metadata: authors: 0: author: Xu,Yuanwei
1: author: Delius,Antonia Johanna Sophie
2: author: Pape,Utz Johann
count: Kenya
docna: 0: docna: Gender Differences in Household Coping
            Strategies for COVID-19 in Kenya
docty: Policy Research Working Paper
subtopic: Gender and Development,Economics and Gender,Gender and Poverty,Gender and Economics,Gender and Economic Policy,Inequality,Livestock and Animal Husbandry
teratopic: Macroeconomics and Economic Growth,Gender,Poverty Reduction,Agriculture
abstracts: cdata!: Understanding how different households
            cope with COVID-19 among a vulnerable population is
            important for the policy design aiming at relieving hunger
            and poverty in a low income setting. This paper uses
            original household data from five waves of a phone survey
            conducted between May 2020 and June 2021 in Kenya (sample
            size 31,715) and investigates the gender differences in
            household coping strategies during the COVID-19 shock. It
            finds that female-headed households are less likely to cope
            by selling assets or taking loans, compared with male-headed
            households. Instead, femaleheaded households rely more on
            social networks to cope. No difference in coping by reducing
            meals is observed across these two types of households. This
            paper documents that the reasons behind the gender
            difference include that female-headed households are poorer,
            and they are more likely to rely on friends and family to
            cope with shocks even prior to the COVID-19 shock. The
            findings suggest that widowed and divorced women are in high
            need of relief programs, and governments should provide
            easily accessible loans to avoid negative impacts in the
            long term from households selling assets.
geo_reg_key: 517191
repnme: repnme: Gender Differences in Household Coping
            Strategies for COVID-19 in Kenya
display_title: Gender Differences in Household
            Coping Strategies for COVID-19 in Kenya
listing_relative_url: /research/2022/03/33751574/gender-differences-household-coping-strategies-covid-19-kenya
topicv3: Female-Headed Households m1326228 916,Poverty m1326687 695,Gender m1327621 615,Livestock m1504490 4024,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2022-03-07T00:00:00Z
dois: 10.1596/1813-9450-9959
historic_topic: Macroeconomics and Economic Growth,Gender,Poverty Reduction,Agriculture
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 28
author_keys: 0: author_key: 2025133
1: author_key: 1897059
2: author_key: 1154467
model_extractions: Policy Research Working Paper 9959 Gender Differences in Household Coping Strategies for COVID-19 in Kenya _Yuanwei Xu Antonia Delius Utz Pape_ Poverty and Equity Global Practice March 2022
Policy Research Working Paper 9959

# **Abstract**

Understanding how different households cope with COVID-19 among a vulnerable population is important for the policy design aiming at relieving hunger and poverty in a low income setting. This paper uses original household data from five waves of a phone survey conducted between May 2020 and June 2021 in Kenya (sample size 31,715) and investigates the gender differences in household coping strategies during the COVID-19 shock. It finds that femaleheaded households are less likely to cope by selling assets or taking loans, compared with male-headed households. Instead, femaleheaded households rely more on social networks to cope. No difference in coping by reducing meals is observed across these two types of households. This paper documents that the reasons behind the gender difference include that female-headed households are poorer, and they are more likely to rely on friends and family to cope with shocks even prior to the COVID-19 shock. The findings suggest that widowed and divorced women are in high need of relief programs, and governments should provide easily accessible loans to avoid negative impacts in the long term from households selling assets.

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 yuanweii.xu@gmail.com, adelius@worldbank.org, and 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
# Gender Differences in Household Coping Strategies for COVID-19 in Kenya

Yuanwei Xu<sup>*</sup> , Antonia Delius<sup>†</sup> , Utz Pape<sup>‡</sup>

**JEL-Codes** : I31 (General Welfare, Well-Being), J16 (Economics of Gender), Z13 (Economic Sociology, Economic Anthropology, Social and Economic Stratification ) **Keywords** : Coping Strategy, Poverty, Social Network, COVID-19

> *Xu: Leibniz University Hannover, Ruhr University Bochum, and the World Bank. Email: yuanweii.xu@gmail.com.

> †The World Bank. Email: adelius@worldbank.org.

> ‡The World Bank and Georg-August-Universit ̈at G ̈ottingen. Email: upape@worldbank.org.
# **1 Introduction**

The spread of COVID-19 has caused huge negative shocks on the economy (Altig et al., 2020), households (Hanspal, Weber and Wohlfart, 2020; Kansiime et al., 2021), as well as businesses (Bartik et al., 2020; Meyer, Prescott and Sheng, 2021) across the globe. Apart from the sharp increase in poverty and inequality (Han, Meyer and Sullivan, 2020; Deaton, 2021), other negative impacts have been documented, including rising armed conflict (Bloem and Salemi, 2021; Ide, 2021), inter-personal violence (Evans, Lindauer and Farrell, 2020; Ag ̈uero, 2021), and elevated mental stress (Pfefferbaum and North, 2020). Several studies have pointed out that the impact of COVID-19 varies across different For households and countries are socio-demographic groups. example, poor more vulnerable than richer ones (Schellekens and Sourrouille, 2020; Mena et al., 2021), as these groups do not have as much resources to cope with such a profound negative shock. Moreover, the current COVID-19 crisis disproportionately affected women compared to men (Alon et al., 2020). Women are more likely to lose employment or income (Etheridge and Spantig, 2020; Kugler et al., 2021; Dang and Nguyen, 2021; Xue and McMunn, 2021), which is due to the fact that they are more likely to work in the service sector, and they have a higher burden of childcare compared to men.

