enumerators to all the plots. Even if they were, the operational costs and travel time required to record GPS measures for all plots are likely to be prohibitive for most survey operations. This raises some analytical concerns, in the form of biased estimates, if the plots that are not measured are systematically different from the ones that are measured. We do find evidence of such bias in our data, and in the next section we discuss how we dealt with the issue.

# **3. Data source and descriptive statistics**

The data for this paper come from the Uganda National Household Survey (UNHS) round implemented by the Uganda Bureau of Statistics (UBOS) in 2005-2006. The UNHS is a multi-purpose household survey with a sample of 7,500 households, selected following a stratified sample design that identified 753 enumeration areas (UBOS, 2009). One special feature of this survey that makes our analysis possible is that it contains plot level information on agricultural land area measured through both GPS and farmers‟ own estimates.

Some 5, 714 of the survey households are located in rural areas and cultivate land. The total number of plots with self-estimated measure is 13,959 and about 65 percent of these (9,173 plots) have a corresponding GPS measure<sup>3</sup> . The agricultural production section, however, only contains information gathered at the household/farm level. Thus, the analysis in this paper is carried out partly at the plot level, and partly at the household/farm level. We use the former to analyze the difference between land area measurement using farmers‟ estimate and GPS, since it is at the plot level that the two measures are collected. The household/farm level analysis is more suitable to investigate the consequences of the bias in land measurement on the IR, since key farm input and output data are only available in the survey at this level, not allowing the calculation of plot-specific productivity measures.

To be able to run the analysis maintaining a consistent sample throughout, we therefore restricted our sample further to 5,767 plots for which we have complete information on both type of measures for all the plots cultivated by the household. In the farm level analysis, for which we also drop 42 households with negative farm profits (equivalent to 1.5 percent of the sample), we end up with a sample of 2,860 rural households for which we have both non-zero land area measures for the entire households‟ landholdings.

Table 1 reports key summary statistics for the plots for which we have information on both types of area measurements, along with the mean and standard deviation (in acres) of plot size measured through GPS, self-reporting, and the absolute and relative difference between the two measures. For simplicity of presentation, we will take the GPS measure as the benchmark and talk of farmers over- (or under-) reporting plot size whenever the area self-reported is larger (smaller) than the area measured by GPS. We do acknowledge however that the GPS measure may also be subject to a certain degree of inaccuracy (Keita and Carfagna, 2009).

> 3 A Garmin 12XL GPS device was used by the survey teams to collect plot area data.

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