# **Methodology**

First, we regress an indicator for household income loss on gender, age, and education of the household head, rural/urban location, and the presence of children (Table 3, column 1).<sup>18</sup> Second, we run the same regression but also including indicators for whether the household received income from farming, nonfarm enterprises, wages, or remittances, in the year before the pandemic (Table 3, column 2).<sup>19</sup> By comparing results from these two analyses, we can assess the extent to which differences in income sources account for heterogeneity in income loss by household demographics. Third, we estimate how household characteristics are related to the probability of income loss for each income source (family farming, non-farm enterprises, wages, remittances from within the country, and remittances from abroad), within the subsample of households receiving income from the respective source (Table 3, columns 3-7). These analyses all focus on early-phase income loss based on the pooled data from the four countries’ first HPFS rounds. We then run the same models for one-year income loss, pooling the data from Nigeria’s Round 9 and Uganda’s Round 5 (as discussed in section 3). The results are reported in Table 4.

# **Results: Early-phase impacts**

In the early phase, urban households and households with children were more likely to lose income, while age and tertiary education of the household head are associated with a significantly lower probability of income loss (Table 3, column 1). These results are similar (in some cases a bit smaller in magnitude) when we condition on income source indicators (column 2), suggesting they are not primarily driven by differences in the types of income sources households relied on.

Income sources do matter (column 2): there is a strong negative association between income from wages and income loss, suggesting that wage employment was an important factor protecting households from income loss, as a relatively secure source of income during the early months of the pandemic. Households with NFE income, on the other hand, were most likely to lose income, while income from family farming and domestic remittances are also associated with a higher probability of income loss.

The higher probability of income loss among urban households reflects a combination of their higher likelihood of losing NFE income and wage income (columns 4 and 5), although the urban coefficient is not statistically significant in those estimations. The presence of children increases the probability of income loss across all sources (for young children) or most sources (older children), but these effects are also not statistically significant (columns 3-7). Tertiary education of the household head reduces the probability of income loss because it is associated with a 0.26 lower probability of losing wage income (column 5). The negative effect of household head age on income loss is driven by a lower probability of losing wage income and domestic remittances (columns 5 and 6). Finally, while there is no significant association between the gender of the household head and the probability of total income loss, female-headed households are significantly more likely to report a loss of NFE income (column 4).

In general, the results illustrate that inequalities in early-phase income loss varied substantially across income sources. They also confirm that inequalities early-phase individual job loss, which was concentrated among young, urban, and female workers, does not translate into the same pattern of household level income loss inequalities.

> 18 Income loss is a dummy variable that takes the value of one if the household reported income loss after the pandemic outbreak during the first round of the HFPS compared with income one year prior to the interview.

> 19 Appendix Table A12 reports estimation results for rural and urban subsamples and by country.

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