Gender inequality is a challenge across the globe, especially in low-income economies (Sachs, 2012). However, the gender difference is likely to be elevated after the COVID19 pandemic (Alon et al., 2020). A large strand of literature shows that men and women have different preferences (Andersen et al., 2013), also during the COVID-19 time (Fan, Orhun and Turjeman, 2020). For example, women have been found to be more risk averse (Dohmen et al., 2011; Eckel and Grossman, 2008), more pro-social (Eckel and Grossman, 1998), more patient (Dittrich and Leipold, 2014), and more conducive to family welfare (Ashraf, 2009; Rubalcava, Teruel and Thomas, 2009). During the COVID19 crisis, women are more risk-averse, worry more about their and others’ health, and take more preventive actions (Fan, Orhun and Turjeman, 2020). In Kenya, women predominate in agriculture but their contributions are mostly unpaid. Moreover, they are less predominant in formal employment and women’s property rights are often through men (Ellis et al., 2007). It is important to understand the gender differences in the COVID-19 impact for the future policy design attempting at alleviating gender inequality as well as for post-COVID relief interventions.

This study pools the first five waves from a nationally representative survey conducted 1
with Kenyan households via phone interviews between May 2020 and June 2021. By controlling for a rich set of household characteristics, which may affect the household head’s gender, we plausibly analyze the differences in household coping strategies household head We show that female-headed households in are by gender. Kenya more likely to rely on transfers or assistance from family members and friends, while male-headed ones have a higher probability of selling assets or taking loans to cope. However, no gender difference in reducing meals is observed. The gender difference in coping could be explained by differences in social preference, as well as poverty levels. Female-headed households are more likely to receive transfers from family members prior to the COVID-19 pandemic, and this habit may shape their coping strategies in to COVID-19. Male-headed households are wealthier and thus response they might have more assets to sell and be qualified to apply for loans to cope.

This paper is related to two strands of literature. Firstly, it adds directly to the literature on the gender differences in the COVID-19 impacts. It has been found that women would experience higher mental stress (Olaseni et al., 2020; Garc ́ıa-Fern ́andez et al., 2021), and are more likely to comply to the COVID-19 regulation (Galasso et al., 2020). In addition, during school closures, women undertake more childcare burden than men, and they were more likely to lose jobs and income (Etheridge and Spantig, 2020; Dang and Nguyen, 2021; Xue and McMunn, 2021). This paper complements this strand of literature by showing that female-headed households are relatively poorer, more likely to suffer from and thus have smaller assets to with the shock hunger, cope negative compared to the male-headed households.

Moreover, this paper adds directly to the literature studying female headship and empowerment, especially in low income economies. For example, it has been found that female-headed households would have lower income but receive larger family transfers (Quisumbing, Haddad and Pe ̃na, 2001; Horrell and Krishnan, 2007). When women are empowered, they would spend more on children’s nutrition, food consumption, and education (Ashraf, 2009; Rubalcava, Teruel and Thomas, 2009; De Brauw et al., 2014; Armand et al., 2020). In this paper, we find evidence that female-headed households are more vulnerable during a profound negative economic shock, and further suggest that specific policies need to target female-headed households to further enhance female empowerment, reduce gender inequality, as well as achieve higher effectiveness in alleviating hunger and COVID-19 impacts.

2
The remainder of this paper is organized as follows. Section 2 provides an overview of the spread of COVID-19 and the government measures in Kenya. Next Section 3 describes the dataset used in this paper and discusses the empirical approach. Section 4 presents empirical results on the gender differences in household coping strategies, including the main results, potential reasons for the difference, as well as sensitivity analyses. Finally, Section 5 concludes.

# **2 Background**

In Kenya, it is estimated that about one-third of the population (20 million people) live under the poverty line (The World Bank, 2021 _b_ ). The COVID-19 pandemic reached Kenya in early 2020, with the first case reported on March 13, 2020. Since then, there have been more than 210,000 confirmed cases, with 3,595 deaths as of June 29, 2021.

The Kenyan government adopted several containment measures following the outbreak of COVID-19. These included promotion of social distancing practices, restrictions on curfews and limits on public gatherings, night public transport passenger capacities. Schools were closed from March 15, 2020, and the re-opening was done in phases, with schools fully re-opening on January 4, 2021. Another lockdown and renewed school closures were put in place in late March 2021. Based on the Oxford Stringency Index (Hale et al., 2021), a composite measure of the severity of policy response in nine areas, Kenya has consistently had more stringent policies in place than other countries in SubSaharan Africa.

# **3 Data and Empirical Strategy**

We use the first five waves of the data from a rapid response phone survey conducted with Kenyan households by the World Bank from May 2020 to June 2021 (The World Bank, 2021 _a_ ). It includes samples of Kenyan nationals as well as refugees. The Kenyan national sample was drawn from two sources: (i) all households that were part of the 2015/16 Kenya Integrated Household Budget Survey Computer Assisted Personal Interview pilot and provided a phone number, and (ii) households selected using 3
the Random Digit Dialing method, using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The refugee samples were drawn from the UNHCR’s proGres database, which covers all registered refugees in Kenya.

Two national lockdowns were put in place during the survey periods. The first one started in mid-March 2020 and covered the first months of the first wave of the survey. A second lockdown was introduced in late March 2021, wrapping the fifth wave of the survey. The data was collected by phone interviews with a nationally representative sample of households using cell phones in Kenya, covering both urban and rural areas. The households were called every two months for five survey rounds, to track the impact of the pandemic over time.

The data consists of a wide range of information including the knowledge of COVID19 and mitigation measures, changes in behaviors in response to the COVID-19 pandemic, employment, income, food security, as well as perceptions of the government’s response.

We use the household head gender as the main indicator of gender. Compared to the male-headed households, the female-headed ones are more likely to receive transfers prior to the COVID-19 crisis (see Table 4 and Figure 2). Moreover, they have a higher childcare burden and smaller livestock values. However, these two types of households are not significantly different with regards to their poverty status.

