<mark>surveys means in most instances across countries.</mark><sup>23</sup> <mark>Appendix tables A.4.7 to A.4.12 present the weighted averages along with confidence intervals for non-model variables. Notably, we reject the null hypothesis that the GMD means are same as the online survey means in more instances for the non-model variables compared to the model-variables. These details are summarized in table A.4.13.</mark> <mark>Compiling data from tables A.4.1 to A.4.12, tables A.4.14 and A.4.15 present the average of the relative deviations of online survey means with GMD means for model-variables and non-model variables, respectively. In both these tables, column (1) specifies the online survey waves that were considered in obtaining the deviation. For example, the variable age is available in all waves, and hence age was dropped to estimate models between wave-1 with GMD, wave-2 with GMD, and wave-3 with GMD. The average presented for age is the relative mean from all the three waves, and this is represented as ‘1-2-3’ in column 1. Since asset variables were only available in wave-3, the models that drop the asset variables were only estimated between wave3 and GMD. This is represented as ‘3’ in column 1 for all asset variables. Columns (3)-(8) present the deviation for each country.</mark> <mark>Results indicate that the average relative deviations for model variables between the Internet surveys and GMD range from 0 precent for cell phone usage in Egypt, to 87.1% percent for the “no education” category in Egypt. For non-model variables, the average varies widely, ranging from as low as 0.4 percent for computer usage in Brazil, to as high as 10,722.2 percent for working in “other sector” in Indonesia. Figure 5.1 presents a boxplot of the average deviations across all countries for model variables (panel A) and non-model variables (panel B). The spread of the deviation and the mean values of deviation are both lower for model variables compared to non-model variables. The mean values of these deviations for each country are reported in table 5.1.</mark> <mark>It is useful to check if the models perform better in countries that have higher Internet coverage data. For this, we present data on Internet coverage across the countries from various official sources (red dot markers in the figure) in the second y-axis and arrange the countries in the x- axis in ascending order of internet coverage. Internet coverage in 2019 is highest in Türkiye and Brazil, followed by Egypt, Indonesia, Sri Lanka, and Kenya in that order. For model variables, countries with higher Internet coverage seem to have lower median values of deviation on average. Türkiye, which has the lowest mean deviation for the model variables (as per table 5.1), has the highest Internet coverage. For non-model variables, evidently, there is no clear indication that the best performing models are in countries where Internet coverage is the highest. The median deviation for Kenya, the country that has the least Internet coverage, seems close to the median deviation for Türkiye, the country that has the highest Internet coverage. Collectively, these results indicate that even after reweighting using inverse probability weights, it is challenging to match the means of non-model variables of the online surveys with nationally representative surveys.</mark>

> 23 <mark>Online survey means statistically significantly different from GMD are indicated by yellow shaded cells in tables A.4.1 to A.4.6.</mark>

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