correlation among imputations. While high correlation may increase the variability in MI point estimates, low correlation may increase the bias in MI point estimates. The

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