In Kenya, about 37.4 percent of the households are female-headed (The World Bank, 2021 _a_ ). The female household heads are more likely to be separated, divorced or widowed relative to the male household heads. This indicates that it is likely that those female heads became the household head after the male head left the household due to death or divorce.

There are in total three categories of coping strategies by the household. The first was coping using the household internal wealth or resources. These include coping by reducing meals, selling assets or livestock, and taking loans. The second kind of coping would be coping based on help from the social network, including using transfers from family members and assistance from friends. Lastly, very few households in Kenya receive help externally, for example, from governments, NGOs, or politicians.

4
There are two sets of coping strategies in the survey data. One set implies the general household coping strategies used in the past 14 days, and the other indicates the household actions in to the COVID-19 shocks. We use the specifically response first set of coping strategies in the main analysis and cross check the gender differences in COVID-19 specific household actions in the sensitivity analysis.

As shown in the summary statistics displayed in Table A.1 in the Appendix, overall, the most commonly used coping strategy in Kenya is to reduce food consumption. About 38 percent of the households reduced meals when hit by COVID-19. Apart from that, households also take loans and rely on family transfers to cope. Only about four percent of the households have received any help from governments or NGOs.

We first perform descriptive analysis by estimating the probability of using various coping strategies by male-headed and female-headed households in each survey wave. Afterwards, we use linear probability models pooling all survey waves to formally test the gender differences in coping strategies. Specifically, the model takes the following form.

Where on the right hand side, _Copehct_ is a dummy, indicating whether the household _h_ in county _c_ , interviewed in wave _t_ , has used the specific coping strategy in the past 14 days. On the right hand side, there is a constant _α_ , as well as a dummy, _Female Headht_ , indicating whether the head of household _h_ in wave _t_ is female (= 1) or male (= 0). Moreover, several household characteristics might determine the household coping strategies, including the level of wealth, local social norms and institution. In light of this, a set of household controls, **X** _ht_ , is included in the regression model. These include the household head age, household size, the child-adult ratio, a dummy indicating whether the household is located in urban or rural areas, and a dummy indicating the status whether the household’s has a dirt poverty by examining dwelling space floor or not. Lastly, county fixed effects ( _δc_ ) and survey wave fixed effects ( _θt_ ) are included in the regression to control for all time-invariant characteristics specific to each county as well as all macro shocks at different interview waves, which might affect the choice of coping strategies directly. These might include, for example, different social norms 5
in coping strategies in different counties. The standard errors, _εhct_ , are clustered within counties.

The child-adult ratio is calculated by dividing the number of children who are under 18 years old by the total number of adults aged between 18 and 65 in the household. We use a dummy indicating whether the dwelling space of the household has a dirt floor or finished floor as an indicator for poverty, as this measure is highly correlated with other poverty indicators in Kenya and feasible to elicit in a phone interview.

Moreover, we hypothesize that female-headed households are poorer and have a closer relationship with social networks, and these cause the gender differences in coping. To formally check whether the gender differences in coping are due to these two reasons, we interact the household head gender dummy with the two hypothetical channels: the household poverty indicator, and the status of receiving transfers from families prior to the COVID-19 crisis.

A series of sensitivity analyses are performed. Firstly, Kenyan nationals might employ different coping strategies compared to refugees. Thus, we exclude the refugee sample from the analysis and cross check the results. Second, 17.72 percent of the households have reported different household head gender across the five waves, and we exclude those households whose head gender was changed.

All analyses are conducted in Stata v. 15.0 (College Station, Texas, USA) and take into account the sample design, particularly the sample weights and stratification.

# **4 Gender Differences in Coping Strategies**

## **4.1 Main Results**

As shown in the descriptive figures (Figure 1), male-headed households are more likely to cope using household internal resources but female-headed ones rely more on social networks, although the differences are not significantly different. For example, maleheaded households are more likely to sell assets to cope with a negative economic shock (Figure 1a). On average, about 10.4 percent of the male-headed households have sold any asset or livestock within the last 14 days, but only 6.9 percent of the female-headed 6
households have sold any asset. However, households with a female head are more likely to receive transfers from family members (Figure 1c). Specifically, female-headed (male-headed) households have a probability of 18.6 (12.7) percent of receiving any transfer or remittance from family. Although both the male-headed and female-headed households tend to reduce meals, there is no significant difference in the probability of reducing meals between these two types of households.

The regression analysis further confirms the differences in coping strategies between female-headed and male-headed households (Table 1). Compared to male-headed households, female-headed households are three percentage points less likely to sell assets or take loans in the past 14 days (columns (2) and (4)). Moreover, they have five (or two) percentage point higher probability of receiving transfers from families (or gifts and assistance from friends), relative to male-headed households (column (6) and (7)).

