least some form of access to education during this period (Table A7).<sup>46</sup> Finally, additional results (Table A9) indicate that externalizing attitudes seem to be the most affected type of behavior – either favorably, by in-person education, or detrimentally, in the case of remote education.

**Table 4. Well-being** **<u>(Fixed Effects)</u>**

||(1)|(2)|(3)<br>Changesi|(4)<br>n Behavior|(5)|(6)|
|---|---|---|---|---|---|---|
|In-person|-0.099***|||-0.072*|-0.197|-0.059|
|Education|(0.038)|||(0.039)|(0.152)|(0.037)|
|Remote||0.340**||0.180|0.866***|-0.102|
|Education|||||||
|||(0.146)||(0.179)|(0.188)|(0.155)|
|Alternative|||0.206**|0.131|-0.371**|0.242***|
|Education|||||||
||||(0.096)|(0.105)|(0.185)|(0.092)|
|Engagement|-0.026|-0.017|-0.018|-0.026|0.144|-0.018|
||(0.036)|(0.037)|(0.037)|(0.036)|(0.120)|(0.041)|
|Discipline|0.437***|0.436***|0.416***|0.423***|0.516***|0.414***|
||(0.063)|(0.065)|(0.062)|(0.062)|(0.103)|(0.066)|
|Child FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Age|3+|3+|3+|3+|3-6|6+|
|R2|0.32|0.30|0.32|0.33|0.49|0.35|
|N|4715|4715|4715|4715|857|3858|

_Significance levels:_ * p<0.1, ** p<0.05, *** p<0.01. Standard errors (svy) in parentheses. Child fixed effects. Data from waves 4 and 5. Control variables: public, poor, childhunger, urban, powergrid, internet, no. children (log), anxious due to outbreak.

# 6. Conclusion

The past couple of years brought unprecedented difficulties to billions of people across the world. Children – particularly in developing countries – have had to deal with a heavy load. In Kenya, home to over 20 million children aged 14 and younger, the virus outbreak and the nine-month-long period of school closures have led to disruptions in education and child development that have potentially longlasting implications. When schools shut their gates in March 2020, few children could resume their education – even if imperfectly – with remote learning, likely increasing pre-existing inequalities, as children from poor and less educated households fell further behind.<sup>47</sup> In particular, girls living in poor households, who were less likely to adopt remote learning when needed and to go back to school after the restrictions were lifted, seem to be particularly at risk.

These early signs of increasing inequality are, to some extent, also reflected in the latest data from the Kenya Continuous Household Survey (KCHS). While school attendance rates remained largely unchanged between 2019 and 2020 for children who live in households with college-educated heads, they fell by almost two percentage points for those living with families whose head is less educated. Over the same period, a similar contrast is observed in terms of place of residence: rates significantly dropped in rural areas, but not in urban centers. Moreover, although we observe a significant reduction

> 46 Table A8 in the appendix reports results of probit regressions of changes in behavior on the first differences of in-person, remote and alternative education (in addition to the same control variables), indicating that these findings are robust to nonlinear specifications. We opt for first differences instead of fixed-effects models to avoid the problem of incidental parameters (Neyman and Scott 1948).

> 47 As mentioned previously, recent evidence indicates that, even if children do have access to virtual and hybrid education, these alternatives are still less effective than face-to-face learning (Halloran et al. 2021). The heterogeneity in terms of quality may be even larger for remote and alternative education, and this is something that we cannot account for in our analysis.

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