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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