Table 1: Gender Differences in General Coping Strategies

|||House|hold Internal|||External - Fa|mily/Network|
|---|---|---|---|---|---|---|---|
||Reduce Meal<br>(0/1)|Sell Asset<br>(0/1)|ln<br>Asset|Loan<br>(0/1)|ln<br>Loan|Family<br>Transfer (0/1)|Gift/Assistance<br>(0/1)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Female Headed Household (0/1)|0.021|-0.032<sup>_∗∗∗_</sup>|-0.142|-0.028<sup>_∗∗_</sup>|-0.006|0.036<sup>_∗∗_</sup>|0.009|
||(0.018)|(0.012)|(0.104)|(0.012)|(0.130)|(0.014)|(0.011)|
|Household Head Age|0.002<sup>_∗∗∗_</sup>|-0.000|0.001|-0.002<sup>_∗∗∗_</sup>|0.009|0.002<sup>_∗∗∗_</sup>|0.001<sup>_∗∗_</sup>|
||(0.001)|(0.000)|(0.004)|(0.001)|(0.007)|(0.001)|(0.001)|
|ln Household Size|0.006|0.022|0.200<sup>_∗_</sup>|-0.009|0.197|-0.092<sup>_∗∗∗_</sup>|-0.039<sup>_∗_</sup>|
||(0.028)|(0.015)|(0.116)|(0.020)|(0.178)|(0.020)|(0.023)|
|Child-adult Ratio|0.067<sup>_∗∗∗_</sup>|0.010|-0.215<sup>_∗∗∗_</sup>|0.010|-0.053|0.043<sup>_∗∗∗_</sup>|0.003|
||(0.019)|(0.015)|(0.079)|(0.014)|(0.114)|(0.012)|(0.013)|
|Urban (0/1)|-0.029<sup>_∗∗_</sup>|-0.004|-0.108|-0.013|0.132|-0.002|0.002|
||(0.014)|(0.011)|(0.105)|(0.015)|(0.133)|(0.013)|(0.010)|
|Dirt Floor (0/1)|0.087<sup>_∗∗∗_</sup>|0.008|-0.146|-0.055<sup>_∗∗∗_</sup>|-0.266|0.041<sup>_∗∗∗_</sup>|0.036<sup>_∗_</sup>|
||(0.015)|(0.014)|(0.102)|(0.018)|(0.172)|(0.015)|(0.018)|
|Network (0/1)|0.014|0.023|0.051|0.043<sup>_∗_</sup>|0.132|0.159<sup>_∗∗∗_</sup>|0.131<sup>_∗∗∗_</sup>|
||(0.022)|(0.020)|(0.160)|(0.024)|(0.162)|(0.023)|(0.020)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|Observations<br>|30,967|30,978|2,521|30,978|4,666|30,934|30,968|
|_R_<sup>2</sup>|0.073|0.027|0.273|0.042|0.179|0.078|0.083|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. Dependent variables are different coping strategies utilized by the household. These include reducing meals, selling the assets, taking loans, receiving transfers from family members, and getting assistance from friends. In all regressions, the household age, household size, child-adult ratio, the urban dummy, a dummy indicating whether the floor is made of dirt, and county, wave, strata fixed effects are adjusted. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

In addition, as shown in Table 2, female-headed households would have smaller expenditures on food consumption and services, and more children in female-headed 7
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Table 2: Gender Differences in Household Consumption

|||E|xpenditures|on ...||# Days Hu|ngry At Night|# Days Ski|pped Meal|# Days No|Meal All Day|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Food|Personal|Durables|Services|Communication|Adult|Child|Adult|Child|Adult|Child|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Female Headed Household (0/1)|-127.920<sup>_∗∗∗_</sup>|11.330|12.742|-36.903<sup>_∗∗∗_</sup>|-32.174<sup>_∗∗∗_</sup>|0.015|0.027|0.097|0.097<sup>_∗_</sup>|-0.031|-0.022|
||(39.799)|(15.679)|(36.370)|(11.247)|(8.736)|(0.033)|(0.029)|(0.066)|(0.056)|(0.027)|(0.024)|
|Household Head Age|-3.201|-0.427|-0.263|-1.627|-0.828<sup>_∗_</sup>|-0.000|0.000|0.006<sup>_∗∗_</sup>|0.006<sup>_∗∗∗_</sup>|-0.000|-0.001<sup>_∗_</sup>|
||(2.240)|(0.499)|(1.673)|(1.160)|(0.442)|(0.001)|(0.001)|(0.003)|(0.002)|(0.001)|(0.001)|
|ln Household Size|594.504<sup>_∗∗∗_</sup>|141.558<sup>_∗∗∗_</sup>|-5.293|52.107<sup>_∗∗∗_</sup>|44.311<sup>_∗∗∗_</sup>|0.069<sup>_∗_</sup>|0.095<sup>_∗∗∗_</sup>|-0.025|0.200<sup>_∗∗_</sup>|0.033|0.049<sup>_∗∗_</sup>|
||(61.717)|(21.376)|(54.955)|(13.782)|(14.886)|(0.035)|(0.023)|(0.128)|(0.085)|(0.032)|(0.024)|
|Child-adult Ratio|-76.848|-65.192<sup>_∗∗∗_</sup>|-66.381<sup>_∗∗∗_</sup>|-49.983<sup>_∗∗_</sup>|-36.264<sup>_∗∗∗_</sup>|0.065<sup>_∗_</sup>|0.114<sup>_∗∗∗_</sup>|0.382<sup>_∗∗∗_</sup>|0.498<sup>_∗∗∗_</sup>|0.017|0.032|
||(66.662)|(18.201)|(16.521)|(21.458)|(8.186)|(0.034)|(0.031)|(0.077)|(0.076)|(0.032)|(0.027)|
|Urban (0/1)|71.933<sup>_∗_</sup>|0.025|-60.108|-20.670|33.342<sup>_∗∗∗_</sup>|-0.014|-0.014|-0.014|-0.031|-0.010|-0.012|
||(37.714)|(15.620)|(52.100)|(35.968)|(9.677)|(0.035)|(0.018)|(0.078)|(0.052)|(0.021)|(0.015)|
|Dirt Floor (0/1)|-247.111<sup>_∗∗∗_</sup>|-92.461<sup>_∗∗∗_</sup>|-75.471<sup>_∗∗∗_</sup>|-45.094<sup>_∗∗_</sup>|-71.131<sup>_∗∗∗_</sup>|0.152<sup>_∗∗∗_</sup>|0.085<sup>_∗∗∗_</sup>|0.322<sup>_∗∗∗_</sup>|0.236<sup>_∗∗∗_</sup>|0.065<sup>_∗∗_</sup>|0.045<sup>_∗_</sup>|
||(44.332)|(16.593)|(25.237)|(20.850)|(9.668)|(0.035)|(0.025)|(0.069)|(0.061)|(0.030)|(0.025)|
|Network (0/1)|7.281|35.702<sup>_∗_</sup>|-32.669|13.936|-13.611|0.010|-0.003|-0.141<sup>_∗_</sup>|-0.177<sup>_∗∗∗_</sup>|-0.028|-0.046<sup>_∗∗∗_</sup>|
||(51.534)|(18.055)|(35.492)|(10.783)|(8.153)|(0.035)|(0.029)|(0.074)|(0.054)|(0.026)|(0.012)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Observations<br>|30,550|30,545|24,354|30,739|30,788|30,884|30,896|30,892|30,900|30,897|30,901|
|_R_<sup>2</sup>|0.108|0.042|0.018|0.087|0.068|0.099|0.092|0.079|0.151|0.061|0.063|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

