metadata: authors: 0: author: Biscaye,Pierre E.
1: author: Egger,Dennis Timo
2: author: Pape,Utz Johann
count: Kenya
docna: 0: docna: Balancing Work and Childcare : Evidence from
            COVID-19 School Closures and Reopenings in Kenya
docty: Policy Research Working Paper
subtopic: Labor Markets,Food Security,Educational Sciences,Rural Labor Markets,Labor Standards
teratopic: Agriculture,Education,Rural Development,Social Protections and Labor
abstracts: cdata!: This paper identifies the impact of
            childcare responsibilities on adult labor supply in the
            context of COVID-19-related school closures in Kenya. It
            compares changes in parents’ labor participation after
            schools partly reopened in October 2020 for households with
            children in a grade eligible to return against households
            with children in adjacent grades. Using
            nationally-representative panel data from World Bank phone
            surveys in 2020–21, the findings show that the partial
            reopening increases affected adults’ weekly labor hours by
            22 percent, with increases concentrated in household
            agriculture. The results suggest that school closures
            account for over 30 percent of the fall in average work
            hours in the first few months after COVID-19 cases were
            detected. The effects are driven by changes in household
            childcare burdens and child agricultural labor when a
            student returns to school. The impacts are not significantly
            different by sex of the adult. Although both women and men
            increased hours spent on childcare during the pandemic,
            women benefited more than men from reductions in childcare
            needs, but took on more of the childcare burden when the
            returning student was a net childcare provider. The results
            highlight the importance of siblings in household childcare
            and suggest that policies that increase childcare
            availability and affordability could increase adult labor
            supply in Kenya.
geo_reg_key: 517191
repnme: repnme: Balancing Work and Childcare : Evidence
            from COVID-19 School Closures and Reopenings in Kenya
display_title: Balancing Work and Childcare :
            Evidence from COVID-19 School Closures and Reopenings in Kenya
listing_relative_url: /research/2022/03/33751567/balancing-work-childcare-evidence-covid-19-school-closures-reopenings-kenya
topicv3: Agriculture and Food Security m1326205 435,Working Hours m1326490 1672,Education m1327048 470,Labor Supply m1504055 2751,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2022-03-07T00:00:00Z
dois: 10.1596/1813-9450-9958
historic_topic: Agriculture,Education,Rural Development,Social Protections and Labor
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 35
author_keys: 0: author_key: NA
1: author_key: 1932783
2: author_key: 1154467
model_extractions: 9958 Policy Research Working Paper Balancing Work and Childcare Evidence from COVID-19 School Closures and Reopenings in Kenya _Pierre E. Biscaye Dennis Egger Utz J. Pape_ Poverty and Equity Global Practice March 2022
Policy Research Working Paper 9958

# **Abstract**

This paper identifies the impact of childcare responsibilities on adult labor supply in the context of COVID-19-related school closures in Kenya. It compares changes in parents’ labor participation after schools partly reopened in October 2020 for households with children in a grade eligible to return against households with children in adjacent grades. Using nationally-representative panel data from World Bank phone surveys in 2020–21, the findings show that the partial reopening increases affected adults’ weekly labor hours by 22 percent, with increases concentrated in household agriculture. The results suggest that school closures account for over 30 percent of the fall in average work hours in the first few months after COVID-19 cases were detected. The effects are driven by changes in household childcare burdens and child agricultural labor when a student returns to school. The impacts are not significantly different by sex of the adult. Although both women and men increased hours spent on childcare during the pandemic, women benefited more than men from reductions in childcare needs, but took on more of the childcare burden when the returning student was a net childcare provider. The results highlight the importance of siblings in household childcare and suggest that policies that increase childcare availability and affordability could increase adult labor supply in Kenya.

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 upape@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
Balancing Work and Childcare: Evidence from COVID-19 School Closures and Reopenings in Kenya Pierre E. Biscaye<sup>†</sup> Dennis Egger<sup>‡</sup> Utz J. Pape<sup>§</sup>

**JEL codes:** D13, H12, J13, J22, J43 **Keywords:** childcare, labor supply, Kenya, COVID-19, school closures

We thank Maximilian Auffhammer, Abdoulaye Cisse, Jose Antonio Cuesta Leiva, Madeline Duhon, Anna Frutero, Ethan Ligon, Jeremy Magruder, Aprajit Mahajan, Ted Miguel, Jed Silver, three anonymous referees, and seminar participants at UC Berkeley, UC Davis, the Busara Center for Behavioral Economics, and the All-California Labor Economics Conference for helpful comments. The IPA Women’s Work, Entrepreneurship, and Skilling (WWES) Initiative and the Department of Economics at UC Berkeley generously provided financial support. Declaration of interest: none.

> † Biscaye (Corresponding Author): Department of Agricultural and Resource Economics, University of California at Berkeley; pbiscaye@berkeley.edu.

> ‡ Egger: Department of Economics, University of California at Berkeley.

> § Pape: The World Bank and Georg-August-University Göttingen.
# **1 Introduction**

The availability and cost of childcare have been shown to significantly affect adult labor supply in high-income countries, particularly for women. But there is less evidence on this relationship in low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa (Halim, Perova, and Reynolds 2021). Yet, a historical perspective highlights the important role of women’s labor supply in driving economic development. Understanding how childcare and adult labor supply interact is therefore crucial in these settings.

Sub-Saharan African countries differ from more frequently studied high-income countries in many ways relevant to this question. Households have more children on average but also more commonly have additional adults (UN 2020). Formal early childhood care availability is increasing, but from a low base and there are concerns around quality and cost (Samman et al. 2016). Older siblings play an important role in household childcare (Jakiela et al. 2020). Female labor participation is higher in Sub-Saharan Africa than any other region, but it is concentrated in informal household activities; this region also has the world’s highest gender gap in wage employment (ILO 2017). Households are more likely to engage in family farm or non-farm enterprises, which may be more accommodating of childcare needs than wage employment. It is not clear _a priori_ how these differences would affect the relationship between childcare needs and labor participation.

An important factor influencing household childcare needs is the availability of low- or no-cost schooling. In response to the COVID-19 pandemic, countries around the world closed schools in an effort to limit virus transmission. This paper leverages pandemic school closure policies in Kenya as exogenous shocks to household childcare burdens to provide empirical estimates of the impact of childcare responsibilities on adult labor supply in an LMIC setting in Africa.

