metadata: authors: 0: author: Tchuisseu Seuyong,Feraud
1: author: Edochie,Ifeanyi Nzegwu
2: author: Newhouse,David Locke
3: author: Silwal,Ani Rudra
count: Africa
docna: 0: docna: Who did Covid-19 hurt the most in
            Sub-Saharan Africa ?
docty: Policy Research Working Paper
theme: Inclusive Growth,Human Development and Gender,Data Development and Capacity Building,Economic Policy,Economic Growth and Planning,Disease Control,Pandemic Response,Public Sector Management,Data production, accessibility and use,Structural Transformation and Economic Diversification
abstracts: cdata!: How did the economic crisis caused by
            the Covid-19 pandemic impact poor households in Sub-Saharan
            Africa? This paper tackles this question by combining 73
            High-Frequency Phone Surveys collected by national
            governments in 14 countries with older nationally
            representative surveys containing information on household
            consumption. In particular, it examines how outcomes
            differed according to predicted per capita consumption
            quintiles in the first wave of the survey, and in subsequent
            waves by households&#x2019; predicted per capita
            consumption. The initial shock affected households
            throughout the predicted welfare distribution. Households in
            the bottom 40 percent responded by sharply increasing
            farming activities between May and July of 2020 and
            gradually increasing ownership of non-farm enterprises
            starting in August. This coincided with an improvement in
            welfare, as measured by a decline in food insecurity and
            distressed asset sales among these households during the
            second half of 2020. With respect to education, children in
            the bottom quintile were 15 percentage points less likely to
            engage in learning activities than those in the top quintile
            in the immediate aftermath of the crisis, and the engagement
            gap between the bottom 40 and top 60 widened in the summer
            before narrowing in the fall due to large declines in
            engagement among the top 60. Poorer households were slightly
            more likely to report receiving public assistance
            immediately following the shock, and this difference changed
            little over the course of 2020. The results highlight the
            widespread impacts of the crisis both on welfare and
            children&#x2019;s educational engagement, the importance
            of agriculture and household non-farm enterprises as safety
            nets for the poor, and the substantial recovery made by the
            poorest households in the year following the crisis.
display_title: Who did Covid-19 hurt the most in
            Sub-Saharan Africa ?
listing_relative_url: /research/2024/03/34283956/covid-19-hurt-most-sub-saharan-africa
topicv3: Education Indicators and Statistics
            m1326116 1522,Social Assistance m1327201 1466,Labor Market Analysis m1582757 4428,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2024-03-19T00:00:00Z
dois: 10.1596/1813-9450-10726
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 21
author_keys: 0: author_key: 1792232
1: author_key: 1862778
2: author_key: 450079
3: author_key: 990783
model_extractions: Policy Research Working Paper 10726

# Who Did Covid-19 Hurt the Most in Sub-Saharan Africa?

_Feraud Tchuisseu Seuyong Ifeanyi Edochie David Newhouse Ani Rudra Silwal_ Poverty and Equity Global Practice & Development Data Group March 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10726

# **Abstract**

How did the economic crisis caused by the Covid-19 pandemic impact poor households in Sub-Saharan Africa? This paper tackles this question by combining 73 High-Frequency Phone Surveys collected by national governments in 14 countries with older nationally representative surveys containing information on household consumption. In particular, it examines how outcomes differed according to predicted per capita consumption quintiles in the first wave of the survey, and in subsequent waves by households’ predicted per capita consumption. The initial shock affected households throughout the predicted welfare distribution. Households in the bottom 40 percent responded by sharply increasing farming activities between May and July of 2020 and gradually increasing ownership of nonfarm enterprises starting in August. This coincided with an improvement in welfare, as measured by a decline in food insecurity and distressed asset sales among these households during the second half of 2020. With respect to education, children in the bottom quintile were 15 percentage points less likely to engage in learning activities than those in the top quintile in the immediate aftermath of the crisis, and the engagement gap between the bottom 40 and top 60 widened in the summer before narrowing in the fall due to large declines in engagement among the top 60. Poorer households were slightly more likely to report receiving public assistance immediately following the shock, and this difference changed little over the course of 2020. The results highlight the widespread impacts of the crisis both on welfare and children’s educational engagement, the importance of agriculture and household non-farm enterprises as safety nets for the poor, and the substantial recovery made by the poorest households in the year following the crisis.

