<mark>individual’s inclination to take up the survey, as illustrated in section 3.2 (also observed in Dever (2011), and Valliant and Dever (2011)). Specifically, those who take up the Internet surveys are more likely to be younger individuals, more educated individuals, and males. Additionally, economic variables are important because higher income often correlates with whether an individual has the luxury of time to participate in the survey. Due to the unavailability of some economic variables in specific survey waves, models between GMD and wave-2 data do not include the asset variables, and models between GMD and wave-1 data do not include variables on assets and the pre-COVID sector of work. Moreover, in the education variable, the “no education” category was not collected in wave 1, and hence we use fewer categories on education in wave-1 regressions.</mark> <mark>Asset data in the online surveys varies across countries. In Brazil, the online survey collected information about the availability of land phones, cell phones, computer, and piped water. In Egypt, the online survey collected data on the availability of land phone, computer, and piped water. In Indonesia, the asset variables include the availability of land phone, computer, car, air conditioner, and fridge. In Türkiye, the data on assets include the availability of computer, car, air conditioner, cable connection, and stove. In Kenya, the data on assets include the availability of computer, piped water, modernized floor, and modernized wall. In Sri Lanka, the assets include toilet and piped water.</mark> <mark>In all regressions, standard errors were clustered at the region level, except in Türkiye where the regional indicators were not available.</mark><sup>22</sup> <mark>From the model in equation (1), we obtained the predicted probability of participation in the online survey (</mark> _<mark>p</mark>_ <mark>) and calculate weights for the online survey observations as</mark> _<mark>(1-p)/p</mark>_ <mark>. The GMD is weighted by its own survey weights. Theoretically, the weighted means of variables used in the model should match between the online surveys and the GMD (Li et al, 2018).</mark> <mark>We also test whether non-model variables match across the datasets. For this, we re-estimate the models after dropping the original model variables one by one. Using these restricted models, we predict the probability of participating in the online surveys (p), as before. Applying the weights as before, we compare the means of the dropped variable, and calculate the relative mean deviation of the Internet survey mean (for each wave) from the GMD mean.</mark>

# **<mark>5. Results</mark>**

# **<mark>5.1. Results from Reweighting</mark>**

<mark>This section presents the weighted averages of model variables based on propensity score weights obtained from the logit models in each country. Appendix tables A.4.1 to A.4.6 present the weighted averages from GMD (column 1), online survey wave-1 (column 2), online survey wave-2 (column 3), and online survey wave-3 (column 4), along with the confidence intervals. Results indicate that we do not reject the null that the GMD means are the same as the online</mark>

> 22 Clustering usually provides more conservative standard error estimates compared to robust standard errors. Still, we face the problem of the small number of clusters biasing our standard error estimates downwards because the number of regions in each country is low and this could lead to over-rejection of the null hypothesis (Cameron and Miller 2015). The ideal way to proceed is to make corrections by estimating the Wild cluster bootstrap- _t_ statistics which would provide more conservative standard errors. This downward bias will however not hurt us because, ideally, we are hoping that the null hypothesis of GMD and online survey mean equivalence is not rejected. But if it is not rejected with clustered standard errors, it will not be rejected with downward adjustments using the Wild method either.

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