Kenya closed all schools nationwide soon after its first COVID-19 cases in March 2020 and partially reopened schools for specific grades in October 2020, prior to fully reopening in January 2021. School closures increased household childcare needs—more so for households without young children already at home due to economies of scale—creating trade-offs in adults’ time allocation across childcare, work in different sectors, and leisure. We exploit the quasi-random variation in the timing that children enrolled in different grades were eligible to return to school and use data from the nationally-representative panel Kenyan Rapid Response Phone Survey to implement a difference-in-differences analysis comparing changes in labor supply after the partial reopening for adults in ‘treatment’ households with children in grades 4 or 8—eligible to return (99% did return)—against those with children in adjacent grades.

Having a child eligible to return to school increases adults’ labor supply in the weeks after reopening. Labor supply effects of the partial reopening are concentrated on the intensive margin: total work hours in the past 7 days increase by 3.6 (22%), driven by a 26% increase in household agriculture hours. We find no effects on labor participation or on wage employment or household enterprise hours, which may take longer to adjust.<sup>1</sup> Household agriculture is likely more flexible

> 1. Analysis of longer-term impacts is complicated by the fact that schools fully reopened in January 2021, meaning our comparison group also becomes ‘treated.’

2
in allowing adults to adjust working hours after a change in the childcare burden; participation in household agriculture was also less affected by the pandemic.

Agricultural households drive average labor impacts. Poorer households—based on an index of housing and assets—increase work hours by more than wealthy ones but not significantly so. They are more likely to be engaged in agriculture and may have had fewer resources to deal with increased childcare needs during closures. We observe no differences in impacts between rural and urban locations. Relatively high participation in agriculture even in ‘urban’ areas and low participation in wage employment or take-up of paid childcare in our sample mean that key differences that could affect how changes in childcare burdens affect labor supply by location are not present in this context.

Surprisingly, the impacts are not significantly different by sex, contrasting with evidence of more negative impacts of the COVID-19 pandemic on mothers’ labor hours in high-income contexts (Alon et al. 2021; Amuedo-Dorantes et al. 2020; Collins et al. 2021; Hansen, Sabia, and Schaller 2022; Heggeness 2020) and expectations based on women’s role as primary caregivers in most Kenyan households. One reason for the lack of difference is that in our sample, both women and men contribute to childcare and increased childcare hours during school closures. A second reason is that in this setting, school-age children are both _recipients_ of childcare and _contributors_ to household productive activities, including childcare to siblings and household agriculture. We find that women’s labor supply responds relatively more to changes in childcare burdens while men respond more to changes in child agricultural labor, leading to offsetting impacts.

The effects of the partial school reopening on adult childcare and work hours vary with household composition—in particular, the presence of below-school-age children—consistent with evidence of economies of scale in childcare and of an important role of siblings in childcare (Jakiela et al. 2020). Whether the returning student is a net provider or recipient of childcare in the household determines whether the reopening is a negative or positive childcare shock. Increases in work hours are driven by households without below-school-age children, where the student’s return to school decreases adults’—and particularly women’s—childcare burdens. A second mechanism for the impacts we observe is participation of school-age-children in household agriculture. Child agriculture hours decrease in treated households by approximately one-quarter of the total increase in adult agriculture hours, suggesting some of this increase substituted for child labor during Kenya’s main harvest season.

This paper explores a new dimension of the relationship between childcare and labor supply (e.g., Browning 1992; Connelly 1992; Ribar 1992) by considering how formal childcare for schoolage children (through schooling) affects households through changes in both childcare burdens and availability of child labor. The current literature largely studies childcare for below-school-age children and treats children solely as childcare recipients, while focusing on settings dominated by wage employment. These characteristics do not generalize to many LMIC contexts. Among studies of childcare and labor supply in African LMICs (Bjorvatn et al. 2021; Clark et al. 2019; Delecourt and Fitzpatrick 2021; Heath 2017; Lokshin, Glinskaya, and Garcia 2000; Martinez, Naudeau, and 3
Pereira 2012; Quisumbing, Hallman, and Ruel 2007), causal identification is limited, only two include rural areas, and none consider the role of children as household labor providers. This paper estimates causal impacts of a change in childcare needs using a natural experiment with a nationally-representative sample of households in an African LMIC with most engaged in household farm and non-farm enterprise rather than wage work. Analyzing a shock affecting formal care provision for school-age children further allows us to shed light on the role of child household labor in the relationship between childcare needs and adult labor supply.

We also contribute to understanding labor impacts of pandemics and pandemic-related policies. A growing number of studies analyzes the gendered effects of the COVID-19 pandemic on childcare and labor supply (see e.g., Alon et al. 2021; Amuedo-Dorantes et al. 2020; Collins et al. 2021; Del Boca et al. 2020; Furman, Kearney, and Powell 2021; Grantham et al. 2021; Hansen, Sabia, and Schaller 2022; Heggeness 2020; Prados and Zamarro 2021; Zamarro and Prados 2021). Though descriptive evidence from COVID-19 in India (Chauhan 2020; Deshpande 2020) and South Africa (Casale and Posel 2020) and from Ebola in Sierra Leone and Liberia (Wenham et al. 2020), along with one causally-identified study on COVID-19 in Shaanxi province, China (Ma, Sun, and Xue 2020), suggests that women increase domestic work and reduce their labor during pandemics more than men in LMICs, causal estimates of impacts of changes to household childcare needs during a pandemic on adults’ labor supply in an LMIC are currently lacking.

The impact of partial reopening on adult work hours corresponds to over 30% of the fall in average hours after the first COVID-19 cases in Kenya. A back-of-the-envelope calculation indicates that school closures decreased work hours across Kenya by 2.1 billion in 2020—at the average hourly income in the data a cost of USD 3 billion (3.1% of 2019 GDP). More generally, we demonstrate that reducing household childcare burdens can broadly increase adults’ labor participation, and provide new evidence of the importance of siblings in household childcare in an African LMIC context.

# **2 Context and Data**

This section summarizes Kenyan COVID-19 school closure policies, the data we use to analyze their impacts on labor supply, and information on childcare arrangements.

## **2.1 Kenyan School System and COVID-19 Closure Policies**

Public primary and secondary education in Kenya is free for all children starting around age 6, and education is compulsory for the first nine years. Pre-primary education has also become broadly available for children ages 4 and 5. Though public education is nominally free, households pay for a variety of school-related costs such as materials, meals, and examinations. These costs are typically in the range of 25-75 USD per year for primary schools (Zuilkowski et al. 2018) and 100-500 USD per year for secondary schools (Bonds 2021), with higher costs for private and boarding schools. Academic years in Kenya begin in January and end in late October, and consist of three terms.