When it comes to household actions specific to COVID-19, similar gender differences are observed. Male-headed households have a higher probability of selling assets and female-headed households are more likely to receive help from the social network (Figure A.1a-A.1b). In addition, the regression analysis in Table 3 suggests that female-headed households are less likely to sell assets and take loans, but have higher probabilities of receiving assistance from family members in light of the COVID-19 shocks.

9
# Table 3: Gender Differences in Household Action to Cope with COVID-19

||Sell|Use|Generate|Sell|Take|Buy w.|Delay|Reduce C|onsumption|Friend/|Family||Assist|||No|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Asset|Savings|Income|Harvest|Loan|Credit|Pay|Food|Non-food|Assist|Borrow|NGO|Employer|Gov|Insurance|Actions|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|(16)|
|Female Headed Household (0/1)|-0.034<sup>_∗∗∗_</sup>|-0.019|0.008|-0.014|-0.028<sup>_∗∗∗_</sup>|0.019|0.014|-0.013|-0.016|0.037<sup>_∗∗∗_</sup>|0.009|-0.002|-0.005<sup>_∗_</sup>|0.004|0.001|-0.009|
||(0.009)|(0.020)|(0.013)|(0.009)|(0.010)|(0.018)|(0.010)|(0.018)|(0.025)|(0.012)|(0.015)|(0.002)|(0.002)|(0.004)|(0.005)|(0.015)|
|Household Head Age|-0.000|-0.001|-0.001<sup>_∗_</sup>|0.001<sup>_∗∗_</sup>|-0.001<sup>_∗∗∗_</sup>|-0.000|-0.000|0.001|0.001|0.002<sup>_∗∗∗_</sup>|-0.001<sup>_∗∗∗_</sup>|0.000|-0.000|0.001<sup>_∗∗_</sup>|0.000|0.000|
||(0.000)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|
|ln Household Size|0.027<sup>_∗_</sup>|0.047<sup>_∗∗_</sup>|0.031|0.014|-0.008|0.042<sup>_∗∗_</sup>|0.024|0.082<sup>_∗∗∗_</sup>|0.065<sup>_∗∗_</sup>|-0.046<sup>_∗∗∗_</sup>|0.026<sup>_∗∗_</sup>|-0.003|-0.006|-0.009|-0.001|-0.019|
||(0.015)|(0.019)|(0.027)|(0.011)|(0.013)|(0.016)|(0.016)|(0.024)|(0.025)|(0.015)|(0.011)|(0.004)|(0.004)|(0.009)|(0.007)|(0.025)|
|Child-adult Ratio|0.003|-0.034<sup>_∗∗_</sup>|-0.015|-0.001|-0.008|-0.011|-0.015<sup>_∗_</sup>|0.029|0.011|0.005|-0.006|0.004|0.001|0.007|-0.002|-0.015|
||(0.013)|(0.013)|(0.021)|(0.008)|(0.006)|(0.014)|(0.008)|(0.019)|(0.016)|(0.015)|(0.013)|(0.004)|(0.003)|(0.010)|(0.005)|(0.013)|
|Urban (0/1)|-0.018|-0.010|-0.000|-0.008|-0.015|-0.009|0.012|0.014|0.021|-0.014|-0.032<sup>_∗∗∗_</sup>|-0.005|-0.003|-0.003|0.001|0.006|
||(0.012)|(0.019)|(0.024)|(0.006)|(0.015)|(0.015)|(0.010)|(0.018)|(0.014)|(0.011)|(0.009)|(0.005)|(0.005)|(0.003)|(0.004)|(0.012)|
|Dirt Floor (0/1)|0.008|0.030|-0.023|0.017<sup>_∗_</sup>|-0.033<sup>_∗∗_</sup>|0.022|-0.011|0.017|-0.002|0.005|-0.027<sup>_∗_</sup>|0.004|-0.007<sup>_∗∗∗_</sup>|-0.001|-0.014<sup>_∗∗∗_</sup>|-0.020|
||(0.013)|(0.019)|(0.019)|(0.010)|(0.013)|(0.019)|(0.012)|(0.020)|(0.018)|(0.014)|(0.015)|(0.004)|(0.002)|(0.003)|(0.005)|(0.018)|
|Network (0/1)|0.004|0.021|-0.079<sup>_∗∗∗_</sup>|0.017|0.052<sup>_∗_</sup>|0.028|0.001|0.056<sup>_∗_</sup>|0.084<sup>_∗∗∗_</sup>|0.142<sup>_∗∗∗_</sup>|0.043<sup>_∗∗_</sup>|-0.001|-0.004<sup>_∗∗_</sup>|0.010|-0.001|-0.020|
||(0.015)|(0.022)|(0.021)|(0.016)|(0.026)|(0.017)|(0.016)|(0.031)|(0.027)|(0.026)|(0.021)|(0.002)|(0.002)|(0.010)|(0.008)|(0.014)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|<br>Observations|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|30947|
|_R_<sup>2</sup>|0.026|0.055|0.041|0.040|0.034|0.029|0.021|0.061|0.069|0.059|0.045|0.091|0.025|0.041|0.028|0.035|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
# **4.2 Reasons for the Gender Difference**

