whether the remote sensing data is rainfall or temperature. Figure 9 presents the share of significant coefficient estimates for three standard _p_ -values: for _p >_ 0 _._ 90 _, p >_ 0 _._ 95 or _p >_ 0 _._ 99. To these bars we add the 95% confidence interval on the mean number of significant coefficients. The north panel presents results from precipitation products while the south panel presents results from temperature products. Each bar and confidence interval in the rainfall panel is based on 6,048 regressions while each bar and confidence interval in the temperature panel is based on 1,728 regressions. To facilitate comparison, we draw red lines to designate the top and bottom of the confidence interval on the mean for Household bilinear, the preferred method.

A quick visual inspection of the results in the top panel of Figure 9 does not reveal many, if any, differences across extraction method. Comparing numerical values for the share of significant coefficients from one extraction to the 95% confidence interval on the mean of any other extraction does reveal some small differences. The share of significant coefficients produced by regressions using data from modified EA simple and Administrative area bilinear are weakly different from EA bilinear. The only other pairwise comparison that is weakly difference is between Household bilinear and Household simple. There are no comparisons that are strongly different from each other. The results in the lower panel on temperature look similar to those with rainfall. No obvious differences exist, though a numerical comparison reveals that Household bilinear is weakly different from Administrative area bilinear. No other pairwise comparisons are weakly different and no comparisons are strongly different. As with our examination of adjusted R<sup>2</sup> values, the preponderance of evidence here implies that different extraction methods do not introduce substantial mismeasurement into the analysis. We fail to reject Hypothesis 1. However, there is the possibility of heterogeneity across or within countries. As such, we next consider this same metric, disaggregated by country. Figures 10 and 11 present different extraction methods across all rainfall and temperature metrics, for each of the six countries. Now that we have divided the results by extraction method, rainfall/temperature, and country, each bar represents the share of significant coefficients from 1,008 regressions for rainfall and 288 for temperature. We simplify the graph by only presenting the share of coefficients with _p >_ 0 _._ 95.

We see more variation within a country based on extraction method than in our previous graphs. In Ethiopia, the two extraction methods based on Administrative area centerpoints are strongly different from Household bilinear. While there are no other pairwise comparisons in Ethiopia, or any of the other countries, that are strongly different, a number of pairwise comparisons are weakly different. However, there seems to be no pattern to which extraction methods are different.<sup>13</sup> The evidence for differences in extraction method using temperature data is equally noisy and without any apparent pattern of one extraction method over/under performing the others.

> 13In Malawi, Household simple and the two zonal means are weakly different from EA bilinear and Administrative area bilinear. In Niger, Administrative area simple is weakly different from modified EA bilinear. In Nigeria, Administrative zonal mean is weakly different from bilinear Household and modified EA. In Tanzania, EA simple and EA zonal mean are weakly different from bilinear Household and EA, as well as Administrative simple and zonal mean. In Uganda, no pairwise comparison is weakly different.

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