This paper is a product of the the Poverty and Equity Global Practice and the Development Data Group, Development Economics. 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 dnewhouse@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
# **Who did Covid-19 hurt the most in Sub-Saharan Africa?**

Feraud Tchuisseu Seuyong Ifeanyi Edochie David Newhouse Ani Rudra Silwal JEL codes: O12, I31 Keywords: Covid-19, Sub-Saharan Africa, Distributional impacts
Introduction How did the economic impact of the crisis caused by Covid-19 impacted poor households in Sub-Saharan Africa? While statistics on infections and deaths suggest that Sub-Saharan Africa (SSA) may have escaped the worst of Covid-19, the economic cost was major. Poverty in the region was projected to increase by 2 percentage points or more in 2020 (Montes et al. 2020 and Lakner et al. 2021). While the full extent of the economic damage is still unknown, data from phone surveys collected since the pandemic provide an indication of the adverse impact on livelihoods in the region.<sup>1</sup> However, little is known about how impacts differed for poor and wealthy households within countries. This note aims to help fill this gap in order to better understand the distributional impacts of the crisis and inform recovery efforts.

We combine data collected through 73 phone surveys in SSA since the early stages of the global pandemic with richer multi-topic household surveys collected prior to the pandemic to examine how the impact of Covid-19 differed for poorer and wealthier households, as measured by predicted household per capita consumption. There are six main findings:

1. The initial impact of Covid-19 on the prevalence of income declines and the ability to work as usual was widespread throughout the predicted consumption distribution.

2. During the summer of 2020, there were large (20 pp) increases in the share of households in the bottom 40 percent that engaged in farming activities, and a more gradual increase 10 pp increase in the share of these households that owned or partially owned a non-farm enterprise.

3. During the latter part of 2020 and early 2021, food insecurity and the incidence of distressed asset sales declined faster for the bottom 40 than the top 60 percent. Conversely, the prevalence of income declines and the inability to work as usual declined faster for the top 60 percent, who were better able to cope with the medium-term impacts of the crisis.

4. Children in the bottom 40 percent were less likely to engage in educational activities in the immediate aftermath of the crisis. This disadvantaged narrowed greatly over the course of December 2020, as the share of children engaging in learning or educational activities fell about 10 percentage points for the bottom 40 and about 17 pp for the top 60 both between June and December 2020.

5. Public government assistance slightly favored poorer households in the spring and summer of 2020, a pattern that changed little as the crisis evolved.

Overall, the results indicate that the initial impacts of the Covid-19 were widespread throughout the predicted welfare distribution, and that the agricultural sector and establishment of non-farm enterprises acted as an important safety net. The movement back to agriculture may have helped poorer households reduce food insecurity and distressed asset sales, but may have delayed their ability to stem declines in income. Households faced serious challenges maintaining their children’s engagement with educational activities during the summer, and households in the bottom 40 were slightly more likely than those in the top 60 to benefit from public assistance throughout 2020.

> 1 See Bundervoet et al, 2021, Khamis et al, 2021, Dang et al, 2020, and Gupta et al, 2020.

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Data and methodology

# **<u>High Frequency Phone Surveys and Global Monitoring Database</u>**

This section describes the data sources and the methodology used to examine the impact of Covid-19 across the welfare distribution in 14 countries in Sub-Saharan Africa. We rely on two different databases for our analysis: High Frequency Phone Surveys (HFPS) implemented since the pandemic began and the Global Monitoring Database (GMD), a database of household surveys implemented prior to the pandemic, compiled and managed by the World Bank. We rely on survey-to-survey imputation, which entails developing a model of household consumption in the GMD for each country, and using the model to impute the welfare status of households in each wave of that country’s HFPS.

Since Spring 2020, the World Bank has supported national statistical offices in developing countries to design and implement phone surveys to examine various aspects of Covid-19 on lives and livelihoods.<sup>2</sup> The rapid pace at which the Covid-19 pandemic has affected countries has made access to timely knowledge on the impacts of Covid-19 critical to effectively design, target and evaluate policy interventions. However, implementing face-to-face interviews was severely restricted in the context of widespread government-imposed social distancing practices. Phone surveys filled this gap by eliciting information from individuals and households rapidly and at low cost. In all 14 of the countries we consider, phone surveys were implemented as follow-up surveys from previous nationally representative surveys. These previous surveys were used to reweight the phone survey to make it more representative of the national population of households. However, the phone surveys disproportionately interview household heads, meaning that individual level characteristics such as the ability to work at usual were not representative of the underlying population of workers. Nonetheless, phone surveys can be helpful in tracking the responses to and impacts of the pandemic, particularly if they are combined with traditional household surveys. Table 1 lists the countries and waves of HFPS that were used for this analysis.

