Research has provided robust evidence for the use of GPS
technology to be the scalable gold standard in land area
measurement in household surveys. Nonetheless, facing
budget constraints, survey agencies often seek to measure
with GPS only plots within a given radius of dwelling locations.
Subsequently, it is common for significant shares of
plots not to be measured, and research has highlighted the
selection biases resulting from using incomplete data. This
study relies on nationally-representative, multi-topic household
survey data from Malawi and Ethiopia that exhibit
near-negligible missingness in GPS-based plot areas, and
validates the accuracy of a multiple imputation model for
predicting missing GPS-based plot areas in household surveys.
The analysis (i) randomly creates missingness among
plots beyond two operationally relevant distance measures
from the dwelling locations; (ii) conducts multiple imputation
under each distance scenario for each artificially created
data set; and (iii) compares the distributions of the imputed
plot-level outcomes, namely, area and agricultural productivity,
with the known distributions. In Malawi, multiple
imputation can produce imputed yields that are statistically
undistinguishable from the true distributions with up to 82
percent missingness in plot areas that are further than 1 kilometer
from the dwelling location. The comparable figure
in Ethiopia is 56 percent. These rates correspond to overall
rates of missingness of 23 percent in Malawi and 13 percent
in Ethiopia. The study highlights the promise of multiple
imputation for reliably predicting missing GPS-based
plot areas, and provides recommendations for optimizing
fieldwork activities to capture the minimum required data.