Schools in Kenya closed on March 16, 2020, days after the first reported COVID-19 cases, as 4
Students in grades 4, 8, and 12 returned for Term 3 from January-March 2021 while other students returned for Term 2; their Term 3 was shifted to May-July 2021. Grade 8 and 12 students sat national exams in March-April. 2021 Term 1 for all students began in late July 2021. Terms and breaks for the 2021-2023 academic calendars were shortened to allow a gradual return to the standard pre-pandemic term schedule (running from January-October) in time for the 2024 academic year.

We focus on the impacts of the partial school reopening for several reasons. First, unlike initial school closures, the partial reopening did not coincide with other pandemic-related policies, allowing for cleaner identification. Second, we can exploit discontinuities in the timing that children enrolled in different grades were eligible to return to school to isolate the effect of the childcare shock. Further, because households vary in whether the students eligible for the partial reopening are net suppliers or demanders of childcare—depending on the presence of younger siblings—this shock sheds light on the importance of sibling-provided childcare.

# **2.2 Data**

Data come from the Kenya COVID-19 Rapid Response Phone Survey (RRPS) panel, collected by the World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California at Berkeley (Pape 2021).<sup>4</sup> The main sample ( _∼_ 80%) is drawn from the nationallyrepresentative Kenya Integrated Household Budget Survey conducted in 2015-2016, and this sample is supplemented by random digit dialing. The sample is intended to be representative of the population of Kenya using cell phones—80% of households nationally report owning a mobile phone, and these have better socioeconomic conditions on average than households that do not (Pape et al. 2021). We use data from the first four survey rounds, covering May 2020-March 2021. In addition, we construct measures for February 2020, before the first COVID-19 cases in Kenya, using recall questions from the first round.

The outcomes of interest are measures of labor supply.<sup>5</sup> The extensive margin is measured by participation in the last 7 days in three activities: employed/wage labor, household non-farm enterprise, and household agriculture. The intensive margin is captured using hours of work by activity in the last 7 days; an individual not working in a given activity is coded as working 0 hours. The survey also includes data on total child hours spent working in household agriculture.

Information on what grades children were enrolled in prior to the initial closures allows us to identify households affected by the partial reopening. We define ‘treatment’ households as those with children enrolled in grades 4 or 8 prior to the pandemic (eligible for the partial reopening)<sup>6</sup> while ‘control’ households have children in grades 3, 5, 6, 7, or 9, but not in grade 4 or 8.<sup>7</sup> We

> 4. See Appendix D for more detail.

> 5. We use the term labor ‘supply’ to refer to equilibrium outcomes, acknowledging that individuals may have been willing to supply additional labor but faced limited demand.

> 6. Few households report any children in grade 12; we test robustness of our results to including them.

> 7. Results are robust to including households with children in grade 2 as controls and to excluding households with children in grade 9 (in secondary school) or in grade 6 (to focus only on immediately adjacent grades).

6
separate ‘mixed’ households with children in both grade groups from ‘treatment’ households as they might experience different effects when not all of their children in the relevant grade range return to school. The main analysis sample includes 335 treatment, 361 mixed, and 948 control households.

Prior to the partial reopening, 98.5% of sample households stated that their children would return to school after schools reopened, despite possible concerns about the pandemic or conditions leading some children to drop out. Close to 99% of eligible students are reported to have returned to school after October 12, 2020.<sup>8</sup> Across all grades, 97% of children enrolled prior to the pandemic closures returned to school after the full reopening in January 2021.

Finally, the data include questions on household childcare arrangements, including respondent childcare hours.<sup>9</sup>

# **2.3 Childcare Arrangements**

At least 93% of children at each age from 6-16 in the RRPS are reported to have been enrolled in school in February 2020. After the March closures these children were all home requiring care and supervision during the working day, representing a large and unexpected shock to household childcare needs. Children primarily stayed at home with a parent during the closures (Figure A1), including situations where parents were simultaneously working. Almost no households report their children spending time with childcare providers outside the home or with a maid/domestic helper at home, and this does not vary by rural/urban setting or change as pandemic restrictions were relaxed and case numbers fluctuated. Adults with schoolchildren at home will have faced trade-offs in their allocation of time across childcare, work in different sectors, and other activities given a limited time budget to accommodate increased childcare burdens.

Figure 2 Panel A presents how hours of childcare from different providers (excluding schools) vary with the number of household children, using data from after schools fully reopened.

Non-household members provide very little childcare on average—86% of households with children report 0 hours of care from non-household members in the last 7 days. While childcare availability has been increasing in Kenya (particularly in urban areas), affordability remains a challenge for most households (Clark et al. 2021; Murungi 2013).

Other adults besides the parents are present in 37% of households with children, and on average provide around 10 hours per week of childcare. Siblings also play an important role in childcare (Jakiela et al. 2020). In households with at least 2 children 55% of children provided childcare to siblings in the last 7 days, for 15-20 hours on average in total, demonstrating how childcare burdens can also fall on household children. Sibling childcare hours may have been higher during school closures as school-age children were at home, but we only measure sibling childcare after the full

> 8. This may be overstated but is consistent with data from a survey of 3,000 grade 8 students in Busia County, Kenya, which shows that 97% of these students reported back to school after the partial reopening in October (Bonds 2021).

> 9. The survey asks about time spent on childcare in the last 7 days, but does not distinguish between time actively spent caring for a child and time spent on other activities while responsible for a child. We topcode reported childcare hours at 140, or 20 hours a day.

7
Figure 2: Count of children and childcare hours in the last 7 days, by provider of care and school closure status <!-- Start of picture text --> 80 A. Providers of household childcareJanuary-March 2021 80 B. Change in respondent childcarehours with school closures<br>70 All household adults 70<br>60 60<br>During school closures, women<br>50 50<br>Total hours per child<br>Before closures, women<br>40 40<br>Respondent During school closures, men<br>30 30 After full reopening, women Before closures, men<br>After full reopening, men<br>20 All household children 20<br>10 10<br>Non-household members<br>0 0<br>1 2 3 4+ 1 2 3 4+<br>Number of household children Number of household children<br>Hours of childcare in last 7 days<br><!-- End of picture text --> The figures show mean childcare hours in the last 7 days by number of household children (ages 0-17). Panel A presents data from RRPS round 4 (January-March 2021) which asks about childcare hours for each household adult, for all children in total, and for all non-household members in total. Previous rounds only ask about childcare hours for the respondent. The hours for ’all household adults’ include the respondent’s hours. Total hours per child is the sum of all childcare hours divided by the number of children.

