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ORIGINAL RESEARCH
published: 07 February 2022
doi: 10.3389/fpubh.2021.806738
Frontiers in Public Health | www.frontiersin.org 1February 2022 | Volume 9 | Article 806738
Edited by:
Noor Hazilah Abd Manaf,
International Islamic University
Malaysia, Malaysia
Reviewed by:
Simon Grima,
University of Malta, Malta
Intan Zanariah Zakaria,
International Islamic University
Malaysia, Malaysia
*Correspondence:
Martha Mnyanga
marthamnyanga@gmail.com
Gowokani Chijere Chirwa
gowokani@gmail.com
Spy Munthali
spymunthali@gmail.com
Specialty section:
This article was submitted to
Public Health Policy,
a section of the journal
Frontiers in Public Health
Received: 01 November 2021
Accepted: 31 December 2021
Published: 07 February 2022
Citation:
Mnyanga M, Chirwa GC and
Munthali S (2022) Impact of Safety
Nets on Household Coping
Mechanisms for COVID-19 Pandemic
in Malawi.
Front. Public Health 9:806738.
doi: 10.3389/fpubh.2021.806738
Impact of Safety Nets on Household
Coping Mechanisms for COVID-19
Pandemic in Malawi
Martha Mnyanga*, Gowokani Chijere Chirwa*and Spy Munthali*
Economics Department, University of Malawi, Zomba, Malawi
Background: Covid-19 pandemic induced various shocks to households in Malawi,
many of which were failing to cope. Household coping mechanisms to shocks have an
implication on household poverty status and that of a nation as a whole. In order to assist
households to respond to the pandemic-induced shocks positively, the government
of Malawi, with support from non-governmental organizations introduced Covid-19
Urban Cash Intervention (CUCI) and other safety nets to complement the existing social
protection programs in cushioning the impact of the shocks during the pandemic. With
these programmes in place, there is a need for evidence regarding how the safety nets
are affecting coping. Therefore, this paper investigated the impact that safety nets during
Covid-19 pandemic had on the following household coping mechanisms: engaging
in additional income-generating activities, receiving assistance from friends and family;
reducing food consumption; relying on savings; and failure to cope.
Methods: The study used a nationally representative panel data from the Malawi
High Frequency Phone Survey on Covid-19 (HFPS Covid-19) and complemented it
with the fifth Integrated Household Panel Survey (IHPS), also known as living standards
measurement survey. Five Random Effects Probit Models were estimated, one for each
coping mechanism.
Results: Findings from this study indicated that beneficiaries of safety net programs
were more likely to rely on remittances from friends and family than the people who had
no safety nets. Furthermore, the safety net recipients were less likely to reduce food
consumption or rely on savings than the non-recipients. Despite the interesting findings,
we also noticed that safety nets had no significant impact on household engagement in
other income-generating activities in response to shocks.
Conclusion: The results imply that safety nets in Malawi during the Covid-19 pandemic
had a positive impact on consumption and prevented the dissolving of savings. Therefore,
these programs have to be scaled up, and the volumes be revised upwards.
Keywords: health economics, COVID-19 Malawi, health policy Malawi, public policy Malawi, safety nets, COVID-19
Africa, social protection programs Malawi, COVID-19 urban cash intervention Malawi
Mnyanga et al. Coping and COVID-19 in Malawi
INTRODUCTION
Countries, including Malawi, responded in various ways to
the novel Covid-19 pandemic that disrupted many economies
around the world. Malawi introduced various containment
measures to reduce the spread of the virus. These measures,
included compulsory screening of all travelers coming into
the country at the port of entry, a ban on all travelers from
highly affected countries, restrictions on public gatherings to a
maximum of 100 people and closure of all schools (1). These
measures, coupled with people’s fears of the novel virus, partly
contributed to the disruptions in the supply chains of various
goods and services in the country (1). Business operations were
also affected as opening hours were restricted.
