youth were also more likely to stop working and lose wage employment in the initial phase of the crisis, relative to adults, though bias due to the nature of the phone survey makes age differentials more difficult to measure. There is currently little systematic evidence on the distributional impacts of COVID-19 in Sub-Saharan Africa.

# **Furthermore, little is known about the medium-term impacts as the pandemic disrupted**

**economies for more than a year.** Kugler et al. (2021) finds a partial recovery in employment over the first six months of the crisis (April and August) was stronger for groups hit hardest by the crisis, such as women, less educated workers, and youth. But detailed information from SubSaharan Africa is difficult to obtain due to data limitations. The pandemic disrupted traditional data collection systems in many countries and alternative data sources such as administrative data are rarely available. While high-frequency phone survey data has compensated for such disruptions in several countries, these data are used to measure short-term dimensions, such as immediate shocks and short-term recovery measures.

**Understanding how the COVID-19 crisis affected urban informal household enterprises and how the owners responded sheds light on the type of social policy interventions that can support this largely unserved population** . This paper leverages High-Frequency Phone Surveys supported by the World Bank to estimate how COVID-19 affected the urban informal sector in 13 Sub-Saharan African countries in the short term and in 27 countries in the medium term. Specifically, we explore which types of urban household enterprises experienced a decline in farm or nonfarm income, which types of households utilized different coping strategies, the nature of the relationship between the use of coping strategies and income declines, and how these trends differed by gender and age of the household head as well as poverty status of the household. We consider these effects both at the beginning of the pandemic, as well as how patterns shifted throughout the first year of the pandemic.

**There are six main findings regarding households with enterprises in the 13 sampled countries.** First, households with non-farm enterprise income were 26 percentage points more likely than households with only wage income to report a decline in total household income. Similarly, households headed by non-agricultural self-employed workers were 20 percent more likely to report declines in total income than households headed by wage workers. Second, declines in non-farm enterprise income were 9 percent more prevalent among female headed households. Third, households that reported declines in farm income were 12 percentage points more likely than those that did not lose income to sell assets in response, as were male-headed households in comparison to female-headed households (by 11 percentage points). Fourth, households that reported losses to enterprise income were equally as likely as households that did not to report losses to receive public and private assistance. The least poor seemed to have had greater access to public and private assistance. Fifth, as the crisis progressed, wage employment recovered more quickly than self-employment and gains were faster for wealthier households and those headed by males and by adults. The gender labor force participation gap widened as men went back to work. Sixth, women, adults, and non-poor self-employed household heads were more successful than men, youth, and the poor at leveraging external sources of support early in the pandemic, but these

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