Panel B presents data for female (black) and male (gray) _respondent_ childcare hours before school closures (dashed lines, recall data from RRPS round 4 about January-early March 2020), during school closures (solid lines, RRPS rounds 1-3 covering May-November 2020), and after schools fully reopened (dash-dot lines, RRPS round 4 covering January-March 2021). Data on childcare hours before and during the school closures period for other care providers are not available.

# reopening.

Respondents provide 30-35 hours a week of childcare. Figure 2 Panel B shows that while female respondents provide around 10-15 hours more than men, men still contribute around 25 hours on average. This contrasts with the image of fathers in African countries as primarily providing economic support and little childcare, but is consistent with recent evidence (Clark, Cotton, and Marteleto 2015; Kah 2012). The gender gap in childcare hours per week increased during school closures—women’s hours increased by 13.4 on average compared to 9.8 for men—but the burden increased significantly for both. This pattern is similar to findings of increases in domestic work 8
during the pandemic for both men and women, but larger for women, in India (Deshpande 2020), South Africa (Casale and Posel 2020), and many higher-income countries (see e.g., Andrew et al. 2020; Del Boca et al. 2020; Farré et al. 2020; İlkkaracan and Memiş 2021). After schools fully reopened, respondent childcare hours returned to slightly below pre-pandemic levels.

Figure 2 indicates that there are significant economies of scale in childcare hours in Kenya: respondent childcare hours increase very little after the first child, with total childcare hours likely determined by the child that requires the most care. Sibling childcare provision may also contribute to these economies of scale. Households with below-school-age children might thus have been less affected by school closures as their existing childcare time allocation could absorb some additional childcare needs. Adults in these households might have even had a smaller childcare burden as school-age siblings provided more childcare while out of school. The importance of sibling childcare suggests that a student returning to school might increase rather than decrease parents’ childcare burden, in situations where they were net childcare providers while at home.

# **3 Empirical Approach**

We identify the effect of partial school reopenings through a difference-in-differences analysis comparing outcomes before and after the reopening between households with and without eligible children. We estimate regressions of the form _yiht_ are labor supply outcomes for adult (age 18-64) _i_ in household _h_ at time _t_ . _Postt_ is an indicator for observations after the partial reopening on 12 October 2020. We include observations from May-November, omitting data from after schools fully reopened. _Treath_ indicates whether all household children in grades 3-9 were eligible to return to school (treatment), none were eligible (control), or some were eligible and others not (mixed). Household fixed effects _μh_ absorb time invariant characteristics of households which may affect labor supply outcomes. County-by-month fixed effects control for common shocks affecting households across locations and over time. Finally, _Xiht_ is a vector of individual- and household-level controls. We include individual sex, age, and household head status, the number of adults, young children (age 0-4), and school-age children (5-17) in the household (as household composition sometimes varies across rounds), and household dummies for engagement in agriculture and engagement in enterprise. We cluster standard errors at the household level.

We exploit quasi-random discontinuities in which households are affected by the partial reopening by restricting our control group to households with children in grades adjacent to those eligible to return to school. Identification is based on the argument that unobserved factors that could affect outcomes are continuous around the thresholds of children being in adjacent grades of school. Respondent and household characteristics and labor participation are similar for treatment and control households during the full school closures period (Table A1). Mixed households look different 9
in terms of household composition by construction as they must have one additional child on average.We focus our analysis on the comparison between control and treatment households. Though we also test for impacts of partial reopening mixed households relative to control, differences in household composition may affect impacts of the partial reopening.<sup>10</sup> Mean work hours trend almost identically for adults in treatment and control households from February until the partial school reopening in October, and differences after the partial reopening are eliminated after schools fully reopen in January—when all households become ‘treated’ (Figure A2). Figure 3 shows further evidence of parallel trends in labor participation on the intensive and extensive margins while schools were closed for both women and men. There are no significant impacts of being in a treatment household in the periods prior to the partial reopening while schools were fully closed for women or men, and there is no evidence of anticipation effects in the period from September to October 11.

Our main analyses pool women and men as both contribute to childcare and increased childcare hours during school closures in our sample, though we also test for different impacts by sex, as well as by particular household characteristics.

# **4 Results**

Table 1 presents results for the impacts of partial reopening on labor supply by activity at the individual level. Just 59% of adults ages 18-64 among control households were working in the last 7 days during the school closures period. Mean work hours of 16.4 reflect this large share of adults not working. Work is concentrated in household agriculture, despite 46% of the sample being classified as ‘urban’—urban locations include city peripheries, where agriculture remains common. Labor supply does not change in control households after schools partly reopen. This suggests that general labor conditions were not changing, consistent with no major simultaneous pandemic policy changes.

We find no effects of treatment on the extensive margin of labor supply but a large impact on the intensive margin. Work hours in the last 7 days increase by 3.6 (23.2%) relative to adults in control households before schools reopened, driven by a 26.0% increase in household agriculture hours. Greater impacts on household agricultural hours than in wage work or in work participation are not surprising given that we estimate short-term impacts in the weeks following the partial reopening, and household agricultural work is likely more flexible. Changing wage work participation is dependent on employers so may be more constrained in the short term. Increased agricultural work may also be a response to reduced child labor.

Non-significant impacts on household agriculture engagement indicate hours increased primarily among those already engaged in agriculture during school closures. Household agriculture engagement was likely affected less by school closures and other pandemic restrictions than other work activities. Adults may have been more likely to pause their engagement in enterprise—more ex-

> 10. Results are robust to dropping mixed households from the analysis.

10
Figure 3: Impact of treatment on labor participation in the last 7 days, by time period <!-- Start of picture text --> A. Women<br>.2 Schools closed Schools partlyreopen 10 Schools closed Schools partlyreopen<br>.15<br>5<br>.1<br>.05<br>0<br>0<br>-.05<br>-5<br>-.1 Pre-reopening Pre-reopening<br>joint significance joint significance<br>-.15 F-stat=.64, p=.527 F-stat=.39, p=.674<br>-10<br>May-June Sep-Oct 11 Oct 12-Nov May-June Sep-Oct 11 Oct 12-Nov<br>B. Men<br>.2 Schools closed Schools partlyreopen 10 Schools closed Schools partlyreopen<br>.15<br>5<br>.1<br>.05<br>0<br>0<br>-.05<br>-5<br>-.1 Pre-reopening Pre-reopening<br>joint significance joint significance<br>-.15 F-stat=.1, p=.906 -10 F-stat=.05, p=.95<br>May-June Sep-Oct 11 Oct 12-Nov May-June Sep-Oct 11 Oct 12-Nov<br>Any work, last 7 days<br>Working hours, last 7 days<br>Any work, last 7 days<br>Working hours, last 7 days<br><!-- End of picture text --> The figures show estimated coefficients and 95% confidence intervals for the interaction between _Treat_ and time period from Equation 1 for treatment households, where _Post_ is replaced with time period dummies, separately for women (panel A) and men (panel B). Outcomes are any work engagement (left) and total work hours (right) in the 7 days prior to the interview. Treatment households have a child enrolled in grades 4 or 8, and control households have a child enrolled in grades 3, 5, 6, 7, or 9. We do not show coefficients for mixed households with children in both grade groups. The reference period is July-August, while schools were closed and before the partial reopening was announced. The red bars indicate the timing of Kenya’s partial school reopening.

posed to infections and pandemic restrictions as well as potentially more challenging to combine with childcare—and slower to resume these activities.

