Resource information
Land area is a fundamental component of
agricultural statistics, and of analyses undertaken by
agricultural economists. While household surveys in
developing countries have traditionally relied on
farmers' own, potentially error-prone, land area
assessments, the availability of affordable and reliable
Global Positioning System (GPS) units has made GPS-based
area measurement a practical alternative. Nonetheless, in an
attempt to reduce costs, keep interview durations within
reasonable limits, and avoid the difficulty of asking
respondents to accompany interviewers to distant plots,
survey implementing agencies typically require interviewers
to record GPS-based area measurements only for plots within
a given radius of dwelling locations. It is, therefore,
common for as much as a third of the sample plots not to be
measured, and research has not shed light on the possible
selection bias in analyses relying on partial data due to
gaps in GPS-based area measures. This paper explores the
patterns of missingness in GPS-based plot areas, and
investigates their implications for land productivity
estimates and the inverse scale-land productivity
relationship. Using Multiple Imputation (MI) to predict
missing GPS-based plot areas in nationally-representative
survey data from Uganda and Tanzania, the paper highlights
the potential of MI in reliably simulating the missing data,
and confirms the existence of an inverse scale-land
productivity relationship, which is strengthened by using
the complete, multiply-imputed dataset. The study
demonstrates the usefulness of judiciously reconstructed
GPS-based areas in alleviating concerns over potential
measurement error in farmer-reported areas, and with regards
to systematic bias in plot selection for GPS-based area measurement.
