with school-age children. Re-employed women were also much less likely to return to a different industry than re-employed men (columns 5-6). Women’s lower mobility may be one of the reasons they are less likely to be re-employed at all. The gender gaps in re-employment and change of industry are also driven by the urban sub-sample (Tables A7 and A8).

We find that age is associated with a lower probability of job loss. As described above, the HFPS samples are more likely to overestimate employment among young people relative to older people. We are therefore likely underestimating the relative disadvantage of younger respondents. Older workers were less likely to be re-employed and to change industry, but these age effects are smaller and statistically insignificant once we consider the pre-COVID-19 industry.

We find no clear education gradient in early phase labor market outcomes. Respondents with a tertiary education were more likely to report job loss than those with no education or less than primary education. The association is smaller and insignificant once we control for the pre-COVID-19 industry, suggesting that highly educated workers were more affected due to their industries being harder hit (columns 1-2).<sup>15</sup> There are strong rural-urban differences. Urban respondents were 6 to 9 percentage points more likely than rural respondents to lose their jobs and significantly less likely to be re-employed. These findings are similar if we control for the pre-COVID-19 industry and hence cannot be explained by relatively limited job losses in agriculture, which dominates in rural areas. The stronger impact on jobs in urban areas is in line with descriptive patterns across Sub-Saharan Africa as reported in Kugler et al. (2021, Table 3), but is more pronounced in our sample. It is driven by the Nigerian and Ethiopian samples (Tables A6 and A7).

Income diversification is negatively related to job loss, suggesting that the opportunity to reallocate work time to different economic activities undertaken by household members helped ensure employment. It is positively related to changing industry among those re-employed. Finally, workers in agriculture and commerce were least likely to report job loss, while manufacturing & utilities and public administration workers were most likely to report job loss (Table 1, column 2).<sup>16</sup> The pre-COVID-19 industry is not significantly related to re-employment, but agricultural workers were least likely to return to a different industry.

# **Results: One-year impacts**

In this section we analyze heterogeneity in labor market outcomes beyond the first few months of the pandemic. The impacts of the pandemic may have evolved differently during the year, as governments lifted or adjusted restrictions, and as people (and their farms and businesses) adapted to the situation. The Nigerian government gradually lifted restrictions between June and October 2020, while most restrictions in Ethiopia and Uganda remained until October. By mid-November, restrictions in all countries were at their lowest level since April 2020 and they did not increase much again until June 2021. Nevertheless, job losses continued after July 2020 in Malawi, Nigeria, and Uganda (see Figure 6).

There is no significant association between gender, age, or rural/urban area and one-year job loss (Table 2, columns 1-2), nor with the probability of being jobless in February/March 2021 (columns 3-4). While coefficient signs in the columns 1 and 2 are the same as for early-phase job loss, they are not statistically significant. We also find no significant gender gap in the urban sub-sample (Tables A9 to A11). Urban respondents were jobless for around 0.2 additional months compared to rural respondents, but the

> 15 There is similar reduction in the coefficient on tertiary education when we control for pre-COVID-19 employment status, in the smaller sample for which we have this information (results available from the authors). Hence the concentration in wage employment and in particular industries both account for the disproportionately job loss among highly educated workers.

> 16 We use industry of work from the pre-COVID-19 face-to-face survey because the HFPS in Ethiopia did not collect data on industry of work. This means the sample is reduced to respondents who reported, in the HFPS, that they were working pre-COVID-19 and who were working in the last face-to-face survey. Results in column 1 are similar if we limit the analysis to this more restrictive sample.

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