For 2020, the pandemic-induced shutdowns implied that traditional household surveys were largely absent and, where available, were switched to phone-based surveys.<sup>5</sup> At the same time, average growth in national accounts may be a poor proxy of growth in household income or consumption during 2020. For example, the wide-ranging public spending policies may not be fully reflected in the national accounts growth rates or the lockdowns had a very heterogeneous impact across sectors. Therefore, for 2020, we rely on the following sources of data, ranked by order of preference.

First, we use the household survey micro data when available in PIP, which applies to only 20 countries. Second, for 8 countries, we rely on tabulated income statistics that report average income for various quantiles of the income distribution available from national statistical offices (NSOs). Third, in 37 countries, we use the High-Frequency Phone Surveys (HFPSs) that the World Bank has collected in collaboration with NSOs.<sup>6</sup> These surveys are conducted over the phone and are hence less comprehensive than traditional household surveys. In many countries, phone surveys are some of the only national surveys available from 2020. Most of the phone surveys have been reweighted, to at least partially address problems of representativeness (Ambel et al. 2021; Brubaker et al. 2021).<sup>7</sup> Fourth, we use distributional changes available in the literature or made available to us by World Bank teams. Fifth, we use sectoral growth rates from national accounts to capture at least the limited heterogeneity across sectors.<sup>8</sup> Sixth, when none of the above sources are available, we use per capita GDP growth rates from national accounts 5 Castaneda et al. (2022) discuss the available 2020 surveys and how COVID-19 led to changes in survey methodologies.

6 At the time of writing, such surveys have been conducted in 85 countries across all developing regions. We only use a subset because some of the countries also conducted a traditional household survey (which we used instead) and because some of the phone surveys do not contain answers to the questions we need. 7 The HFPSs are not nationally representative for a few reasons. First, households without a phone are outside the sampling frame. Second, among the households contacted, a non-negligible share declines to be interviewed. Third, there are many missing values in our key variable of interest, the share of households that have experienced income declines or increases. Fourth, in a few countries, the surveys did not cover the entire country. In the Democratic Republic of the Congo, most notably, the survey only covered Kinshasa, less than 20% of the whole population. It is important to note that reweighting cannot address these issues if the households captured are fundamentally different from households not captured, conditional on covariates. We could further post-stratify the weights based on the missing values of our variable of interest. However, we use these surveys only to get statistics for types of households (e.g. rural households that experienced an income loss with the head-of-household between 31 and 45 years of age, with any secondary schooling, and living in a family of between 2 and 4 people). Since we would have used the same variables to post-stratify and define types, the post-stratification would have only shifted the total weight from some types to others, without affecting the distribution of weights within types.

8 We use sectoral growth rates data from the April 2022 vintage of the World Bank’s Macro and Poverty Outlook (MPO).

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