We two reasons behind the differences in propose potential gender coping strategies. the female-headed households are the femaleFirstly, relatively poorer. Although headed households are not more likely to have unfinished floors, they bear relatively higher childcare burden (Figure 2b, and Table 4, column (3)). The child-adult ratio is about 7.2 percent higher in female-headed compared to male-headed households. In male-headed ones, the average child-adult dependency ratio is 0.47, that is, there are 2.13 adults for every child in the household. However, the female-headed households have an average child-adult dependency ratio of 0.54, and there are only 1.85 adults for every child. In addition, the average livestock value in the male-headed households is about 60 percent higher than in the female-headed ones (Figure 2c, and Table 4, column (4)). Moreover, the female-headed households are less likely to engage in agricultural production or have wage employees to generate income (Table 4, column (5)-(7)). Secondly, female-headed households are more dependent on social networks prior to the COVID-19 outbreak (Figure 2d, and Table 4, column (1)). These two factors might explain why male-headed households are more likely to cope using household internal wealth (since they are relatively richer) but female-headed one rely more on social network (as they are more dependent on network even before the COVID-19 shock).

Table 4: Channels - Gender Differences in Social Network, Poverty, and Source of Income

||Network (0/1)|Dirt Floor (0/1)|Child-Adult Ratio|ln Livestock|Agriculture (0/1)|Wage Employment (0/1)|Family Business (0/1)|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Female Headed Household (0/1)|0.071<sup>_∗∗∗_</sup>|0.006|0.136<sup>_∗∗∗_</sup>|-0.600<sup>_∗∗∗_</sup>|-0.046<sup>_∗_</sup>|-0.064<sup>_∗∗∗_</sup>|0.003|
||(0.017)|(0.025)|(0.027)|(0.160)|(0.024)|(0.020)|(0.015)|
|Household Head Age|0.001|0.004<sup>_∗∗∗_</sup>|-0.004<sup>_∗∗∗_</sup>|0.001|0.005<sup>_∗∗∗_</sup>|0.001<sup>_∗_</sup>|-0.002<sup>_∗∗∗_</sup>|
||(0.001)|(0.001)|(0.001)|(0.003)|(0.001)|(0.001)|(0.000)|
|ln Household Size|-0.051<sup>_∗_</sup>|0.124<sup>_∗∗∗_</sup>|0.827<sup>_∗∗∗_</sup>|0.028|0.208<sup>_∗∗∗_</sup>|0.105<sup>_∗∗∗_</sup>|0.028|
||(0.027)|(0.033)|(0.027)|(0.241)|(0.031)|(0.033)|(0.022)|
|Child-adult Ratio|-0.000|0.040<sup>_∗_</sup>||-0.114|-0.026|-0.012|0.001|
||(0.020)|(0.022)||(0.196)|(0.022)|(0.016)|(0.022)|
|Urban (0/1)|0.002|-0.132<sup>_∗∗∗_</sup>|0.009|-0.100|-0.119<sup>_∗∗∗_</sup>|-0.031<sup>_∗_</sup>|0.006|
||(0.010)|(0.023)|(0.029)|(0.188)|(0.024)|(0.016)|(0.012)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|<br>Observations<br>|31,187|31,481|31,703|6,095|31,703|28,930|28,389|
|_R_<sup>2</sup>|0.058|0.252|0.501|0.161|0.311|0.140|0.050|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

When we interact the household head gender dummy with the two hypothetical channels, and include the channel variables into the regressions, we find further 11
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the coefficients of the poverty dummy in columns (4) and (5) show that those poor households are less likely to take loans, and would take smaller loans if they take any, to cope.

