normally distributed. Overall, from a technical standpoint, GPS-measured areas would be expected to create land data with classical measurement error.

While, technically speaking, GPS area measurement error should be random, the use of the technology in practice has produced different results. For reasons left unexplained by device manuals and technological articles, handheld GPS devices often slightly under report the true area on average with empirical evidence suggesting that the GPS-based area measurement appears to be less precise to a non-trivial degree for plots smaller than 0.5 hectares or 1.24 acres (Schoning, Apuuli and Muwanga-Zake, 2005; Keita and Carfagna, 2009). However, in field experiments using various GPS devices, Hejmanowska, et al (2005) found that Garmin units did not bias estimates either downward or upward in their sample of plots from 0.369 -3.92 hectares although other units did show bias in one direction or the other. The smaller area as measured by GPS could be explained if the GPS unit, and the enumerator using the device, cuts corners, thus shaving off the precise corner point of the plot if there is one, an effect that could be exacerbated by the speed with which the plot is navigated. Analysis by Bogaert, Delince and Kay (2005) confirms the hypothesis that operator speed has an impact on GPS measurement accuracy while adding that the optimal speed is not fixed, but rather varies with plot size. Finally, training of enumerators in the use of the device and the manner in which results are recorded could influence final measurement.

Even with these issues, GPS is considered to be a more accurate method of area measurement than self-reporting. Although the issues with GPS measurement error should not be overlooked and are discussed again in the conclusion of the article, here GPS measurement is considered closer to the true land area and we refer to deviations from the GPS area as self-reported measurement error.

# _LSMS-ISA data_

The LSMS-ISA data sets create an opportunity for analyzing the different methods of land data collection since the studies employ GPS measurement while continuing to collect self-reported land areas. Data are currently available for four LSMS-ISA countries: Malawi (2010/11), Uganda (2009/10), Tanzania (2010/11) and Niger (2011). For agricultural households in each of the countries, the plot manager or head of household is first asked to estimate the plot areas and then the plots are measured using GPS, so as not to influence the farmer’s reported figure.

Depending on the survey, farmers may be required to report in a single unit (as in Uganda, Tanzania and Niger) or they may have the choice to report in a number of units (as in Malawi). In either scenario, the estimate is subject to previously noted errors. GPS devices allow for measurement in a variety of units, but are typically recorded in acres in LSMS-ISA projects and rounded to the nearest hundredth.

The exclusion of certain plots from GPS measurement was mandated by the survey manual for the sake of time and budget. In Malawi, for example, enumerators were instructed to measure all plots within two hours of walking from the household. In Tanzania the guidelines were to measure all plots within one hour (via any mode of transportation) and Uganda required all plots within the enumeration area to be measured (Government of Malawi, NSO, 2010; World Bank Group, n.d.; UBOS, 2009). Table 1 provides basic information on the surveys considered in this study and how land data were collected in each case. The number and fraction of total plots or parcels that have both GPS and self-reported areas varies by country. In Niger and Uganda, the survey captured 47% and 58% of plots on GPS while Tanzania and Malawi have more complete data reaching 78% and 96%, respectively. The variation is related both to the rules of data collection and the concentration 5