<mark>The pre-COVID-19 LSMS-ISA-supported surveys that serve as sampling frames for the phone surveys were designed to be representative at the national, regional and urban/rural levels. These surveys include the Ethiopia Socio-economic Survey (ESS) 2018/19, Malawi Integrated Household Panel Survey (IHPS) 2019, Nigeria General Household Survey (GHS) - Panel 2018/19, and Uganda National Panel Survey (UNPS) 2019/20. In Ethiopia, Malawi, and Uganda, the phone survey attempted to call all LSMS-ISA households for whom at least one phone number was available either for a household member or for a reference individual. In Nigeria, a national subsample was drawn from the universe of LSMS-ISA households with phone numbers.</mark> The anonymized survey data and documentation are accessible through the World Bank Microdata Library<sup>_12-15_</sup> and are <mark>comparable across countries, based on the template questionnaire modules and the phone survey sampling guidelines that were made publicly available by the World Bank prior to the start of the phone survey activities</mark> (see Supplementary Materials – Methods).

We directly measure the effects of the pandemic on 8,603 households across the four countries, as well as how households attempt to cope with these effects. We use well-established sampling techniques<sup>_7-9_</sup> to correct for potential selection bias associated with not interviewing households that do not own mobile phones or that cannot be reached despite repeated call attempts (see Supplementary Materials – Methods for more details on the calculation of phone survey sampling weights). The correction for selection bias allows us to provide estimates of the total number of households, individuals, and children associated with any of the reported outcomes.

We then use reduced-form econometric techniques,<sup>_10_</sup> which are common to the field, in order to estimate heterogeneity in effects across 1) countries, 2) rural and urban sectors, 3) pre-COVID-19 wealth, 4) gender, and 5) time (see Supplementary Materials - Methods). Tracking how people’s lives are differentially affected by the COVID-19 pandemic can enable governments and policy makers to better understand the circumstances faced by their citizenry and to make data-driven, informed policy decisions. The need to understand the contemporaneous impacts and coping strategies of households is and will continue to be important for low-income countries, as they are likely to lack access to a vaccine for longer than high-income countries.<sup>_16,17_</sup> The longitudinal data collected through the high-frequency phone surveys cultivate this understanding by documenting near-real-time trends.

# **3. Results**

# **Government action is reflected in household knowledge of and behavior around COVID-19:**

With the global spread of the SARS-CoV-2 virus, numerous low-income countries followed worldwide trends by declaring states of emergency, issuing stay-at-home orders, closing schools, and imposing curfews.<sup>_18_</sup> Considering the countries in our data set, Ethiopia closed schools on March 16<sup>_19_</sup> and declared a state of emergency on April 8.<sup>_20_</sup> Nigeria and Uganda issued stay-athome orders on March 29 and 30, respectively.<sup>_21, 22_</sup> Malawi attempted to issue stay-at-home orders 3