**Table 1: Sample size of HFPS waves and the corresponding multi-topic survey from GMD**

|Country<br>|||Hig <br>|h Frequency<br>|Phone Su<br>|rvey<br>|||||G<br>s<br>|MD<br>urvey<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Wave|1|2|3|4|5|6|7|8|9|10|11|Survey|
|Burkina Faso|1,968|2,210|2,125|2,104||2,071|2,032|2,011|1,998|||2018 EHCVM|
|Ethiopia|3,249|3,107|3,058|2,878|2,770|2,753|2,536|2,222|2,077|2,178|1,982|2015 HICES|
|Gabon|1,656|||||||||||2017 EGEP|
|Ghana|3,265|||||||||||2016 GLSS-VII|
|Gambia|1,436|1,385|1,335|1,332|1,297|||||||2018 EHCVM|
|Mali|1,766|1,935|1,901|1,901|1,766|1,884|1,760|1,694||||2018 EHCVM|
|Mozambique|1,097|978|960|841|901|||||||2014 IOF|
|Malawi|1,729|1,646|1,624|1,616|1,589|1,591|3,112|3,098|3,090|||2016 IHS-IV|
|Niger|1,264|||||||||||2018 EHCVM|
|Nigeria|1,950|1,820|1,790|1,789|1,773|1,762|1,726|1,723|1,706|1,699|1,680|2018 LSS|
|Senegal|1,220|||||||||||2018 EHCVM|

> 2 https://www.worldbank.org/en/topic/poverty/brief/high-frequency-monitoring-surveys

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|Sierra Leone|6,571|4,885|||||2018 SLIHS|
|---|---|---|---|---|---|---|---|
|Uganda|2,226|2,199|2,145|2,135|2,122|2,100|2016 UNHS|
|Zimbabwe|1,747|1,639|1,451||||2017 PICES|

A secondary source of data is the Global Monitoring Database (GMD), which is a collection of globally harmonized household survey data compiled by the World Bank. The GMD includes most recent household surveys used by national statistical agencies around the world, and subsequently, by the World Bank, to compute the official poverty statistics. In addition to the welfare aggregate, surveys in GMD include variables on demographics, assets, labor, education, and other topics that have been harmonized to a common set of specifications, making the GMD well-suited for cross-country analysis.

This analysis covers 14 countries in SSA (Table 1). Although more countries are included in both the GMD and HFPS, we were limited by three factors. First, the analysis could only be conducted in countries where the HFPS was conducted and which also has a survey available in GMD that was conducted prior to the pandemic. Second, we limited our analysis only to surveys in GMD that were conducted in recent years, using a cutoff of 2014. Third, we limited our analysis to countries that both had a minimum set of variables that could be used to model household per capita consumption.

# **<u>Survey-to-survey imputation</u>**

Survey-to-survey imputation (S2S) predicts household per capita consumption in a household survey that collects information only on selected covariates of consumption by using information from another survey that collected both the per capita consumption and its covariates. This literature builds largely on the poverty mapping literature (Elbers, Lanjouw, and Lanjouw, 2003) and has been widely implemented (Dang 2020, Newhouse et al. 2014, Stifel and Christiaensen 2007). This literature is in contrast to the multiple imputation literature inspired by Rubin (1987), which largely focuses on filling missing data within a survey rather than using information from one survey to make predictions in another one. There are generally two types of S2S studies in the literature: one that conducts the imputation over time and another that conducts the imputation within the same period but across surveys. The first type includes studies such as those by Newhouse and Vyas (2022) and Stifel and Christiaensen (2007). Studies that impute across surveys within the same time frame include Beltram et al. (2020), Dang (2019), Lucchetti et al. (2018), Dang et al. (2018), and Cuesta and Ibarra (2017). Dang (2020) and Dang et al. (2019) provide guidance on the appropriate methodology and caveats to using S2S.