Table 5: Gender Differences in General Coping Strategies - Poverty Channel

|||Househ|old Interna|l||External - Fa|mily/Network|
|---|---|---|---|---|---|---|---|
||Reduce Meal<br>(0/1)|Sell Asset<br>(0/1)|ln<br>Asset|Loan<br>(0/1)|ln<br>Loan|Family<br>Transfer (0/1)|Gift/Assistance<br>(0/1)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Female Headed Household (0/1)|0.044<sup>_∗∗∗_</sup>|-0.031<sup>_∗_</sup>|-0.140|-0.024|-0.072|0.034<sup>_∗∗_</sup>|0.011|
||(0.016)|(0.016)|(0.145)|(0.016)|(0.134)|(0.016)|(0.014)|
|Female Head (0/1)_×_Dirt Floor (0/1)|-0.064<sup>_∗∗_</sup>|-0.003|-0.006|-0.013|0.241|0.008|-0.006|
||(0.026)|(0.025)|(0.245)|(0.027)|(0.286)|(0.028)|(0.024)|
|Dirt Floor (0/1)|0.110<sup>_∗∗∗_</sup>|0.009|-0.144|-0.051<sup>_∗∗_</sup>|-0.348<sup>_∗_</sup>|0.038<sup>_∗_</sup>|0.038<sup>_∗_</sup>|
||(0.020)|(0.019)|(0.131)|(0.022)|(0.198)|(0.020)|(0.021)|
|Household Head Age|0.002<sup>_∗∗∗_</sup>|-0.000|0.001|-0.002<sup>_∗∗∗_</sup>|0.009|0.002<sup>_∗∗∗_</sup>|0.001<sup>_∗∗_</sup>|
||(0.001)|(0.000)|(0.004)|(0.001)|(0.006)|(0.001)|(0.001)|
|ln Household Size|0.005|0.022|0.200|-0.009|0.182|-0.092<sup>_∗∗∗_</sup>|-0.039<sup>_∗_</sup>|
||(0.028)|(0.015)|(0.124)|(0.020)|(0.171)|(0.020)|(0.022)|
|Child-adult Ratio|0.068<sup>_∗∗∗_</sup>|0.010|-0.214<sup>_∗∗_</sup>|0.010|-0.044|0.043<sup>_∗∗∗_</sup>|0.003|
||(0.019)|(0.015)|(0.083)|(0.014)|(0.110)|(0.012)|(0.013)|
|Urban (0/1)|-0.030<sup>_∗∗_</sup>|-0.004|-0.108|-0.014|0.138|-0.002|0.002|
||(0.014)|(0.011)|(0.105)|(0.015)|(0.131)|(0.013)|(0.010)|
|Network (0/1)|0.014|0.023|0.051|0.043<sup>_∗_</sup>|0.137|0.159<sup>_∗∗∗_</sup>|0.131<sup>_∗∗∗_</sup>|
||(0.022)|(0.020)|(0.160)|(0.024)|(0.160)|(0.023)|(0.020)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|Observations<br>|30,967|30,978|2,521|30,978|4,666|30,934|30,968|
|_R_<sup>2</sup>|0.074|0.027|0.273|0.042|0.180|0.078|0.083|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Similarly, in Table 6 column (1), we also find that households with a female head or those which relied on family transfers prior to the COVID-19 crisis are more likely to reduce meals. However, those with a female head and relied on family transfers before are less likely to reduce meals. In columns (6)-(7), we observe that the magnitude of the positive correlation between the female head dummy and coping using family transfers or network assistance reduces after including the network dummy, compared to Table 1. This indicates that the prior receipt of family transfers is correlated with the female head dummy, i.e. households with a female head are more likely to receive family transfers prior to COVID-19. In addition, those households, which received transfers prior to the crisis, are more likely to cope using help from the social network, which further When both channel variables as well as the interaction terms are included in the same regression (Table 7), we still observe that being poor and reliant on social network 13
Table 6: Gender Differences in General Coping Strategies - Social Network Channel

|||House|hold Internal|||External - Fa|mily/Network|
|---|---|---|---|---|---|---|---|
||Reduce Meal<br>(0/1)|Sell Asset<br>(0/1)|ln<br>Asset|Loan<br>(0/1)|ln<br>Loan|Family<br>Transfer (0/1)|Gift/Assistance<br>(0/1)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Female Headed Household (0/1)|0.037<sup>_∗_</sup>|-0.031<sup>_∗∗_</sup>|-0.095|-0.022|-0.033|0.037<sup>_∗∗_</sup>|0.014|
||(0.019)|(0.013)|(0.102)|(0.014)|(0.138)|(0.014)|(0.011)|
|Female Head (0/1)_×_Network (0/1)|-0.146<sup>_∗∗∗_</sup>|-0.015|-0.326|-0.057|0.206|-0.002|-0.054|
||(0.046)|(0.047)|(0.369)|(0.062)|(0.250)|(0.046)|(0.044)|
|Network (0/1)|0.091<sup>_∗∗∗_</sup>|0.031|0.186|0.073|0.034|0.161<sup>_∗∗∗_</sup>|0.159<sup>_∗∗∗_</sup>|
||(0.027)|(0.028)|(0.146)|(0.045)|(0.183)|(0.030)|(0.030)|
|Household Head Age|0.002<sup>_∗∗∗_</sup>|-0.000|0.001|-0.002<sup>_∗∗∗_</sup>|0.009|0.002<sup>_∗∗∗_</sup>|0.001<sup>_∗∗_</sup>|
||(0.001)|(0.000)|(0.004)|(0.001)|(0.007)|(0.001)|(0.001)|
|ln Household Size|0.005|0.022|0.191|-0.009|0.197|-0.092<sup>_∗∗∗_</sup>|-0.039<sup>_∗_</sup>|
||(0.028)|(0.015)|(0.117)|(0.020)|(0.179)|(0.020)|(0.023)|
|Child-adult Ratio|0.068<sup>_∗∗∗_</sup>|0.010|-0.210<sup>_∗∗∗_</sup>|0.010|-0.055|0.043<sup>_∗∗∗_</sup>|0.003|
||(0.019)|(0.015)|(0.078)|(0.015)|(0.114)|(0.012)|(0.013)|
|Urban (0/1)|-0.029<sup>_∗∗_</sup>|-0.004|-0.109|-0.013|0.131|-0.002|0.002|
||(0.014)|(0.011)|(0.105)|(0.015)|(0.132)|(0.013)|(0.010)|
|Dirt Floor (0/1)|0.087<sup>_∗∗∗_</sup>|0.008|-0.139|-0.055<sup>_∗∗∗_</sup>|-0.267|0.041<sup>_∗∗∗_</sup>|0.036<sup>_∗∗_</sup>|
||(0.015)|(0.014)|(0.103)|(0.018)|(0.171)|(0.015)|(0.018)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|<br>Observations|30,967|30,978|2,521|30,978|4,666|30,934|30,968|
|_R_<sup>2</sup>|0.075|0.027|0.275|0.042|0.179|0.078|0.084|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

could the choice of used the households. as seen shape coping strategies by Firstly, in columns (6) and (7), those households, which have received transfers from social networks prior to the COVID-19 crisis, are more likely to rely on transfers and assistance from family and friends to cope. In addition, when partialling out the effect of the pre-COVID dependency on social network, female-headed households are still 3.7 percentage points more likely to cope using transfers from family members. Secondly, we could observe that poorer households are less likely to take a loan to cope (columns (4)-(5)).

# **4.3 Robustness Checks**

We conduct two sensitivity analyses and check the robustness of the results in Table 1. To begin with, the rapid response phone survey sample consists of Kenyan nationals as well as refugees. Refugees might have different coping strategies from nationals and thus we exclude the refugee sample and cross-check the gender differences in coping among the Kenyan nationals.

