On average the two methods produce strikingly similar estimates of land area. The average size of plots using GPS is 2.24 acres, a mere 0.11 acres (equivalent to 4.9%) larger than the area reported by farmers. The sample level means however mask pervasive differences in measurement that emerge at closer scrutiny. In the overall sample, farmers overestimate plot size in 54.12 percent of the cases (or 3,121 plots), and underestimate it in 44.13 percent of plots (equivalent to 2,545 plots). For the remaining 101 plots (1.75 percent) the survey reports identical measures with either method. For the plots where a „positive discrepancy‟ is observed, namely a GPS measure larger than self-reporting, the amount of area overestimated is larger in absolute value (1.07 acres on average, or 35.1 percent) than for the plots for which the bias is negative (-0.67 acres or 47.2 percent).

The fact that we dropped a substantial number of plots from our analysis because of gaps in the GPS measurement raises some concerns regarding whether this may introduce a source of bias in our analysis. This intuition is partially confirmed by an inspection of the summary statistics for the two sets of plots. Annex Table 1 reports the means and t-tests for the difference in means for some key plot and household characteristics. The plots that we had to exclude from the analysis because of gaps in the GPS measure are on average smaller than those we did include, they are more likely to be on a steep slope, and less likely to be protected by a fence. The heads of households to which these plots belong are somewhat older and less educated, and more likely to be female. Also, these plots belong to households that are on average smaller. These plots are therefore not randomly selected, which is what one would expect, since taking the actual GPS measurement of a plot requires a household member and enumerator to move for the site of the main interview to the location of the plot to take the GPS measurement. More distant, less accessible plots are therefore more likely to be excluded. These statistically significant differences in plot characteristics between the part of the sample we use and the part we had to discard suggest the presence of possible sample selection issues arising from these restrictions and impacting on the analysis of farm productivity. We will return to this point in the result section, to show how these issues to do not in fact appear to be affecting our results. The nonrandom selection of GPS-measured plots, however, does represent a challenge that future surveys employing these methodologies will have to find ways to confront.

Table 2 summarizes land measurement statistics at the household level. We report statistics for deciles of landholding to investigate how measurement bias plays out at different points of the distribution. This information will be important later, when we analyze the impact of land measurement bias on the IR. The last column of table 2 reports the discrepancy in percentage terms. According to this measure, the magnitude of the discrepancy appears to increase monotonically as one moves from the bottom (smallest landholders, -97 percent) to the top deciles (largest landholders, 19 percent). Up to 4.57 acres of farm size (the seventh decile), the bias is on average negative, whereas it becomes positive in the top three deciles. There is therefore a pattern in the direction of the bias, with 6