Implementation of S2S in this study first involved identifying a set of variables that are available both in HFPS and GMD. This yielded the following variables: household size, urban/rural location, subnational administrative area, share of children, and share of elderly residents in the household. This list of variables was selected by carefully comparing the data dictionaries of HFPS and GMD. Both the HFPS and the GMD are both designed to be nationally representative, except for Mozambique, where the HFPS only covers urban areas. A major difference between the HFPS and the GMD is that while the GMD sample covers both households that own phones and those that do not, the HFPS (by design) only covers households that own a mobile phone. Since households that own mobile phones are likely to be a non-random sample of the population, we select a comparable sub-sample of mobile phone owners in the GMD for the purposes of S2S. Figure 1 compares these samples for the 14 countries in the sample. Appendix Figure 1 presents these differences for individual countries in our analysis sample.

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<!-- Start of picture text --> Mean Median<br>Household Size 74 7.5 8.3 6.7 6.2 7.1<br>Urban(household) 31.6 28.8<br>Children share<br>Elderly share 3.2 2.7 3.7 3.3 24 3.5<br>GMD GMD Phone HFPS GMD GMD Phone HFPS<br>Note: Percentage is used for non numeric indicators.<br><!-- End of picture text -->
With a measure of household predicted welfare in hand, it is straightforward to report averages of outcomes from the first survey wave by predicted welfare quintile, to get a sense of how the initial impacts of the crisis varied over the predicted per capita consumption distribution. Analyzing how these differences evolved over time, however, is more complicated because countries enter and exit the sample, forming an unbalanced sample. To analyze the evolution of the impact of Covid-19, we therefore pooled data from 73 phone surveys from the 14 countries with imputed welfare. The goal of the analysis is to investigate how trends in income loss, employment, food insecurity, and social protection evolved for households in the bottom two quintiles as opposed to the top three. To examine trends, we estimate a logit model using the following model specified at the household level: hc 1 2 hc hc is the outcome of interest observed in household h, region (i.e. state/province) cc **_r_** _,_ country **_c_** , and yy = ββ ff(tt) + ββ ff(tt) ∗BB40 + θθ + εε month **_t_** . In this case it is in this case is one of eight indicators: “Household received decreased total hrr(cc)cc yy income”, “Respondent could work as usual”, ”Household with children engaged in any learning/education activities since school closures”, “Households with adult member skipped a meal in the last month due to lack of money or other resources”, “Household engaged in farming activities since the beginning of 2020”, “Households owning a non-farm enterprise”, “Households receiving government assistance” , and “Households that sold assets to cope with the pandemic”. hc is a dummy variable taking on one if household h is in the bottom 40 percent. is a country fixed effect, and U hc is a dummy variable indicating that household h resides in an urban area. The term BB40 1 is the cubic cc polynomial in the month of the survey, measured since March 2020. Thus, the θθ 1 term reflects the UU average trend across months for the top three quintiles and the 1 2 ββ ff(tt) term ββ ff(tt) represents the average trend for households in the bottom 40 percent. Finally, hc is the error term, hrrcc ββ ff(tt) + ββ ff(tt) ∗BB40 clustered at the level of the survey wave. After estimating the model, we plot the average predictive εε margins for the bottom 40 and top 60 percent by month, evaluated at the means of the other covariates and treating all dummy variables as balanced.

# Results

This section contains the main findings of this analysis based on a S2S imputation of per capita household computation in HFPS based on a model of consumption in GMD. We examine the impact of Covid-19 on changes in household income and labor, food security, children’s education, and coping strategies implemented by households across the consumption distribution.

## **<u>Household Income and labor</u>**

The initial impact of Covid-19 on the prevalence of reported total income declines was widespread, with minor differences across the distribution. Figure 2a plots the share of households that experienced a decrease in total income immediately after Covid-19 struck. Across the 14 countries analyzed in this sample, the average country reported that more than 76% of households experienced a decrease in total income. This share is about 5 percentage points higher for the poorest quintile (Q1). Appendix Figure 3 show a similar pattern for farm income. Declines in wage income, in the initial wave of the HFPS, were most prevalent in the third and fourth quintile. Figure 2c describes how the prevalence of total income declines has evolved since the beginning of the pandemic, after controlling for country and urban location.

