metadata: authors: 0: author: Batana,Yele Maweki
1: author: Nakamura,Shohei
2: author: Rajashekar,Anirudh Venkatanarayan
3: author: Viboudoulou Vilpoux,Mervy Ever
4: author: Wieser,Christina
count: Ethiopia,Congo, Democratic Republic of
docna: 0: docna: Spatial Heterogeneity of COVID-19 Impacts on
            Urban Household Incomes : Between- and Within-City Evidence
            from Two African Countries
docty: Policy Research Working Paper
subtopic: Labor Markets,Employment and Unemployment,Transport Services
teratopic: Social Protections and Labor,Transport
abstracts: cdata!: This paper examines spatial
            heterogeneity in the impacts of the early days of the
            COVID-19 pandemic on urban household incomes in Ethiopia and
            Kinshasa, Democratic Republic of Congo. Combining new panel
            household surveys with spatial data, the fixed-effects
            regression analysis for Ethiopia finds that households in
            large and densely populated 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 groups, such as female, low-skilled,
            self-employed, and poor, particularly suffered in those
            towns. In Kinshasa, labor income-mobility elasticities are
            higher among workers—particularly female and/or low-skilled
            workers—who live in areas that are located farther from the
            city core area or highly dense and precarious neighborhoods.
            The between- and within-city evidence from two Sub-Saharan
            African countries points to the spatial heterogeneity of
            COVID-19 impacts, implying the critical role of mobility and
            accessibility in urban agglomerations.
geo_reg_key: 517191,517196
repnme: repnme: Spatial Heterogeneity of COVID-19 Impacts
            on Urban Household Incomes : Between- and Within-City
            Evidence from Two African Countries
display_title: Spatial Heterogeneity of COVID-19
            Impacts on Urban Household Incomes : Between- and
            Within-City Evidence from Two African Countries
listing_relative_url: /research/2021/08/33376596/spatial-heterogeneity-covid-19-impacts-urban-household-incomes-between--within-city-evidence-two-african-countries
topicv3: Transport m1326661 135,Population, Aging and Health m1327736 640,Jobs m1503931 2670,Self-Employment m1504000 2698,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2021-08-30T00:00:00Z
dois: 10.1596/1813-9450-9762
historic_topic: Social Protections and Labor,Transport
origu: EFI-EAP-POV-Poverty and Equity (EEAPV)
no_of_pages: 36
author_keys: 0: author_key: 1156965
1: author_key: 1389722
2: author_key: 1679288
3: author_key: 1542968
4: author_key: 1297316
model_extractions: Policy Research Working Paper 9762 Spatial Heterogeneity of COVID-19 Impacts on Urban Household Incomes Between- and Within-City Evidence from Two African Countries _Yele Maweki Batana Shohei Nakamura Anirudh Rajashekar Mervy Ever Viboudoulou Vilpoux Christina Wieser_ Poverty and Equity Global Practice August 2021
Policy Research Working Paper 9762

# **Abstract**

This paper examines spatial heterogeneity in the impacts of the early days of the COVID-19 pandemic on urban household incomes in Ethiopia and Kinshasa, Democratic Republic of Congo. Combining new panel household surveys with spatial data, the fixed-effects regression analysis for Ethiopia finds that households in large and densely populated 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 groups, such as female, low-skilled, self-employed, and poor, particularly suffered in those towns. In Kinshasa, labor income-mobility elasticities are higher among workers—particularly female and/or low-skilled workers—who live in areas that are located farther from the city core area or highly dense and precarious neighborhoods. The between- and within-city evidence from two Sub-Saharan African countries points to the spatial heterogeneity of COVID-19 impacts, implying the critical role of mobility and accessibility in urban agglomerations.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at snakamura2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Spatial Heterogeneity of COVID-19 Impacts on Urban Household Incomes: Between- and Within-City Evidence from Two African Countries**

Yele Maweki Batana, World Bank Shohei Nakamura, World Bank<sup>1</sup> Anirudh Rajashekar, World Bank Mervy Ever Viboudoulou Vilpoux, World Bank Christina Wieser, World Bank Keywords: Accessibility; Mobility; Urban labor markets; Poverty; COVID-19 JEL Codes: R14, R21, R41, O18

> 1 Corresponding Author: snakamura2@worldbank.org. We would like to thank Ambar Narayan, Nistha Sinha, Tom Bundervoet, David Newhouse, Megha Mukim, and Judy Baker for their useful comments. We would also like to appreciate the support for data access from Alemayehu Ambel, David Newhouse, Siwei Tian, and Feraud Seuyong. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations or those of the Executive Directors of the World Bank or the governments they represent.
# **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
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.

