available interactive dashboard.<sup>5</sup> The dashboard and its underlying microdata, henceforth referred as the harmonized HFPS data, is the basis for much of the evidence presented in this paper. The paper draws on the findings of several recent publications, some of which are updated with additional data and/or complemented with additional analysis of harmonized HFPS data.

Since phone surveys like the HFPS cannot collect reliable data on income or consumption, the analysis relies on proxy indicators, such as self-reported job or income losses, missed meals (as a proxy for food insecurity), and so on. While the HFPS are designed to be nationally representative (using reweighting methods to adjust for differential response rates among subgroups of the population), several limitations arise because of the inherent nature of phone surveys in developing country setting.<sup>6</sup> Three sources of potential inaccuracies are particularly important. First, groups with limited network coverage or no access to phones, who are also likely to be among the poorest, are likely to be under-represented in the sample – a bias that may not be fully corrected by sample re-weighting that can be only done using observable characteristics of households. Second, individual level indicators, such as employment and unemployment, are likely to have been overestimated in the HFPS relative to the full population, since the respondents to phone surveys were household heads in most countries, who are more likely to be male and older (and if a woman, more likely to be working than the general population of women in a country).<sup>7</sup> Third, the share of missing values in indicators of interests is at times quite high, and regularly higher than in household surveys. Since the reweighting has not factored variable-specific missing values into account, if indicator values are not missing at random, estimates may be biased.

These biases suggest the need for caution in interpreting the findings from HFPS data. In particular, direct comparisons between employment and labor force participation rates in HFPS with those obtained from conventional surveys can be problematic. However, the caveats are less of a concern when it comes to tracking indicators like job and income losses over time among those who were working before the pandemic and comparing these losses across different population groups. Using evidence from five countries, Kugler et al (2021) show that the bias is of similar magnitude across gender, education, and urban/rural groups, meaning that HFPS data does give accurate picture of group disparities in employment rates following the onset of the pandemic. Thus, for all its flaws, HFPS data is a valuable source for identifying and measuring inequalities in the socioeconomic impact of the pandemic on households.

**4. Short-term impacts on inequality – what we know from phone survey data**

In addition to widening gaps in well-being across countries (see Section 2 above), the pandemic might have affected global inequality through its short-term impact on within-country inequality in developing countries. To understand the distribution of economic impacts within countries in the first three months of the pandemic, we present evidence below on the distribution of job and income losses, food insecurity, and continued learning, based on harmonized HFPS data from 52 developing countries and up to 47,000 respondents. The findings are based on an update of earlier work by Bundervoet et al (2021), which was

> 5 <u>https://www.worldbank.org/en/data/interactive/2020/11/11/covid-19-high-frequency-monitoring-dashboard. The</u> dashboard currently includes 126 indicators covering 15 topics for 64 countries, with multiple rounds of data for some countries.

> 6 See Bundervoet et al (2021) and Kugler et al (2021) for detailed discussions.

> 7 The respondents were household heads in all countries where the phone surveys were sampled from a nationallyrepresentative pre-pandemic survey.

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