# **1. Introduction**

The COVID-19 pandemic has brought devastating economic impacts to low- and middle-income countries. The containment measures implemented by the governments to prevent the spread of the virus, such as the orders of lockdowns, the closure of non-essential businesses, and social distancing, have resulted in employment and income loss among people with limited coping strategies. The number of global extreme poor—who live on less than US$1.9 per day in 2011 PPP terms—is projected to have increased for the first time during the last two decades (World Bank 2020a). A recent study of harmonized household phone surveys in 34 developing countries highlights the immediate impacts of the pandemic: 36 percent of respondents stopped working because of the pandemic and 64 percent of respondents reported income loss (Bundervoet, Davalos, and Garcia 2021). Moreover, COVID-19 exacerbated existing inequalities and those who were disadvantaged before the pandemic, such as women, youth, and low-skilled workers, are experiencing even greater challenges.

Moreover, the impacts of the COVID-19 pandemic on household incomes and welfare have been spatially uneven. It has been widely observed that the pandemic more severely affected urban households—many of whom are informal, self-employed, or casual workers—in many low- and medium-income countries (Bundervoet, Davalos, and Garcia 2021). However, there are other important and less analyzed spatial factors that contributed to heterogeneous COVID-19 impacts on urban households’ employment and incomes. Households in cities and towns with a larger population size and higher population density may have suffered from larger income losses, because of higher contagion risks and the government’s implementation of tighter restrictions on people’s movement and business operations. In addition, the mobility shocks induced by the COVID-19 pandemic probably had larger effects on households in larger and denser cities, as their agglomeration economies are hinged on connectivity and accessibility. Within a large city, workers in poorly connected neighborhoods in the suburbs and peri-urban areas may have found it difficult to access jobs and operate their businesses.

This paper aims to analyze such spatial heterogeneity in the COVID-19 impacts on urban household incomes. We look at the early days of the pandemic in Ethiopia for the analysis of between-city heterogeneity. We turn to Kinshasa, Democratic Republic of the Congo (DRC) to exemplify within-city heterogeneity. Our analysis primarily relies on panel household phone surveys—high-frequency phone surveys (HFPS)—that have been collected in many low- and middle-income countries since the COVID19 outbreak.<sup>2</sup> We focus on the first six rounds of the survey from April to October 2020 for Ethiopia and the first six rounds from June to December 2020 for Kinshasa. During these periods, urban households’ incomes experienced a gradual recovery from the initial shocks. In our Ethiopia analysis, we estimate two-way fixed-effects regression models to examine the changes in the probability of households experiencing income reductions across time and locations. For Kinshasa, we estimate the elasticities of the probability of income reductions with respect to mobility changes—which are captured by the Facebook movement data—and examine how the elasticities vary by locations, such as the distance from the central business district (CBD) and neighborhood density.

It is important to highlight the challenges and limitations of our empirical strategy. First, the HFPS data have limitations. The data were collected only from those who provided phone numbers in the baseline face-to-face surveys, which had been collected in June–September 2019 in Ethiopia and December 2018 in Kinshasa. While this is potentially a concern in case of rural households, many of whom do not own mobile phones, it is less of a concern for urban households with much higher phone penetration rates.

> 2 World Bank COVID-19 High Frequency Monitoring Dashboard

<u>https://www.worldbank.org/en/data/interactive/2020/11/11/covid-19-high-frequency-monitoring-dashboard</u> 2