3
the United States (see a review of the literature on spatial mismatch hypothesis by Gobillon and Selod [2014]).<sup>4</sup> We contribute to these strands of literature by showing a link between job accessibility and employment outcomes cities in Sub-Saharan Africa.

This paper is structured as follows: Section 2 describes the contexts in urban Ethiopia and Kinshasa. Note that the background about the spatial structures of Ethiopia and Kinshasa are provided in Appendix B. Section 3 explains our empirical approach, such as the conceptual framework, data, and econometric models. Section 4 presents the results and section 5 concludes.

# **2. Context**

_Ethiopia_ Ethiopia’s first COVID-19 case was observed in mid-March 2020. The number of confirmed COVID-19 cases reached over 159,072 by the end of February 2021, with nearly 2,365 recorded deaths. While the number of cases in Ethiopia peaked in August, the fact that the positive rate for the tests conducted remains at over 5 percent suggests that infections are likely to be underreported (Panel A in Figure 1).

The Government of Ethiopia has put in place a range of measures to mitigate the economic impact of the COVID-19 pandemic, while aiming at containing transmission. Right after the first few cases of COVID19 were detected, the government implemented a state of emergency—which remained in effect until September 2020—and adopted a comprehensive COVID-19 National Emergency Response Plan to ensure that efforts to fight the crisis are comprehensive and well-coordinated. Specifically, Ethiopia implemented surveillance at borders, conducted contact tracing, established designated quarantine facilities, ensured the supply of drugs and protective equipment, and embarked on several communication efforts to raise awareness on how to deal with the virus. To mitigate impacts on people and firms, authorities announced several economic measures, including additional expenditure on healthcare, provision of emergency food to the vulnerable, tax and social security payment deferrals, and liquidity injections and extension of forbearance measures in the financial sector.

The restrictions imposed by the government on people’s movement and business operations in the early stage of the pandemic resulted in a sharp decline in mobility levels. As shown in Panel A of Figure 1, the Facebook movement data indicate that the mobility level plummeted in April 2020 to 50 percent of the baseline level—which is February 2020. This reduction in mobility was caused by both people’s voluntary responses and government restrictions on public gatherings and transport services between regions.<sup>5</sup> Unlike other countries, there was no enforcement for the closure of nonessential businesses, including restaurants, and within-region transport services.

> 4 Andersson et al. (2018), for example, find that better job accessibility reduced the duration of joblessness among low-income workers—especially blacks, women, and older workers—after a massive layoff in the United States. In the context of cities in developing countries, Franklin (2018) finds that a transport subsidy to young workers resulted in their successful job search in Addis Ababa, Ethiopia. Indeed, many workers face a limited level of job accessibility in Sub-Saharan African cities, as shown by Nakamura and Avner (2021) for Nairobi, Kenya.

> 5 Ethiopia is a country with a federal system, consisting of 10 regions and 2 chartered cities.

4
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percent of urban households’ incomes were either reduced or had completely disappeared in the early weeks of the pandemic, according to the first round of the HFPS. Yet, employment rates rebounded in the second half of 2020 reaching pre-COVID levels in rural areas while remaining slightly lower than before the pandemic in urban areas (Panel B in Figure A1 in the Appendix). This also ensured that incomes recovered; yet, in October, income losses continued for 27 percent of urban households.

# _Kinshasa, DRC_

Since the confirmation of the first COVID-19 case on March 10, 2020, the Government of the DRC has implemented various measures to prevent the spread of the virus (Panel B in Figure 1). The government declared a state of health emergency on March 24, prohibiting gatherings and nonessential activities. For example, restaurants were allowed to open only for a limited duration during the day for takeaway orders. All state services adopted minimum service. Only essential personnel were required to ensure the continuity of state services. No office could accommodate more than five employees. This state of emergency lasted until July 22, 2020.