‘Mixed’ households with children eligible to return to school as well as children in adjacent grades experience no labor supply impacts of the partial reopening. This is not surprising given what we observe about economies of scale in childcare hours in Kenya: one child returning while another of a similar age stays home is unlikely to decrease adult childcare hours in this context.

Impacts across treatment households are driven more by households with a grade 8 student eligible to return than those with a grade 4 student (Table A3). Though grade 4 children likely require more care than grade 8 children, they also likely contribute less to household agriculture and are more likely to have young siblings, mechanisms we explore in subsection 4.2. Impacts on work hours are smaller in magnitude if we expand our treatment definition to include grade 12 students 11
Table 1: Impacts of partial school reopening on adult labor supply

||N|Control Mean<br>(SD)|Post<br>(SE)|Post x Treat<br>(SE)|Post x Mixed<br>(SE)|
|---|---|---|---|---|---|
|Engaged in any work in last 7 days|8538|0.587|-0.003|0.041|0.030|
|||(0.492)|(0.031)|(0.026)|(0.026)|
|Engaged in wage employment in last 7 days|8538|0.062|-0.006|0.011|-0.006|
|||(0.241)|(0.018)|(0.013)|(0.013)|
|Engaged in HH agriculture in last 7 days|8538|0.510|0.015|0.037|0.006|
|||(0.500)|(0.027)|(0.027)|(0.023)|
|Engaged in HH non-ag enterprise in last 7|8538|0.072|-0.007|0.015|0.019|
|days||(0.259)|(0.019)|(0.016)|(0.015)|
|Total work hours, last 7 days|8538|16.434|0.074|3.630<sup>_∗∗∗_</sup>|-0.569|
|||(20.027)|(1.806)|(1.365)|(1.413)|
|Wage hours, last 7 days|8538|1.986|0.053|0.395|-0.367|
|||(9.374)|(0.731)|(0.565)|(0.582)|
|Ag hours, last 7 days|8538|11.895|0.575|3.090<sup>_∗∗∗_</sup>|-0.564|
|||(15.403)|(1.362)|(1.091)|(1.125)|
|Enterprise hours, last 7 days|8538|2.434|-0.178|0.281|0.036|
|||(10.023)|(0.681)|(0.625)|(0.630)|

This table presents estimates of Equation 1 for individual labor supply. Individuals not working in a given sector are coded as working 0 hours. From left to right, the columns show the dependent variable, number of observations, the control mean prior to the partial reopening, and the impacts of being in the partial reopening period for control households (Post), treatment households (Post x Treat), and mixed households (Post x Mixed). Control households have a child in grades 3, 5, 6, 7, or 9, treatment households have a child in grades 4 or 8, and mixed households have both. ’Post’ is a dummy for being observed on or after the partial school reopening on October 12. Regressions include household and county by month fixed effects and controls for household and individual characteristics. Standard errors are clustered at the household level. Data include observations for adults age 18-64 from May to November 2020. Significant treatment impacts on total and agricultural work hours are robust to multiple testing adjustment using FDR q-values. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 also eligible to return to school (Table A3). This result is not surprising as grade 12 students are net providers of household childcare and increase rather than decrease childcare burdens when returning to school.

We conduct a variety of robustness tests (Table A2). Results are unchanged when using individual rather than household fixed effects, when focusing on sub-samples of adults more likely to be parent caregivers and engaged in work, and when defining _Post_ not by the date schools reopened but by the date the potential reopening was announced.

# **4.1 Heterogeneity**

We test for heterogeneity in impacts by adult sex and different household characteristics by estimating Equation 1 and fully interacting a characteristic _Z_ with all right-hand side variables other than the household fixed effects, focusing on total working hours (Table 2).<sup>11</sup> Figure 4 displays estimated effects from regressions for sub-samples with particular characteristics.

Impacts of schools reopening on work hours are not significantly different for women (54% of the sample) relative to men. This contrasts with evidence from high-income countries during the pandemic (e.g., Alon et al. (2021) and Collins et al. (2021)), which consistently report larger effects

> 11. Results are very similar if we consider agricultural hours (Table A4). We note any exceptions in our discussion below.

12
Table 2: Heterogeneity in impacts of partial school reopening on working hours by individual/household characteristics

|Interaction term _Z_|(1)<br>Female|(2)<br>Ag HH|(3)<br>Urban|(4)<br>Large<br>Urban|(5)<br>Above<br>Mean<br>Wealth|(6)<br>Any Kids<br>Aged 0-4|(7)<br>>2 HH<br>Adults|
|---|---|---|---|---|---|---|---|
|Post|-1.516|-1.047|0.994|0.994|-1.319|-1.322|-4.799<sup>_∗_</sup>|
||(2.330)|(2.222)|(3.214)|(3.227)|(2.644)|(2.737)|(2.710)|
|Post _×_ Treat|3.793<sup>_∗∗_</sup>|1.756|3.337|3.337|6.095<sup>_∗∗_</sup>|5.035<sup>_∗∗∗_</sup>|2.598|
||(1.785)|(1.820)|(2.127)|(2.136)|(2.590)|(1.945)|(2.086)|
|Post _×_ Z=1|1.570|-0.591|-1.100|0.698|1.056|-1.526|6.723<sup>_∗_</sup>|
||(1.739)|(3.738)|(4.117)|(5.162)|(3.705)|(3.696)|(3.732)|
|Post _×_ Treat _×_ Z=1|-0.687|5.485<sup>_∗_</sup>|-0.280|-1.238|-4.209|-3.376|1.065|
||(1.629)|(3.204)|(2.940)|(4.108)|(3.108)|(3.080)|(2.858)|
|Observations|8538|8538|8538|5172|8538|8538|8538|
|Mean, pre-reopen control|16.475|16.475|16.475|16.475|16.475|16.475|16.475|

