businesses, restricting movement, or imposing curfews (Hirvonen et al., 2022). In Malawi, the President declared a state of disaster on 20 March 2020, which included closing schools and limiting the size of public gatherings. A stay at home order was issued in April. However, this order faced legal challenges, which culminated in the High Court barring the regulation and preventing the stay-at-home order from going into effect, leaving daily economic activity largely intact (Josephson et al., 2021). Nigeria’s response primarily occurred at the state level. Most Nigerian states closed schools and suspended large gatherings by 24 March 2020 and suspended inter-state travel on 23 April. While non-essential shops as well as restaurants were ordered to close, the government’s attempts to impose these closures along with curfews, social distancing, and self-quarantine, were largely ignored, meaning daily economic activity was relatively unchanged (Jacobs and Okeke, 2022). The government lifted the closure order less than a month later. Compared to lockdowns in China, Europe, and the United States, the closures of businesses and the restrictions to daily activities in Ethiopia, Malawi, and Nigeria were substantially less strict. This is important since it means that households in these countries were less constrained in pursuing livelihood opportunities than those in regions of the world with government imposed lockdowns.

To account for the variation in COVID-19-related restrictions over time, we use _Our World in Data_ ’s COVID-19 Government Stringency Index in some of our empirical specifications (Mathieu et al., 2020). The index considers nine metrics to calculate daily scores for each country: school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls. The stringency index is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter regulatory regime. To match these daily data to each round of our data, we take the average daily score during each survey period. Figure 1 displays the average government stringency index in each country over time.

# **2.2 Sample Selection and Surveys**

To examine the relationship between livelihood diversification and welfare outcomes, we use panel data from high-frequency phone surveys (HFPS) in Ethiopia, Malawi, and Nigeria (WB, 2022; NSO, 2022; NBS, 2022). In each country, interviewers conducted these surveys on a monthly basis with households for a period of at least 12 months following the outbreak of COVID-19.<sup>2</sup> The sample for the HFPS is drawn from households that had been interviewed during the most recent (2019) round of the national longitudinal household survey implemented by the respective national statistical office, with assistance from the World Bank (CSA, 2019; NSO, 2020; NBS, 2019). These pre-COVID-19 Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-

> 2The following agencies implement the monthly surveys with support from the World Bank Living Standards Measurement Study (LSMS): Laterite Ethiopia, the Malawi National Statistical Office, and the Nigeria Bureau of Statistics.

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