In Kinshasa, the governor announced the lockdown of the entire city on March 28, 2020 but cancelled the announcement on the same day due to a rapidly spreading panic among citizens. Instead, the city government locked down only the central business district (CBD) area of the city—Gombe—from April 6 to June 29, 2020. The central market of the city, located in the CBD area, was also closed until August 3. For the first month and a half of the lockdown, people’s entry into the CBD area was restricted. Later the restriction was relaxed, allowing people with a pass to come to work. Grocery stores and restaurants were also allowed to open on specified times and days. Even during this period, no lockdown policy was imposed to the areas outside the CBD and markets remained open.

It is worth noting that, according to official data from the Multisectoral Committee for the Response to COVID-19, as of mid-January 2021, Kinshasa had nearly 17,000 confirmed cases, representing about 80 percent of all identified cases in the DRC. As with many countries, the DRC is not immune to the second wave of the pandemic and saw an increase in confirmed COVID-19 cases by 75 percent in less than two months between the end of November 2020 and mid-January 2021. In addition to the measures taken previously, including the postponement of the resumption of school and academic activities, the government imposed a curfew from 9 p.m. to 5 a.m.

The immediate impact of the COVID-19 pandemic on citizens of Kinshasa was indicated by the fact that more than half of household heads were not working at the time of the first round of HFPS interviews in June 2020, about half of whom listed COVID-related reasons (see Figure A2 in Appendix A). For example, a third of workers did not work due to the lockdown policy. Yet, COVID-related reasons for not working declined in later rounds.<sup>6</sup> Even if employed, workers reported that they were not able to work as usual. In the first round of the HFPS, 22 percent of employed workers could not travel to work or work from home as usual in the past two weeks. The rate of workers who could not work as usual declined in later rounds, reaching 6 percent in September and only 1 percent in November.

> 6 In the first round of the HFPS, respondents were also asked about the government policies that affected their jobs or their household members’ employment. Most frequently reported was the lockdown policy (31 percent), followed by stay-at-home order (11 percent), closure of essential activities and markets (9 percent each), and movement restrictions (4 percent).

6
# **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
necessarily the heads of households, though the data include various employment information about household heads.

We use the Ethiopia HFPS data for our nation-wide analysis of urban areas, while relying on the Kinshasa HFPS data for our city-level analysis. We focus on the first six rounds of the Ethiopia HFPS, spanning April through October 2020 (Table 1). We restrict the sample for our analysis to 1,704 (Ethiopia) and 880 respondents (Kinshasa) who were interviewed in all included HFPS rounds.<sup>7, 8</sup> Summary statistics of the samples are reported in Table A1 for Ethiopia and Table A2 for Kinshasa in Appendix A.

**Table 1. Survey samples: Ethiopia and Kinshasa**

||||Survey rou<br>|nds<br>|||
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|**Ethiopia**<br>|||||||
|Period|APR 22 to|MAY 14 to|JUN 4 to|JUL 27 to|AUG 24|SEP 21 to|
||MAY 13|JUN 3|JUN 29|AUG 21|to SEP 18|OCT 14|
|Total respondents in urban areas|2,233|2,113|2,064|1,978|1,934|1,885|
|Interviewed in all rounds|1,704|1,704|1,704|1,704|1,704|1,704|
|(numberoftowns)|(103)|(103)|(103)|(103)|(103)|(103)|
|**Kinshasa**|||||||
|Period|JUN 16 to|JUL 23 to|AUG 25 to|OCT 16|NOV 7 to|DEC 5 to|
||JUL 9|AUG 11|SEP 17|to NOV 9|NOV 24|DEC 26|
|Number of respondents|1,038|967|1,001|959|942|926|
|Interviewedinall rounds|880|880|880|880|880|880|

In Ethiopia, the main outcome variables of our analysis are indicators about a decrease in labor and total incomes at the household level. The labor income variable indicates whether the household experienced a decrease in labor income since the month of the outbreak of COVID-19 (the first round) or during the last four weeks (the second and third rounds). The share of households with reduced labor income was 72.7 percent in the first round of the survey, declining to 57.5 percent in the second round and 44.5 percent in the sixth round (Table 2). The other household-level outcome variable of interest indicates a decrease in _total_ household income, declining from 62.2 percent in the first round to 27.1 percent in the six rounds. As further discussed in Section 3.3, our econometric analysis controls for assistance and remittances received by households.

