HFPSs have been conducted by the World Bank, in collaboration with national statistical offices, in over 80 countries around the world and cover over 100 harmonized indicators. As such, they are designed to be nationally representative by using various weighting measures. Relative to the traditional in-personbased surveys, however, phone surveys (or other remote surveys such as internet surveys) have several limitations. For instance, the phone survey sample is mostly representative of the phone-owning population, which could mean that the surveys potentially underrepresent the poorest populations within a country. In addition, these surveys have been found to collect more information from respondents who are more likely to be heads of households, overstating such things as employment rates (Kugler et al. 2021). While the phone surveys are not as comprehensive as the traditional household surveys, they remain important modules for data collection when traditional surveys cannot be conducted – as was mostly the case for 2020.

For poverty calculations, the phone surveys report whether (or not) a household has lost income or consumption because of the pandemic. However, the phone surveys by themselves cannot be used to quantify the welfare changes of households for at least two reasons: (i) no baseline household welfare measure is available in the phone surveys, and (ii) we do not know the size of the welfare changes in the phone surveys, only which households lost or did not lose income or consumption.<sup>8</sup> To quantify the impact of COVID-19 on poverty, we need to map the income or consumption changes from the phone surveys to a traditional household survey for that country.

Mahler et al. (2022) propose a methodology that can be used to map the income or consumption losses in phone surveys to an underlying welfare distribution (see also Narayan et al., 2022). First, they calculate the probabilities for each type of household defined by the household size, education and age of household head, and area of residence from phone surveys to estimate the income loss probabilities for a particular type of household. These probabilities for each type of household from the phone surveys are then mapped to the 2019 household survey available for each country. Second, whereas one can estimate the probabilities of income losses for each type of household, the size of these losses for each household is still not clear. The sizes of these losses are important for understanding the changes in household income in 2020, and thus, understanding the impact of the pandemic on poverty. To get a sense of the size of the losses, Mahler et al. (2022) rely on sectoral – agriculture, industry, and service – growth rates in national accounts. The underlying assumption in their formulation is that the sum of all household losses (calculated using the first step above) are equivalent to the total loss in national accounts. Using this assumption allows

> 8 Countries reporting change in household income report either if they lost, gained, or had no change in income since the start of the pandemic. Countries reporting change in household consumption report if households lost or did not lose consumption since the start of the pandemic. The income aggregate is preferred where available. Table A1 lists countries with the respective welfare measure used.

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