A key issue on which little analysis has been conducted is the potential selection bias associated with GPS-based plot area estimates. For a variety of reasons connected to (i) reducing survey costs, (ii) keeping household interview durations within reasonable limits, and (iii) the difficulty of asking respondents to accompany enumerators to agricultural plots that are situated far from dwelling locations, survey fieldwork protocols often require enumerators to record GPS-based area measurements only for plots that are within a given radius of the dwelling location. Plots that are located within an acceptable distance from the dwelling may still not be measured due to respondent refusal or lack of physical access at the time of the interview. Consequently, GPS-based plot area measurements, which are still recorded alongside farmers’ own assessments in agricultural and household surveys, present large gaps and a score of missing land area values that can be catalogued as “item non-response.” It is not uncommon to have missing GPS-based area values for as much as a third of the plots owned and/or cultivated by sample households, thus limiting the analytical value of the GPS-based data collection effort, to the point that one could even question whether it is worth collecting such data. The potential selection bias in GPS-based plot area estimates is most recently recognized but not addressed by Carletto et al. (2013), who explore the empirical implications associated with the choice of farm size measures based on farmer-reported vs. GPS-based plot areas, and document a stronger inverse farm size-land productivity relationship when using the GPS-based farm size measure.

The gaps in GPS-based plot area data are reminiscent of similar patterns of item nonresponse in other statistical series, such as income data in OECD countries. In the US, for instance, the problem is common in some of the Census Bureau series (Scheuren, 2005), in the National Interview Health Survey (NHIS; Schenker et al., 2006), but also in agricultural and forestry surveys such as the Agricultural Resource Management Survey (ARMS, Ahearn et al., 2011) and the National Survey on Recreation and the Environment (Zarnoch et al., 2010). Other examples include the Labor Force Survey of the Municipality of Florence in Italy (Giusti and Little, 2011), and the Labor Force Survey in South Africa (Vermaak, 2012). What several of these experiences have in common is the systematic approach to dealing with missing data as datasets are released in the public domain and analyzed for research and policy purposes. Increasingly, the technique of choice for dealing with missing data in public use datasets is “multiple imputation” (Rubin, 1987; 1996), which, provided that the assumptions regarding the missing data mechanism hold, allows for valid inference on a restored “complete” dataset, while taking into account the uncertainty associated with the imputation process itself.

As the drive towards open data advances in the developing world, methodical approaches to reliably address structural gaps in critical data, such as GPS-based land areas, are likely to be of increasing interest to national statistical agencies, their international partners, and 3