In Kinshasa, the labor outcome variable indicates whether the worker did not work or was not fully paid during the last seven days. The share of individuals who experienced the labor income shocks declined from 57.7 percent in the first round to 47.0 percent in the second round and 14.4 percent in the sixth round. Unlike the Ethiopia HFPS, this variable indicates the income status of the respondents, who are household heads, instead of their households. Unfortunately, a household-level indicator is not available in the survey. Thus, even if the variable indicates the household head’s income reduction, the household’s incomes may have remained at the same level because of increased income from other household members.

> 7 Attrition is explained by the weariness of some households to be surveyed on a regular basis. To reduce this attrition, a telephone call credit is offered to each household that answered the questionnaire as an incentive. On the other hand, data collection teams sometimes reach households that could not be reached in the previous round, which explains why in some cases there is an addition in a round over the previous round.

> 8 In addition, we exclude respondents who have changed their residential locations since the baseline surveys, as their current locations are not known in the HFPS data. The HFPS asks whether the respondent has changed residential locations, although it does not ask the exact location.

8
<!-- Start of picture text --> (A) Log town population (B) Log town population density<br>20- 20-<br>[oya<<g8, ag8).oy<br>0- j 0-<br>6 8 10 12 14 16 6 7 8 9 1 11<br>Log of town population Log of town population density<br><!-- End of picture text -->
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<!-- Start of picture text --> v0<br>_<br>f=}<br>& 110<br>3<br>im<br>3 100<br>Fa<br>x8€ 90<br>3 Addis Ababa<br>= 80<br>70 Baseline 1 2 3(June) 4 (July/Aug) 5 (Aug/Sep) 6 (Sep/Oct)<br>(Feb) (April/May) (May/June)<br>HFPS round (month)<br><!-- End of picture text --> <!-- Start of picture text --> 115 110.7<br>§ 110<br>a<br>tL<br>2 105<br>&3 100 100.1 100.6 100.3<br>= 100<br>3B 95<br>2<br>S 90 87.6<br>8<br>aw8 gs, 81.9<br>80<br>Baseline 1 2 3 4 5 (Nov) 6 (Dec)<br>(Feb) (June/July) (July/Aug) (Aug/Sept) (Oct/Nov)<br>HFPS round<br><!-- End of picture text -->
<!-- Start of picture text --> (Round ) +B<br>(Round ) +B (Ro nd ) +<br>(Ro nd ) +B<br>(MoB )+B<br><!-- End of picture text -->
Column 1 in Table 3 reports the result for the probability of households losing labor incomes. The interaction terms between the survey rounds and the log of town population are statistically different from zero in the second and sixth rounds (0.020 and 0.016, respectively), indicating that households in large towns faced a higher chance of reduced labor incomes between the first and second round (i.e., latter half of May 2020) and between the fifth and sixth round (August/September 2020). By contrast, we do not observe such spatial heterogeneity for household total incomes in column 3. Panels A and B in Figure 6 show the trajectories of the predicted probability of labor and total incomes for towns with different population sizes.

We then estimate the model in Equation (2) to look at both town population and population density. Columns 2 and 4 in in Table 3 summarize the results for labor incomes and total incomes, respectively. Similar to column 1, the interaction terms between the first and sixth-round indicators, the log of town population and the log of town population density are significant in column 2 (0.032 and 0.025), pointing to higher impacts on labor incomes among households who live in large and densely populated towns. As shown in Panel (C) in Figure 6, households in large and densely populated towns indeed struggled, as indicated by their higher probability of income loss and the slow recovery. As for household total incomes, we see a clear interaction only in the first survey round (column 4).