This table presents estimates of Equation 1 but interacting a characteristic _Z_ with all right-hand side variables except the household fixed effects. The column label indicates which characteristic _Z_ is being used. Values for household characteristics are from the first time they are observed in the data. ‘Large Urban’ is a dummy for location in one of Kenya’s largest urban areas (Nairobi, Mombasa, Nakuru, Kisumu, Kiambu) relative to any rural area, while ‘Urban’ is a dummy for location in any urban area. ‘Above Mean Wealth’ is a dummy for whether and index of household wealth, based on housing and asset ownership, is above the sample mean. The dependent variable is total working hours over the last 7 days, with individuals not working coded as working 0 hours. Observations include data from May to November 2020, and include treatment households with children in grades 4 or 8 (indicated by ’Treat’), control households with children in an adjacent grade, and ‘mixed’ households with both (results not shown). ’Post’ is a dummy for being observed on or after the partial school reopening on October 12. Regressions include household and county by month fixed effects, and additional household and individual controls. SEs clustered at household level. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 of the pandemic on mothers’ labor supply relative to fathers’ and other women’s, pointing to school closures as the mechanism. But this result aligns with the data on childcare hours in Kenya: responsibilities prior to the pandemic were less gendered than expected prior to the pandemic and both women’s and men’s hours increase by over one-third during school closures, though women’s hours increase by more in absolute terms. A childcare shock in this context could thus affect both parents’ labor participation. Different mechanisms may also underlie the similar increases for women and men, which we discuss further in subsection 4.2.

The impact of partial reopening on work hours is over four times as large for adults in agricultural households (61%), defined as households with any agricultural activity. This is consistent with effects on total hours driven largely by household agriculture.<sup>12</sup> Adults in non-agricultural households do not significantly increase work hours, likely reflecting constraints on increase wage or enterprise labor supply in the short term.

We observe no differences in impacts between urban (46%) and rural households. The definition of ‘urban’ in the data includes many peri-urban areas; over 35% of household classified as urban 12. The difference is larger and significant when considering impacts on agricultural hours (Table A4).

13
Figure 4: Heterogeneity in impacts of partial school reopening on adult work hours <!-- Start of picture text --> All<br>Women<br>Men<br>No HH agriculture<br>}*<br>Any HH agriculture<br>Rural<br>Urban<br>Below mean wealth<br>Above mean wealth<br>No child age 0-4<br>Any child age 0-4<br>1-2 HH adults<br>3+ HH adults<br>-5 0 5 10 15<br><!-- End of picture text --> The figure summarizes estimated coefficients and 95% confidence intervals for the effect of _Post ∗ Treat_ from Equation 1 for sub-samples with specified characteristics. Only coefficients for treatment households are shown. The outcome is total work hours in the 7 days prior to the interview. Results are reported in Table 2. Brackets indicate significant differences between pairs of characteristics. Data include observations from May-November 2020. Household characteristics are from the first time they are observed. Wealth is measured by an index based on housing and asset ownership.

are engaged in agriculture.<sup>13</sup> The sample may also not be representative of all rural households with schoolchildren: sample households must have a mobile phone and are on average better off than the population (Pape et al. 2021). This may further blur the urban/rural distinction in these data. Low take-up of formal childcare services and low wage employment in the data may also limit heterogeneity by urban location.

Adults in poor households increase work hours by more than those in wealthy households, measuring wealth by an index based on housing and asset ownership, though the difference is not significant. Below-mean-wealth households in the analysis sample are around 30 percent more likely to engage in agriculture than wealthier households and so may have been better able to increase work in the short term after the partial reopening, but differences in agricultural hours do not fully explain the difference (Table A4). Wealthier households may also have had resources to better absorb increased childcare burdens during school closures and thus been less affected by the reopening.

We also consider differences by household composition. Households with below-school-age chil13. The difference remains insignificant when restricting urban households to those in counties with the largest cities in Kenya (Column 4), though even there 25% of ‘urban’ households engage in agriculture.

14
dren aged 0-4 (41%) likely face _increased_ childcare burdens when an older sibling returns to school as the role of siblings in childcare may be particularly important in the context of high needs of very young children, while households without below-school-age children may benefit from reduced childcare needs after the partial reopening. Indeed, we observe that the increase in work hours is 3 times larger and only significant in households with no children ages 0-4, consistent with differences in the nature of the childcare shock with reopening by household composition.

Changes in work hours do not differ by whether the household has more than two adults (45%), perhaps due to two competing mechanisms. Households with more adults may have spread out the increased childcare burden during school closures more than households with 1-2 adults. Conversely, they would also better absorb any reduction sibling-provided childcare after the reopening. The competing mechanisms may explain the lack of significant difference.

Different types of work may also be more or less affected by a childcare shock; we consider differences in impacts by individual work participation by sector prior to the pandemic and during the school closures period (Table A5). Increased work hours are driven by adults that were engaged in agriculture in February 2020, consistent with overall impacts driven by agriculture hours. Similarly, works hours only increase after the partial reopening for treated adults that were working at some point during the school closures, reflecting how impacts are concentrated on the intensive rather than the extensive margin of labor supply. Treated adults working for a wage or in household enterprise during school closures do not increase hours after the partial reopening, which again may reflect constraints in increasing hours in these activities in the weeks we observe after reopening.

# **4.2 Mechanisms**

Adults must allocate their limited time across work in different sectors, childcare, other activities, and leisure. Changes in household childcare burdens after the partial school reopening may therefore affect adults’ time allocation. The differences in impacts by household composition indicate that positive labor supply effects of the partial reopening are driven by households for whom the childcare shock was likely to be positive. As a proxy for childcare burdens, we directly test for impacts of the partial school reopening on childcare hours using Equation 1, though childcare data are only available for respondents in these survey rounds so we cannot fully capture household-level changes.

Table 3 column 1 shows that respondent childcare hours in the last 7 days do not change significantly for treatment households after the partial school reopening. This is not surprising as most households have multiple children and we observe significant economies of scale in household childcare hours by number of children. Point estimates are negative for treated households with a grade 4 student and positive and larger for the return of older students (Table A3), consistent with older students more likely being net providers of childcare on average. The average impact of treatment on childcare is negative but not significantly different for female respondents.<sup>14</sup> Household children provide 15-20 hours of childcare per week to their siblings in the period after

> 14. The positive point estimate on _Post_ for women may be due to increased burden of childcare on women during the harvest period.