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<!-- Start of picture text --> 100 100<br>81.3<br>80 747 75.1 75.0 =v 80<br>~ 60 ~ 60<br>cc<br>88<br>oao oao<br>40 40<br>26.9<br>mB 24.2 23.2 23.8<br>20 20<br>0 0<br>Qi Q2 Q3 Q4 Q5 Qi Q2 Q3 Q4 Q5<br>ad<br>oO<br>@<br>Q<br>wo<br>gt<br>no}ot<br>cwon<br>a<br>3%<br>6a<br>o”<br>coaN<br>oe<br>re<br>2<br>o<br>Apr May Jun Jul Aug Sep Nov Dec Jan Feb<br>Note: % of respondents who were not able to work as usual last week. Standard errors clustered on survey wave<br><!-- End of picture text -->
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<!-- Start of picture text --> 100 100<br>80 80<br>= 60 60<br>9°Qo ca)°o<br>a o<br>40 40<br>20 20<br>12.7 11.4 10.9 9.4 8.6<br>0 i =.<br>OF 2 OS 4S at a2 st<br>: =<br>ZLi) ko]2no2nono<br>g 2saa<br>£ S<br>i)geo=o £ono5ono55<br>7p)<br>o<br>oO Apr May Jun Jul Aug Sep Nov Dec Feb Apr<br>Apr May Jun Jul Aug Sep Nov Dec Mar Apr —*®— Top60% -—®— Bottom 40% 40%<br>—®— Top60% —®— Bottom 40% Note: % of HHs HHs sold assets such as property during the pandemic to pay for basic needs. assets such as property during the pandemic to pay for basic needs. such as property during the pandemic to pay for basic needs. property during the pandemic to pay for basic needs. during the pandemic to pay for basic needs. pandemic to pay for basic needs. to pay for basic needs. pay for basic needs. for basic needs. basic needs. Standard errors clustered on survey wave clustered on survey wave on survey wave survey wave wave<br>Note: % of HHs received government assistance since the start of the pandemic. Standard errors clustered on survey wave<br><!-- End of picture text --> <!-- Start of picture text --> =<br>ko]2no2nono<br>2saa<br>S<br>£ono5ono55<br>o<br>Apr May Jun Jul Aug Sep Nov Dec Feb Apr<br>—*®— Top60% -—®— Bottom 40% 40%<br>Note: % of HHs HHs sold assets such as property during the pandemic to pay for basic needs. assets such as property during the pandemic to pay for basic needs. such as property during the pandemic to pay for basic needs. property during the pandemic to pay for basic needs. during the pandemic to pay for basic needs. pandemic to pay for basic needs. to pay for basic needs. pay for basic needs. for basic needs. basic needs. Standard errors clustered on survey wave clustered on survey wave on survey wave survey wave wave<br><!-- End of picture text -->
Conclusion Understanding the impact of Covid-19 on households in different parts of the welfare distribution was an important factor when considering appropriate policy responses in response to the pandemic and will provide lessons for similar events in the future. We combined the High-Frequency Phone Surveys collected by national governments since the beginning of the pandemic with older surveys with information on household consumption to shed light on this question. We find that the initial impact of Covid-19 has been fairly widespread throughout the consumption distribution. In the initial aftermath of the crisis, poorer households were equally likely to report income declines but less likely to report the inability to work as usual. While government assistance slightly favored poorer households initially, this progressive targeting disappeared quickly and the share of households receiving government assistance was similar for the bottom 40 and top 60 percent for the year after the crisis. In the summer and fall of 2020, there was a large increase for households in the bottom 40 in farming activities and non-farm household enterprises. This coincided with economic recovery, as seen by large reductions in food insecurity and distressed asset sales among the bottom 40 during this time, as well as a reduction in the prevalence of income declines. However, the engagement of children in educational activities remains a concern. By the end of 2020, only about one third of households with children reported that children aged 6 to 18 were engaged in educational activities, a decline of about 15 percentage points.

To sum up, the results suggest that the initial impact of the shock was severe throughout the welfare distribution, and that there was a marked recovery for poor households associated with an increase in farming and non-farm household enterprises. However, poor households remained more likely than nonpoor households to report income declines and not working as usual in the medium term. The impact on educational engagement was severe and felt throughout the distribution. The results highlight the importance of policies to ensure productivity in the agricultural sector, as well as non-agricultural household enterprises, in response to the crisis.

The analysis in this note could be extended in several directions. We do not closely examine variation in the impact of the global pandemic across the 14 countries in our analysis sample, or differences between urban and rural areas, which could provide further insights. We also do not examine the differences across various socio-economic characteristics such as age, gender, and labor market characteristics of individuals, although this is challenging in phone surveys because of the non-representative nature of the sample. As more face-to-face surveys are implemented and analyzed, a clearer picture will emerge of the distributional impacts of this prolonged pandemic in Sub-Saharan Africa.

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