As a robustness check, we estimate the models above using subsamples and different specifications. First, we estimate the specifications in columns 2 and 4 in Table 3 by using only the subsample of households that do not live in Addis Ababa. This is to test the possibility that our findings are driven by households in Addis Ababa, by far the largest city in Ethiopia and unique in many aspects. Second, we estimate the same set of FE regression models by excluding households whose main jobs are in agriculture, as those households may have been less affected by the pandemic. Finally, we add to the model in Equation (2) additional controls for household characteristics, such as age, sex, education, marital status, household size, consumption quintiles, and the industry of their primary jobs. We control for their trends by interacting those time-invariant variables with round FE. As shown in Table A3 in Appendix A, our findings are robust against all these variations.

We also find that disadvantaged group of households in large towns particularly struggled. Figure 7 summarizes the estimated results of the labor-income model in Equation (2) with the density variable replaced with other household characteristics (see Figure A4 in Appendix A for the results for total incomes). The pace of recovery among female-headed households has been slow in terms of labor incomes—particularly in large towns—in Panel (A), though the difference with male-headed households is not significant in the case of total incomes (Panel B). Low-skilled households have experienced higher probability of income loss throughout the six rounds and the pace of recovery has been a bit slower in large towns (Panels C and D). Self-employed households experienced severer income loss in earlier rounds, but they recovered fast in terms of the probability of further reducing labor incomes both in small and large towns (Panel E). Finally, poor households experienced severer income shocks in the early rounds, but those who live in small towns recovered fast (Panels G and H). By contrast, poor households in larger towns still had a higher probability of income loss even in round six.

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**Table 3. Estimation results of two-way FE models: Urban Ethiopia**

||Labor i|ncome|Total|income|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Round 1|0.664<sup>***</sup>|3.477<sup>***</sup>|0.654<sup>***</sup>|2.771<sup>***</sup>|
||(0.127)|(0.998)|(0.139)|(0.967)|
|Round 2|0.349<sup>***</sup>|-0.103|0.504<sup>***</sup>|0.797|
||(0.111)<br>|(1.086)|(0.159)<br>|(1.534)|
|Round 3|0.530<sup>***</sup>|2.190|0.430<sup>**</sup>|2.635|
||(0.114)|(1.412)|(0.192)|(2.292)|
|Round 4|0.366<sup>***</sup>|1.066|0.280<sup>*</sup>|0.410|
||(0.124)|(1.060)|(0.149)|(1.470)|
|Round 5|<br>0.360<sup>***</sup>|<br>2.161<sup>*</sup>|<br>0.203|<br>0.017|
||(0.121)|(1.285)|(0.143)|(1.246)|
|Round 6|0.244<sup>**</sup>|2.521<sup>***</sup>|0.153|0.271|
||(0.108)|(0.938)|(0.104)|(0.910)|
|Round 1 × ln(Population)|<br>0.006|<br>-0.267<sup>**</sup>|<br>-0.002|<br>-0.205<sup>**</sup>|
||(0.010)|(0.103)|(0.011)|(0.098)|
|Round 2 × ln(Population)|0.020<sup>**</sup>|0.116|-0.001|-0.018|
||(0.008)|(0.107)|(0.012)|(0.161)|
|Round 3 × ln(Population)|<br>0.004|<br>-0.133|<br>0.004|<br>-0.211|
||(0.008)|(0.142)|(0.015)|(0.206)|
|Round 4 × ln(Population)|0.012|-0.080|0.009|-0.057|
||(0.009)|(0.109)|(0.012)|(0.141)|
|Round 5 × ln(Population)|<br>0.012|<br>-0.136|<br>0.013|<br>0.023|
||(0.009)<br>|(0.127)<br>|(0.012)|(0.119)|
|Round 6 × ln(Population)|0.016<sup>*</sup>|-0.193<sup>**</sup>|0.011|-0.058|
||(0.008)|(0.096)|(0.008)|(0.094)|
|Round 1 × ln(Density)||-0.335<sup>***</sup>||-0.252<sup>**</sup>|
|||(0.123)||(0.118)|
|Round 2 × ln(Density)||0.050||-0.036|
|||(0.133)||(0.189)|
|Round 3 × ln(Density)||<br>-0.199||<br>-0.263|
|||(0.169)||(0.269)|
|Round 4 × ln(Density)||-0.082||-0.012|
|||(0.132)||(0.177)|
|Round 5 × ln(Density)||<br>-0.217||<br>0.023|
|||(0.152)<br>||(0.146)|
|Round 6 × ln(Density)||-0.272<sup>**</sup><br>||-0.010<br>|
|||(0.114)||(0.110)|
|Round 1 × ln(Population) × ln(Density)||<br>0.032<sup>**</sup>||<br>0.024<sup>**</sup>|
|||(0.012)||(0.011)|
|Round 2 × ln(Population) × ln(Density)||-0.011||0.002|
|||(0.012)||(0.019)|
|Round 3 × ln(Population) × ln(Density)||<br>0.016||<br>0.025|
|||(0.016)||(0.024)|
|Round 4 × ln(Population) × ln(Density)||-0.11||0.007|
|||(0.013)||(0.016)|
|Round 5 × ln(Population) × ln(Density)||<br>0.018||<br>-0.001|
|||(0.014)||(0.013)|
|Round 6 × ln(Population) × ln(Density)||0.025<sup>**</sup>||0.007|
|||(0.011)||(0.011)|
|Assistance received|0.019|<br>-0.014|-0.041|<br>-0.043|
||(0040)|(0027)|(0042)|(0043)|
|Remittances received|.<br>0.010|.<br>0.053|.<br>-0.152<sup>***</sup>|.<br>-0.148<sup>***</sup>|
||(0.054)|(0.079)|(0.049)|(0.050)|
|Household FE|Yes|Yes|Yes|Yes|
|Obs.|10982|10982|11668|11668|