15
Table 3: Impacts of partial school reopening on respondent childcare hours and child agricultural labor

|||Re|spondent|Childcare H|ours||C<br>Ag.|hild<br> Hours|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Post|0.253|6.414|-6.786|-10.657|1.995|5.817|0.829|0.744|
||(6.443)|(9.764)|(9.798)|(13.736)|(12.157)|(17.687)|(1.084)|(2.862)|
|Post _×_ Treat|0.461|-0.575|-5.972|-11.648|-3.170|-6.997|-1.480<sup>_∗∗_</sup>|-2.635|
||(5.032)|(6.922)|(6.960)|(10.601)|(9.311)|(16.262)|(0.739)|(1.758)|
|Post _×_ Any child age 0-4|||17.426<br>(13.770)|49.104<sup>_∗∗_</sup><br>(23.043)|||||
|Post _×_ Treat _×_ Any child age 0-4|||8.394<br>(11.204)|39.520<sup>_∗∗_</sup><br>(19.482)|||||
|Post _×_ Any child age 0-8|||||0.670|5.050||-0.636|
||||||(14.686)|(23.237)||(3.125)|
|Post _×_ Treat _×_ Any child age 0-8|||||5.729<br>(11.563)|9.185<br>(18.999)||1.715<br>(1.992)|
|Observations|3073|1722|3073|1722|3073|1722|3077|3077|
|Control Mean|52.743|59.905|52.743|59.905|52.743|59.905|3.848|3.848|
|Adult Sex|Both|Women|Both|Women|Both|Women|-|-|

This table presents estimates of Equation 1 for respondent childcare hours (columns 1-6) and total household child agriculture hours (columns 7-8). Dependent variables are defined over the last 7 days. Childcare hours are not measured for household adults besides the respondent in these survey rounds. Households not engaged in agriculture are coded as having 0 child agriculture hours. Observations include data from May to November 2020, and include treatment households with children in grades 4 or 8 (indicated by ’Treat’), control households with children in an adjacent grade, and ‘mixed’ households with both (results not shown). ’Post’ is a dummy for being observed on or after the partial school reopening on October 12. Regressions include household and county by month fixed effects, and additional household and individual controls. SEs clustered at household level. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 schools fully reopen (Figure 2) and likely provided more during school closures. Though childcare needs could fall after the partial reopening in households without younger children in the home still requiring large amounts of care, if the sibling returning to school was a net provider of childcare this could increase adult childcare hours. Given what we observe about the role of siblings and economies of scale in childcare in the sample, children in grades 4 and 8 (typically around age 9 and 13) are likely net providers of childcare when home from school in households with younger children. Their return to school would thus _increase_ the childcare burden on adults. In households without younger children, the return to school of these students would instead decrease that burden.

Columns 3-6 of Table 3 support this, showing increased childcare hours for treated adults with young children and decreases for those without.<sup>15</sup> Differences are more pronounced by the presence of below-school-age children (ages 0-4) than by the presence of younger children more generally (ages 0-8), consistent with much higher childcare needs of very young children.

Labor supply effects of the partial school reopening by presence of below-school-age children are consistent with these childcare differences: positive impacts of the partial school reopening are

> 15. Control adults—particularly women—with young children also increase childcare hours following reopening. We suspect this reflects a general household reallocation of childcare responsibilities toward women during the harvest period which coincides with the reopening.

16
driven by households without below-school-age children (Table 2). Adults increase their work hours when the partial reopening constitutes a positive childcare shock (the student returning to school is a net childcare recipient) but not when it is a negative shock (the student is a net childcare provider). These results highlight the importance of sibling-provided childcare and the role of childcare in explaining labor supply impacts of the reopening.

Results indicate that childcare may only be particularly gendered for care of the youngest children. Women in treatment households with children ages 0-4 increase childcare by 39 hours more than women without after the partial reopening in households, a much larger difference than for men. Positive impacts of treatment among households without young children are driven more by women, suggesting the childcare mechanism is more important for women, but limited reductions in childcare hours on average may help explain the lack of significant difference in impacts of the partial school reopening by sex.

School reopenings may also affect adult work hours through the loss of child labor, particularly as the timing coincides with the main harvest season for most households in Kenya. Nearly 40% of agricultural households with children in grades 3 through 9 report that their children contribute to household agriculture.<sup>16</sup> Children combined to work an average of 4.6 hours per week on household farms during the school closures period across all households engaged in agriculture, around 9% of total household agriculture hours. This increases to 18.1 hours per week (28.6% of the total) for farm households with non-0 child agricultural hours. Part of treatment adults’ increase in agriculture hours after partial reopening may therefore be driven by the need to make up for reduced child labor on the farm when a child returns to school.

Estimated impacts of schools reopening on child agricultural labor are negative and significant for treatment households (Table 3 Columns 7-8). A reduction of 1.48 hours suggests that students are not able to contribute as many hours when they return to school. Reductions are larger in households without other young children to make up for the lost child labor. For a two-parent household, the reduction in child labor represents 23.9% of the increase in adult work hours on the household farm. Substitution for lost child agricultural labor could thus explain part but not all of the impacts on adult work hours we observe.

This mechanism could also help explain similar impacts of the partial reopening for women and men: the impact of partial reopening on agricultural hours is larger for men than for women (Table A4). As women are more responsive to the change in childcare burdens and men to the change in child agricultural labor, these combined mechanisms could lead to similar impacts of the reopening for both women and men. Child labor could also contribute to the significantly different impacts by household wealth: poor households engaged in agriculture are 20.8% more likely to report child labor than more wealthy households.

Finally, though there were no additional fees incurred when schools reopened, adults may have also increased labor supply to generate income to pay for materials, meals, and extra lessons. Such

> 16. This appears consistent with Moyi (2011) who reports that around 30% of Kenyan children ages 6-14 engage in farm work (often alongside attending school). Child farm labor may be underreported but is not correlated with treatment prior to the partial reopening (Table A1).

17
costs would be higher for students in grade 12 than in grades 4 or 8. Estimated impacts of the partial reopening are smaller when including households with grade 12 children in the sample (Table A3). Given expected differences in school-related costs, net childcare burden, and child agricultural labor by student grade, smaller increases in parent work hours for a grade 12 student returning to school relative to a younger student suggest childcare burdens are the main mechanism, rather than schoolrelated costs and child agricultural labor.

