# **3. Empirical approach**

## **3-1. Conceptual framework**

The pandemic brought serious health and economic impacts that reduced urban households’ incomes, leading to welfare losses. As described in the previous section, the results from the HFPS indicate severe employment and income impacts in Ethiopia and Kinshasa. An important channel of these income losses is the reduction in urban residents’ mobility. For instance, the government restricted public gatherings in Ethiopia and people entering the CBD area in Kinshasa. Reduced mobility constrained workers’ labor performance because of the difficulty in commuting to their workplace. Moreover, it constrained business operations, job search, and work-related interactions. In low-income countries like Ethiopia and the DRC, few can work remotely.

Most importantly, labor impacts of the COVID-induced mobility shocks are expected to be spatially heterogenous. A key question to be addressed in this paper is how COVID-19 impacts on workers’ labor outcomes vary depending on their locations—for example, which towns and where in the towns. For town characteristics, we focus on population and population density. Given the expected higher contagion risks, the government may have implemented tighter non-pharmaceutical interventions in larger and denser towns. Fearing about contagion risks, people may also tend to travel less in such towns. Moreover, mobility and accessibility are probably more important for workers in larger and/or more densely populated towns, as their labor income premium stems from agglomeration economies. Within a city, workers living far from job opportunities, such as those who live in suburbs and peri-urban areas, are likely to rely more on freedom of movement to access jobs. With their movement more severely affected, impacts on labor outcomes are likely to be larger.

## **3-2. Data**

Our empirical analysis primarily relies on panel household phone surveys that have been regularly collected since the outbreak of the COVID-19 pandemic in Ethiopia and Kinshasa. In addition, we use Facebook movement data to construct an index to capture mobility trends.

_Ethiopia and Kinshasa high-frequency phone surveys_ In Ethiopia, the World Bank designed and conducted an HFPS of households to monitor the effects of COVID-19 on Ethiopia’s economy and people and to inform interventions and policy responses (Wieser et al. 2020). The HFPS builds on the national longitudinal Ethiopia Socioeconomic Survey (ESS) that the Central Statistical Agency (CSA), Ethiopia’s national statistical office, carried out in 2019 in collaboration with the World Bank. The HFPS drew a subsample of the ESS sample that was representative of households with access to a working phone. The HFPS is collected every month for 12 survey rounds, starting in April 2020. The 15-minute questionnaire covers topics such as knowledge of COVID and mitigation measures, access to educational activities during school closures, employment dynamics, household income and livelihood, income loss and coping strategies, and assistance received.

To monitor the impacts of COVID-19, the National Institute of Statistics ( _Institut National de la Statistique_ , INS), the national statistical office of the DRC, and the World Bank have collected HFPS data every month since June 2020 (INS 2020). The HFPS builds on the baseline Kinshasa Household Survey, which was collected from about 2,600 households in Kinshasa by the INS and the World Bank in December 2018. The Kinshasa HFPS drew a subsample of the Kinshasa Household Survey sample that was representative of households with recorded phone numbers. Respondents of the surveys were not 7