_Note:_ Cluster-robust standard errors in parentheses.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. Dependent variables are dummy indicators about whether the household reduced their labor incomes (columns 1 and 2) or total incomes (columns 3 and 4) since the last survey round.

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<!-- Start of picture text --> (A) By sex (B) By skill<br>$$ OOEEE 1<br>S38 Ss<br>3S<br>8g 6 5e 6-<br>88<br>££<br>a 4 a4<br>2ol 2SS<br>1 2 3 4 5 6 1 2 3 4 5 6<br>round round<br>- @- = In_pop=10: Male = © In_pop=10: female -@- In_pop=10: high-skilled = ©. In_pop=10: low-skilled<br>—®— _In_pop=14: Male ==®= In_pop=14: female —®— _In_pop=14: high-skilled =®= In_pop=14: low-skilled<br>(C) By wage/self-employment (D) By consumption<br>1 1<br>Ss gs<br>53<br>86 8 6<br>a4 = 4<br>22<br>iI I I I i I I I I 1 1<br>1 2 3 4 5 6 1 2 3 4 5 6<br>round round<br>-@- In_pop=10: wage = ©@= — In_pop=10: self-employed -@- In_pop=10:T60 = = In_pop=10: B40<br>—®— In_pop=14: wage —=®=_ In_pop=14: self-employed —®— _In_pop=14: T60 —=®= In_pop=14: B40<br><!-- End of picture text -->
<!-- Start of picture text --> (A) Outcome trends (B) Predicted probability<br>60 6<br>5 40 i<br>In i 4<br>: i,<br>* 20<br>10 2<br>1 2 3 4 5 6 -20 -15 -10 5 0 5<br>‘Survey round Mobility change from the baseline (%)<br>—-=- <Skm —— §-10km -—@- In_dist=8 —@- = In_dist=9<br>— 10-15km —— = = 15kmor farther —@ = In_dist=10<br>(C) By sex (D) By skills<br>66<br>./ 5<br>ig iz<br>gg<br>E3E3<br>22<br>-20 -15 -10 5 0 5 -20 -15 -10 5 0 5<br>Mobility change from the baseline (2%) Mobility change from the baseline (%)<br>-—@- = In_dist=8, female=0 —4&- In_dist=8, female=1 -@- = In_dist=8, High-skilled —4- In_dist=8, Low-skilled<br>—@®— Indist=10, female=0 —4— Indist=10, female=1 —@— = In_dist=10, High-skilled —4— In_dist=10, Low-skilled<br><!-- End of picture text -->
the mobility level in Kinshasa reduces the chance of workers experiencing income decrease by 5.8 percent and 11.6 percent, respectively. Panel A in Figure 8 visually illustrates the result.

