the dependent variable, with the relationship with plot size being again best characterized as L-shaped. Nigeria is an exception in that no statistically significant relationship with plot size is revealed by these two specifications.

These results are in line with the earlier descriptive analysis in that the overall distribution of the bias does not seem to bear much relationship with plot size, as they are largely equally distributed on the positive and negative side. The absolute magnitude of the error does, however, increase somewhat with plot size but less than proportionally. For that reason, in percentage terms the bias actually declines fairly rapidly as plot size increase, stabilizing as plot size reaches the 1-2 acres range.

Of the covariates reflecting physical characteristics that are expected to affect the quality of GPS measures (cloud and canopy cover, plot slope) hardly any are consistently significant across country. What appears to matter most are closing error and the perimeter/area ratio. The former reflects inaccuracy in the CR measure, while the latter is a proxy for the complexity of the plot shape which is likely to affect the accuracy of GPS measures, but can in principle also be capturing noise in the CR measure besides what is captured by the closing error. In Ethiopia and Tanzania (as well as in the fourth specification of the pooled sample regression), there is evidence that heavy canopy cover does increase relative bias. An unsystematic comparison of plot outlines computed from the CR method and collected in the GPS also suggests that it may often be the case that enumerators may tend to simplify the shape of the plot more when collecting CR than GPS data (Figure 6, see Carletto et al. 2016, for additional evidence).

Although the difference between the two objective measurements is relatively small on average, it is worth digging into the problem cases in which the deviation is much larger. Table 9 reports results of a probit model (see equation 2) estimating the probability of a plot being a “problem plot” – defined here as having a relative bias greater than 10% (in absolute value). In all countries we find evidence of a cubic relationship between the probability of GPS overestimating area by more than 10 percent and plot size. That translates into the probability being highest for very small plots, and decreasing fast as plot size increases, before flattening fairly quickly (and eventually tilting up somewhat) for larger plot sizes. The same relationship is found for plots under-estimated by GPS in Ethiopia, but not in the other two experiments. As in the OLS regression, in the probit model the other covariates that appear to be playing a role are closing error and perimeter/area ratio. Tree canopy cover appears to play more of a role in these regressions, implying that the effects of canopy cover are not felt equally throughout the distribution of the bias variable, but that they kick-in in particular regions of the distribution. Weather at the time of GPS measurement has a more limited effect. In Tanzania, plots that are measured during “mostly cloudy”, “all cloudy”, or “rainy” weather are slightly more likely to be over-stated by the GPS by 10% or more 24