# **5 Discussion**

How do the labor supply effects of the positive shock of partial school reopenings compare to the initial labor supply reductions at the onset of the pandemic in Kenya? Using recall data on respondents’ labor supply in February 2020, we find that labor participation across adults ages 18-64 in Kenya fell from 76% before the pandemic to 59% in May-July.Average working hours in the last 7 days also fell, from 23.9 hours in February to 16.9 hours in May-July.<sup>17</sup> For respondents ages 18-64 in our analysis sample households, average weekly hours fell from 30.4 in February to 19.3 in May-July. Adult work hours in the last 7 days increased by 3.6 after the partial reopening, corresponding to 32.4% of the pandemic reduction in work hours in this sample.

The partial reopening only affected a subset of children older than those with the greatest childcare needs, whereas initial closures affected all school-age children. Reducing labor supply is also likely easier than increasing it, reflected in the lack of significant impacts of the partial reopening outside of household agriculture hours. Considering impacts of the partial school reopening should thus provide a conservative estimate of the contribution of school closures to initial pandemic labor participation decreases.

Increasing work hours for adults in households with school-age children (66.4% of households) during the school closure period by our estimate of the amount they increased due to the partial reopening reduces the drop in average work hours from February to May-July among all adults nationally from 7.0 hours to 4.8 hours. We therefore estimate that school closures account for (at least) 30% of the pandemic decrease in work hours.

Across Kenya’s labor force of 23.7 million (ILO 2021), a reduction of 2.2 work hours per week over the period of the school closures adds up to over 2.1 billion hours, or USD 2.96 billion at the average hourly income observed in our sample—3.1% of Kenya’s 2019 GDP. This is a simplified back-of-the-envelope calculation making many assumptions and focusing just on labor supply of adults in the household, but provides a likely conservative rough estimate of the magnitude of the labor supply impact of Kenya’s school closures.

A better understanding of the labor supply impacts of school closures may inform discussion of school closures as a policy response to a resurgent COVID-19 or a future pandemic.

Although the childcare shock we analyze takes place in the context of a global pandemic, the results on labor supply impacts will continue to have relevance as the pandemic is unfortunately

> 17. Among those working, hours fell from 39.7 to 29.2.

18
unlikely to be completely overcome in many countries in the immediate future. For example, after fully reopening schools in January 2021, Kenya closed them again in late March after a spike in COVID-19 cases before reopening again in mid-May. Further, although some pandemic-related restrictions were still in effect at the time schools partly reopened in Kenya in October 2020, many had been relaxed, so the estimated impacts of a childcare shock may generalize to similar settings with some ongoing COVID-19 caseloads and basic government policies around public health and safety—likely to be the new normal moving forward.

# **6 Conclusion**

We present some of the first nationally-representative results for the impacts of childcare on labor supply in an African or LMIC setting, using pandemic-related school closure policy changes as exogenous childcare shocks. Having a child eligible to return to school increases adult labor supply in the weeks after schools partially reopen, particularly in household agriculture hours. Increases are driven by less wealthy households who are more engaged in agriculture and may have been less able to cope with the shock of schools closing. Based on our results we estimate that school closures account for at least 30% of the decrease in average work hours observed during the first few months after the first COVID-19 cases in Kenya and cost the economy 2.3 billion USD.

Unlike studies of pandemic school closures in high-income countries, impacts of the partial school reopening in Kenya are not concentrated primarily among women. Men in our sample contribute a large amount of time to childcare (though less than women), and may also be responding to changes in child agricultural labor. In the many households where eligible students were likely net childcare providers, women bear more of the increased burden when they return to school, preventing increases in labor supply experienced by women in households where the reopening was a positive childcare shock. One student returning to school—particularly older students who may also provide household childcare—thus may not sufficiently reduce childcare needs on average for gendered differences to emerge. Some studies of changes in childcare availability or cost in Africa report significant impacts for men as well as women (Bjorvatn et al. 2021; Martinez, Naudeau, and Pereira 2012), but others focus exclusively on women (Clark et al. 2019; Lokshin, Glinskaya, and Garcia 2000; Quisumbing, Hallman, and Ruel 2007). Better data on how childcare burdens are allocated across all household members and other providers would help to shed light on the intra-household distributional impacts of childcare shocks in this context.

Our study generates three main policy-relevant takeaways. First, parents in Kenya appear to have limited feasible options for dealing with an increase in their childcare burden beyond reducing their work hours or combining work and childcare. This is true despite many households having additional adults, many parents being engaged in potentially more flexible household farm and nonfarm enterprises, and adults working just 24 hours a week on average (less than ‘full-time’) before the pandemic.

Second, the cost of childcare may be a greater barrier than availability in this context. Older 19
siblings are an important source of (unpaid) childcare, and the results suggest that the partial school reopening increased the childcare burdens for parents with younger children when sibling caregivers returned to school.This indicates that either households lack alternative childcare options or their cost is more than adults could earn by working instead of caring for children themselves. Sample households report almost no childcare provision by non-household members, and several studies point to high costs as a main constraint to Kenyan parents in using formal childcare centers, and advocate for public subsidies to facilitate access (Clark et al. 2021; Murungi 2013). Policies aiming to increase availability of childcare may therefore be less effective if they are not complemented by policies to reduce cost.

Third, the timing of when children are in school affects some households through a reduction in child agricultural labor: treatment adults increase agriculture hours after the partial school reopening partly to make up for reduced child farm labor. Kenya’s pandemic school closures pushed back two academic terms from 2020 to 2021, and efforts to gradually shift back toward the prepandemic academic calendar over 2021-2023 will change the timing of school terms and breaks relative to the agricultural cycle. This will affect whether children are in school during the main October-November harvest period in Kenya, with implications for households that rely partly on child labor. Given the role of children in agricultural household production, future work could consider how these changes affect parents’ labor allocation, children’s school attendance, demand for hired farm labor, and other household production decisions.

The results have relevance to other policies affecting household childcare burdens. If we expect that childcare needs are decreasing in child age, we would expect the impacts on labor supply we observe to be lower bounds on the impact of a policy that gives households access to free fullday childcare for young children during the working week (as schools implicitly provide for older children). Clark et al. (2019) show that subsidies for childcare centers increase labor supply for women in an informal settlement in Nairobi. Our results indicate such policies could have positive effects outside urban settings and also for men. Women could particularly benefit from policies that allow them to maintain labor market attachment while children are very young. Older children might also benefit from reduced need to care for younger siblings.

Finally, the large magnitude of the labor supply effects of the partial school reopening highlights another point that is often underappreciated: universal primary school (now close to reality across the globe), pre-school, and other forms of childcare may play a substantial role in increasing adult labor supply and promoting economic growth, and are a key component of development.

20