As a robustness check, we estimate the same set of specifications by restricting the sample only from the first to the fourth rounds, the period in which the mobility level continuously grew (Panel B in Figure 5). Restricting the sample for the first four rounds does not substantially change the results (columns 5 to 8 in Table 4).

We then analyze the additional heterogeneity of the income-mobility elasticities by worker characteristics, such as sex, skills, self-employment, and poverty status. The estimation results of the fixed-effects models with additional interactions with those characteristics are summarized in Table 5. We find that the labor incomes of female and/or low-skilled workers in neighborhoods that are located farther from the CBD are more elastic to mobility changes. As shown in Panel C (female workers) and Panel D (low-skilled workers) in Figure 8, the probability of their experiencing income reductions changes to a greater degree corresponding to the mobility changes.

Another analysis of Kinshasa involves the neighborhood characteristics based on their precariousness and building density. Table A4 in Appendix A summarizes the estimation results of regressions that interact the mobility index with the indicator about neighborhood characteristics. The positive coefficient estimates for the neighborhood characteristics dummies—with the precarious and high-density neighborhoods as the reference category—imply that labor incomes are more elastic to mobility changes in precarious and high-density neighborhoods. The results are, however, not clear enough to distinguish density across precarious neighborhoods due to wide standard errors in the estimates (see Figure A6 in Appendix).

**Table 4. Regression results with distance from CBD: Kinshasa**

||Whethe<br>|r the househ<br>Rounds<br>|old head di<br>1to 6<br>|d not work or<br>|decrease lab<br>|or income d<br>Round<br>|uring the las<br>s1to4<br>|t 7 days<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)<br>|(5)|(6)|(7)|(8)<br>|
|MOB|||1.527|4.908<sup>**</sup>|||1.398|4.677<sup>*</sup>|
||||(1.530)<br>|(2.453)<br>|||(1.533)<br>|(2.392)<br>|
|MOB × ln(Distance from CBD)|-0.283<sup>*</sup>|-0.385<sup>*</sup>|-0.281<sup>*</sup>|-0.387<sup>*</sup>|-0.251|-0.388<sup>*</sup>|-0.247<sup>\</sup>|-0.387<sup>*</sup>|
||(0.164)|(0.206)|(0.163)|(0.207)|(0.163)|(0.203)|(0.163)|(0.204)|
|Assistance received (private)||-0.014||-0.010||-0.013||-0.029|
|||(0.027)||(0.027)||(0.034)||(0.038)|
|Assistance received (government)||0.053||0.083||0.023||0.064|
|||(0.079)||(0.067)||(0.090)||(0.077)|
|Individual FE<br>|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Round FE|Yes|Yes|No|No|Yes|Yes|No|No|
|Round × Ind/HH characteristics|No|Yes|No|No|No|Yes|No|No|
|MOB × Ind/HH characteristics|No|No|No|Yes|No|No|No|Yes|
|Obs.|5235|5016|5235|5016|3614|3466|3614|3466|

_Note:_ Cluster-robust standard errors in parentheses.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. MOB indicates the Facebook mobility index with the mobility level in February 2020 as the baseline. Individual and household characteristics include age, sex, education, household size, dependency ratio, self-employment, industries, internet access, and consumption quintiles.

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**Table 5. Heterogeneity by worker characteristics: Kinshasa**

