# **2.1 Early impacts of the pandemic**

**We hypothesize that urban household enterprise income fell, few coping strategies were utilized, and women, youth and the poor were disproportionately affected by the COVID-19 shock.** The HFPS data do not allow us to observe the extent of income declines, though there is a measure of their prevalence. We expect that income declines were more prevalent among urban household enterprises than urban households that rely on other sources of income. We also expect that the disproportionate negative income impacts on women, youth and the poor that were observed in other studies will emerge in these data. Finally, given the scarcity of public and private support such as insurance or safety nets in urban Sub-Saharan Africa (Guven et al. 2022), we expect that few household enterprises were able to access these tools or to use them to offset the impact of the pandemic.

**To analyze the early impact of the pandemic on enterprise income and use of coping mechanisms, we pool information from the first wave of the HFPS for the 13 countries in the sample and estimate linear probability models.** The sample is restricted to people who were employed and report information on income decline. Table 1 shows the month of the lockdown, month of the first survey wave, and the share of respondents that are household heads. Most countries locked down in March or April 2020, while most data were collected one to three months after the first lockdown. Due to slight differences in the questions included in each country survey, the subset of countries differs by definition and across empirical exercises. We use the household sample weights.

**We carry out four empirical exercises to better understand the initial impacts of the crisis** . We estimate each model for the sample derived from each of the two definitions of household enterprises described above. In each case, observations are weighted using the survey weights included in the HFPS.

**First, we examine if the incomes of urban households with enterprises changed in the first months of the COVID-19 pandemic, relative to urban households without household enterprises.** The exercise uses the full sample to assess if households with enterprises have differential propensity of income loss compared to households that do not run an enterprise. We estimate a LPM that takes the following general form:

_Yhpc_ takes the value 1 if the household ( _h)_ in province _p_ within country _c_ reported a decline in total income since the pandemic started and 0 if it did not.<sup>17</sup> As noted above, the data only provide the binary dummy; there is not information on the magnitude of the change in income.

_Ehpc_ is a vector of dummy variables for the type of employment (when using the household head definition) or the type of enterprise (when using the household business definition). The vector for the type of head employment are farm self-employed,<sup>18</sup> non-farm self-employed, wage employees,

> 17 The survey question is: "Has your Total Household Income changed since the pandemic started?"

> 18 Although the category is “self-employed or works in a family business”, we shorten it to “self-employed” for ease in presentation

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