14
Table 7: Gender Differences in General Coping Strategies - Both Channels

|||Househ|old Interna|l||External - Fa|mily/Network|
|---|---|---|---|---|---|---|---|
||Reduce Meal<br>(0/1)|Sell Asset<br>(0/1)|ln<br>Asset|Loan<br>(0/1)|ln<br>Loan|Family<br>Transfer (0/1)|Gift/Assistance<br>(0/1)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Female Headed Household (0/1)|0.059<sup>_∗∗∗_</sup>|-0.030<sup>_∗_</sup>|-0.102|-0.018|-0.091|0.034<sup>_∗∗_</sup>|0.016|
||(0.016)|(0.017)|(0.136)|(0.018)|(0.141)|(0.016)|(0.013)|
|Female Head (0/1)_×_Network (0/1)|-0.142<sup>_∗∗∗_</sup>|-0.015|-0.329|-0.056|0.175|-0.003|-0.053|
||(0.046)|(0.046)|(0.383)|(0.062)|(0.243)|(0.046)|(0.044)|
|Network (0/1)|0.089<sup>_∗∗∗_</sup>|0.031|0.186|0.072|0.054|0.161<sup>_∗∗∗_</sup>|0.159<sup>_∗∗∗_</sup>|
||(0.028)|(0.028)|(0.148)|(0.045)|(0.181)|(0.030)|(0.030)|
|Female Head (0/1)_×_Dirt Floor (0/1)|-0.060<sup>_∗∗_</sup>|-0.003|0.017|-0.011|0.227|0.008|-0.004|
||(0.027)|(0.025)|(0.257)|(0.026)|(0.285)|(0.028)|(0.024)|
|Dirt Floor (0/1)|0.109<sup>_∗∗∗_</sup>|0.009|-0.144|-0.051<sup>_∗∗_</sup>|-0.345<sup>_∗_</sup>|0.038<sup>_∗_</sup>|0.038<sup>_∗_</sup>|
||(0.020)|(0.019)|(0.131)|(0.022)|(0.199)|(0.020)|(0.021)|
|Household Head Age|0.002<sup>_∗∗∗_</sup>|-0.000|0.001|-0.002<sup>_∗∗∗_</sup>|0.009|0.002<sup>_∗∗∗_</sup>|0.001<sup>_∗∗_</sup>|
||(0.001)|(0.000)|(0.004)|(0.001)|(0.006)|(0.001)|(0.001)|
|ln Household Size|0.004|0.022|0.192|-0.009|0.183|-0.092<sup>_∗∗∗_</sup>|-0.039<sup>_∗_</sup>|
||(0.028)|(0.015)|(0.125)|(0.020)|(0.172)|(0.020)|(0.023)|
|Child-adult Ratio|0.068<sup>_∗∗∗_</sup>|0.010|-0.211<sup>_∗∗_</sup>|0.010|-0.046|0.043<sup>_∗∗∗_</sup>|0.003|
||(0.019)|(0.015)|(0.082)|(0.015)|(0.109)|(0.012)|(0.013)|
|Urban (0/1)|-0.029<sup>_∗∗_</sup>|-0.004|-0.109|-0.013|0.136|-0.002|0.002|
||(0.014)|(0.011)|(0.105)|(0.015)|(0.130)|(0.013)|(0.010)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|Observations|30,967|30,978|2,521|30,978|4,666|30,934|30,968|
|_R_<sup>2</sup>|0.076|0.027|0.275|0.043|0.180|0.078|0.084|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

15
As set out in Table A.2 in the Appendix, female-headed households are three percentage points less likely to cope by selling assets and taking loans, but are five percentage points more likely to receive family transfers. The results are almost the same as those in Table 1. Therefore, the results are in line with the main analysis.

Secondly, about 18 percent of the households report different household head gender. We exclude this type of households and analyze whether there is different household coping by different gender heads. As seen from Table A.3 in the Appendix, the femaleheaded households are less likely to sell assets, and more likely to receive transfers from family and assistance from friends. Overall, the sensitivity analyses here confirms the main results as shown in Table 1.

# **5 Conclusion**

In this paper, we study the differences in coping strategies by households with different head genders in Kenya. We find that female-headed households are more likely to cope using transfers from family members and male-headed ones would sell assets or take loans. However, no gender difference in reducing meals is observed. We also show that the difference is due to differences in household wealth and reliance on social networks.

While a few studies have analyzed the household coping strategies in light of COVID19, no prior paper has investigated in the gender differences in coping. This paper has not only revealed the differences in household coping strategies, but also pointed out the potential reasons behind them. Moreover, the findings in this study are consistent with Quisumbing, Haddad and Pe ̃na (2001), who find that female-headed households in Africa are poorer but more likely to receive family transfers.

The in this have several we out that the findings paper implications. Firstly, point female-headed households in Kenya are specifically vulnerable in light of a negative shock. Moreover, those female household heads are more likely to be divorced or widowed compared to the male household heads. This suggests that the relief programs need to specifically target certain types of households (which are female-headed, and poorer) or widowed and divorced women to achieve higher effectiveness. Secondly, we point out that female-headed households rely more on family transfers even before the COVID-19 pandemic. This indicates that those households might be in desperate need 16
of resources, and governments and NGOs could set up special cash transfer programs for this specific group. Finally, COVID-19 might have a long-lasting negative influence on household assets. The male-headed households are more likely to sell assets and livestock than the female-headed ones, but the female-headed ones have a smaller amount of assets. For the male-headed ones, while they might be able to cushion the shock in the short run, the sale of assets or livestock could have severe consequences for household welfare and productivity in the medium and long run. For the femaleheaded ones, there is need of supporting them in securing assets.

17