||Wheth|erthehous<br>Rounds|eholdhead <br>1 to 6|didnot worko|rdecreaselabo|r income du<br>Rounds|ring thelast <br>1 to 4|7 days|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|MOB|-1.953|0.789|3.777<sup>*</sup>|5.166<sup>**</sup>|-2.193|1.260|4.142<sup>*</sup>|4.968<sup>*</sup>|
||(2.743)|(2.965)|(2.119)|(2.560)|(2.819)|(2.742)|(2.164)|(2.601)|
|MOB × ln(Distance from CBD)|<br>0.236|<br>-0.038|<br>-0.377<sup>**</sup>|<br>-0.554<sup>**</sup>|<br>0.262|<br>-0.070|<br>-0.423<sup>**</sup>|<br>-0.550<sup>**</sup>|
||(0.268)|(0.292)|(0.185)|(0.251)|(0.278)|(0.275)|(0.191)|(0.258)|
|MOB × Female|7.960<sup>**</sup>||||8.533<sup>***</sup>||||
||(3.094)||||(3.245)||||
|MOB × ln(Distance from CBD) ×|<br>-0.882<sup>***</sup>||||<br>-0.922<sup>***</sup>||||
|Female|(0.322)||||(0.338)||||
|MOB × Low-skilled||5.252<sup>*</sup>||||4.599|||
|||(2.914)||||(2.874)|||
|MOB × ln(Distance from CBD) ×||<br>-0.583<sup>*</sup>||||<br>-0.529<sup>*</sup>|||
|<br>Low-skilled||(0.315)||||(0.311)|||
|MOB × Self-employed|||1.682||||0.263||
||||(2.916)||||(2.804)||
|MOB × ln(Distance from CBD) ×|||<br>-0.136||||<br>0.014||
|<br>Self-employed|||(0.309)||||(0.296)||
|MOB × Poor||||-4.856||||-5.000|
|||||(3.916)||||(3.940)|
|MOB × ln(Distance from CBD) ×||||<br>0.536||||<br>0.552|
|<br>Poor||||(0.408)||||(0.411)|
|Individual FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Round FE|No|No|No|No|No|No|No|No|
|Round × Ind/HH characteristics|No|No|No|No|No|No|No|No|
|MOB × Ind/HH characteristics|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs.|5139|5016|5016|5016|3552|3466|3466|3466|

_Note:_ Cluster-robust standard errors in parentheses.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. MOB indicates the Facebook mobility index with the mobility level in February 2020 as the baseline. Individual and household characteristics include age, sex, education, household size, dependency ratio, self-employment, industries, internet access, and consumption quintiles.

# **5. Discussion and conclusions**

This paper examines spatial heterogeneity of the impacts of the early days of the COVID-19 pandemic on urban households’ incomes in Ethiopia and Kinshasa. Drawing on new panel household surveys, the fixed-effects regression analysis for Ethiopia finds that households in large and densely populated towns were exposed to higher risk of labor income reductions at the beginning of the pandemic and their recovery was slower than other households. Disadvantaged groups, such as female, low-skilled, selfemployed, and poor workers, particularly suffered in large and densely populated towns. In the case of Kinshasa, we find that labor incomes are more elastic to the changes in mobility among workers— particularly female and low-skilled workers—who live in areas that are located farther from the city core area and/or highly dense and precarious neighborhoods.

Our analyses do not allow to distinguish specific paths in the COVID-19 impacts on urban households’ incomes. The disadvantaged groups may have been particularly vulnerable to mobility shocks, as they need to travel around for their jobs in self-employment, with no option to work remotely. Their employment may also be unstable due to the lack of written contracts and informality. In large and densely populated towns, people are less likely to travel during the pandemic in view of the high contagion risk. The disadvantaged group of workers who live far from their workplace must have been hit hard by mobility restrictions, further reducing their incomes. In the short term, it is important to provide direct support to those households in the form of, for example, cash transfers. In the medium term, it might be effective to build their resilience against shocks by improving their accessibility to jobs. This could be achieved by reducing the distance between jobs and residential locations through, among others, land use management, transport development, and/or the provision of transport subsidies to workers.

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We conclude by mentioning limitations of this study. First, relying on the Facebook mobility data has drawbacks. The data may not represent the mobility trend of urban workers and households, as it only captures the movement of Facebook users who keep the smartphone app and location information on— they are probably younger and wealthier. Moreover, the Facebook mobility data track the mobility trend only at a geographically aggregated level. Thus, we cannot differentiate within-city locations or individual characteristics. Second, the HFPS data do not capture the initial shock period between midMarch and May in Kinshasa. People who were relatively resilient against the COVID-19 shock might have already recovered by the first round of the HFPS. If the share of those workers is correlated with town population or density, our estimation would be biased. Third, the HFPS data record employment conditions of only one respondent, typically the household head. As a result, we cannot analyze other workers within the household. This limits, for example, the possibility of differentiating the estimated impacts for youth and female workers. Finally, the HFPS data do not record the exact amount of labor income of individuals and households, let alone their consumption levels. As a result, we cannot quantify the welfare impacts of the COVID-19 pandemic in our research design.

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