potential issues also need to be weighed against the alternative of not conducting interviews during the crisis.<sup>6</sup> For each of the four countries, Appendix Table A1 presents characteristics of the sample of households included in the last face-to-face survey and compares it to the subsample of households that were included in the first round of the HFPS. The samples are similar in terms of household and household head characteristics, though there are some differences in household head education and in asset ownership, especially in Ethiopia. Furthermore, as Kugler et al. (2021) describe, household heads and their spouses are overrepresented relative to other household members in the HFPS and as a consequence, employment rates in the sample are higher than in the full population when only one respondent is interviewed. Using different weighting strategies to correct for potential bias, Kugler et al. (2021) show that sampling bias is of similar magnitude across gender, education, and urban/rural groups, so that the HFPS can accurately capture inequalities in employment impacts between these groups, which is the focus of our analyses. Still, in some countries, given that most of the respondents are heads of household, the HFPS may significantly overestimate employment for young workers compared to adults, thereby underestimating the employment penalty faced by youth that are not head of households. This is important to keep in mind as it can affect our estimates of inequalities by age groups. In summary, our results are best seen as reflecting the situation of household heads, rather than the general working age population in these countries.

For the analysis in this paper, we use HFPS data collected between April 2020 and March 2021. Due to differences in the timing of the surveys, this period covers 10 rounds for Ethiopia and Nigeria, 8 rounds for Malawi, and 5 rounds for Uganda. The dates of data collection in each country, sample size and average stringency index for each round are presented in Appendix Table A2.

# **Variables and descriptive statistics**

Using the LSMS-ISA and HFPS data, we construct several indicators to analyze early-phase and one-year labor market impacts and income loss. Descriptive statistics on all outcome indicators and household and respondent demographics are reported in Appendix tables A3-A4.

To measure the early-phase labor market impacts of the COVID-19 pandemic, we analyze three indicators: job loss, re-employment, and change of industry. Job loss captures whether respondents who were working before the pandemic (as self-reported, retrospectively, during the first HFPS round) lost their job between the start of the pandemic and July 2020. This is measured using all HPFS rounds implemented up to July 2020.<sup>7</sup> Note that job loss includes the loss of paid employment, but also loss of other types of work, including self-employment and family farming. For the sub-sample of respondents who lost their job during this period, the re-employment indicator captures whether they were working during at least one of the HFPS rounds between July and October 2020. And finally, for those who were re-employed, we construct an indicator for “changing industry” by comparing their industry of work in the last pre-COVID-19 face-toface LSMS-ISA survey with their industry of work upon re-employment. We distinguish between agriculture (including mining); manufacturing and utilities; construction, transport and communication, financial and business services; commerce; public administration; and other services.

The early-phase labor market indicators are summarized in Table A3 and Figure 5. Among respondents who worked pre-COVID-19 (81% of all respondents), on average 34% lost their job in the first four months of the pandemic. The rate of job loss was much higher in Nigeria (61%) compared to the other countries

> 6 Ambel, McGee and Tsegay (2021) analyzed the survey sample and selection process of the HFPS used in this paper, they found that the contacted samples were biased toward wealthier households with higher living standards. The coverage rates (i.e., the share of households in pre-COVID-19 with phone numbers) for Ethiopia, Malawi, Nigeria and Uganda were: 79%, 73%, 99%, and 73% respectively. While the response rates (i.e., the households interviewed as a share of the initial sample) were: 60%, 74%, 65%, and 93% respectively.

> 7 In each HFPS round, respondents were asked whether they worked during the past week. In round 1, respondents who worked at the time of the survey were _not_ asked about their pre-pandemic employment status, so we assume that everyone employed at the time of the HFPS first round was also working before the pandemic. HFPS data for Latin America show that this assumption has minor effects on the job loss estimates, because few people began working immediately after the pandemic started (Khamis et al. 2021a).

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