**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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