Another, and more important limitation in the HFPS data relates to self-reported loss of household income. Income losses are collected through a binary indicator on whether households’ incomes recently declined or not, which is susceptible to measurement errors and makes it difficult to quantify the COVID19 impacts on their incomes. In addition, the mobility trend in the Facebook data may not represent Kinshasa residents, as the data is collected from the smartphone app users, who are likely to be richer and younger than other urban populations.

With these limitations in mind, the results of our analyses highlight the spatial heterogeneity of COVID19 impacts on urban households’ incomes as well as the key role of mobility and accessibility in urban agglomerations. In Ethiopia, households in large and dense towns were more likely to lose their labor incomes in the early phase of the pandemic, and their recovery was slower than other households. Disadvantaged households in those large and dense towns are particularly affected, such as those with female heads, low-skilled heads, self-employed heads, and poor households. The city-level analysis for Kinshasa demonstrates that workers’ income is more elastic to mobility shocks in suburbs and precarious and highly densely populated neighborhoods. Disadvantaged workers, such as female and low-skilled workers, in those areas were particularly affected. These findings highlight the need for policies and infrastructure in the medium and long terms to enhance mobility and connectivity and build functional urban agglomerations.

Our study is related to two strands of literature. First, we contribute to the rapidly growing literature of the impacts of COVID-19 on individual/household labor and economic outcomes. Various studies analyze economic and welfare impacts at the household level, such as Adams-Prassl et al. (2020); Baek et al. (2020); Chetty et al. (2020); and Crossley, Fisher, and Low (2021) for developed countries and Bundervoet, Davalos, and Garcia (2021); Egger et al. (2021); and Khamis et al. (2021) for developing countries. While a few studies examine COVID-19 impacts on employment and incomes at the subnational levels (for example, Beyer, Jain, and Sinha 2020), a study on within-city variations of COVID-19 impacts is still rare. For instance, by combining a variety of private and public data sources, Chetty et al. (2020) analyze COVID-19 impacts on consumer spending, employment, and various other indicators at a geographically granular level (that is, zip code level) in the United States.<sup>3</sup> Our study adds to this literature on subnational and within-city analysis of COVID-19 impacts on employment and income.

Second, our work contributes to the literature on job accessibility in urban labor markets. Poor connectivity between firms and workers—as well as among themselves—is a major friction in urban labor markets, constraining productivity gains from agglomeration economies. Urban workers are more productive when job opportunities are physically more accessible, through, among others, lower job search costs and improved matching with firms (Combes and Gobillon 2015; Duranton and Puga 2004). Unfortunately, in many African countries, urban workers endure poor job accessibility in crowded, disconnected, and costly cities, where land use is fragmented and workers’ mobility is constrained by the lack of affordable and reliable transportation (Lall, Henderson, and Venables 2017). To exacerbate lowskilled workers’ situation, limited mobility and accessibility disproportionally affect them. Previous studies have found the effect of workers’ residential areas within a city or metropolitan area on their labor outcomes (Chetty, Hendren, and Katz 2016; Gobillon, Magnac, and Selod 2011). Among the mechanisms of such location effects on employment is the limited physical accessibility to jobs, which could exacerbate labor outcomes of already disadvantaged workers, such as African Americans in the context of

> 3 Several other studies also analyze at a similar geographic level but tend to focus on COVID-19 cases rather than employment outcomes—for example, Glaeser, Gorback, and Redding (2020) examine the effects of mobility for the spread of COVID-19 at a zip code level in New York City and a few other cities in the United States.

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