As a result, Covid-19 Pandemic induced other shocks to
households, communities and nations apart from being a health
shock in itself (2–4). According to the National Statistical Office
(NSO) of Malawi, the uppermost shocks that the households
reported to have experienced from mid-March to July 2020 as
a result of Covid-19 pandemic included a fall in the price of
farming/business output reported by 66% of the interviewed
households. An increase in price of farming/business inputs and
disruption of farming, livestock, and/or fishing activities were
experienced by 30 and 29% of the households, respectively (5).
Whereas, from August 2020 to January 2021, the statistics show
that 59% of the interviewed households were affected by an
increase in price of major food items consumed, 36% by an
increase in price of farming/business inputs and 20% by non-
farm business closure (6). This shows that most of the Covid-19
induced household shocks were not idiosyncratic in nature as
they likely affected a large proportion of individuals within the
sample and beyond.
As such, the affected households no longer turned to each
other for assistance or for credit and most of them were finding it
hard to cope with the shocks that they faced. For instance, 78% of
the households who were hit by one or more shocks from March
to July 2020 did nothing in response to these shocks (5). This
has negative implications since such households were likely to
move into poverty if shocks persisted or they would not be able
to move out of it if they were already poor. On top of that, 20
and 31% relied on savings from March to July 2020 and August
2020 to January 2021, respectively (5,6). Channeling savings into
household consumption has long term negative implications on
household investment and, therefore that of the nation.
On top of that, reliance on savings is not sustainable, and there
was a high probability that such households would move into
poverty and fail to cope if shocks persisted. In turn, all these
pose a challenge toward eradicating poverty as highlighted in
the development agendas such as Sustainable Development Goals
(SDGS), Malawi Vision 2063, Pan African Vision 2063, and the
Malawi Growth and Development Strategy (7–10). Therefore, it
is important to understand how households had been responding
to these shocks given that coping responses have an implication
on the households’ socio-economic well-being and that of a
nation as a whole.
Literature shows that health shocks trigger borrowing or
assistance from friends and family rather than the use of any
other strategy as these shocks are idiosyncratic (11–14). However,
evidence of a reduction in these informal-risk sharing coping
mechanisms during Covid-19 pandemic has also been reported
elsewhere (15). Additionally, others (16) also found evidence that
there was less reliance on savings during a lockdown as a result
of Covid-19 pandemic. However, these studies did not consider
the economic interventions that had been put in place given their
recency and the role they played in assisting households cope
positively with various shocks.
In order to assist households to respond to the economic
crisis due to pandemic positively, the government of Malawi,
with support from Non-Governmental Organizations introduced
Covid-19 Urban Cash Intervention (CUCI) to complement the
existing social protection programs. Safety nets play a big role
in replacing lost incomes for households affected by various
economic shocks and those facing credit constraints to avoid
the use of negative coping mechanisms and improve their
consumption and increase their asset holdings (17–20). In this
regard, this study was implemented to investigate the impact that
safety nets during Covid-19 pandemic had on household coping
mechanisms for the pandemic in Malawi.
The study matters and it is of significance to undertake in
Malawi at this point and offers lessons beyond Malawi. Firstly,
it feeds into the development agenda for Malawi as it potentially
shows the aspects that may help put the country on track with the
agenda. The Malawi development agenda is guided by the United
Nations Sustainable Development Goals (SDGs), whose goal
number 1 is to “end poverty in all its forms everywhere.” Under
this goal, target 3 stresses the “implementation of nationally
appropriate social protection systems and measures for all,
including floors, and by 2030 achieve substantial coverage of
the poor and the vulnerable.” Target 5 states, “by 2030, build
the resilience of the poor and those in vulnerable situations
and reduce their exposure and vulnerability to climate related
extreme events and other economic, social and environmental
shocks and disasters” (7). Therefore, this study established the
role of safety nets during Covid-19 pandemic in reducing
household vulnerability to shocks and building their resilience
to future shocks for the realization of these targets in Malawi.
Hence, the study speaks directly to SDGs 1 and 5.
Furthermore, Goal number 2 under the SDGs is to “end
hunger, achieve food security and improve nutrition and
promote sustainable agriculture.” Target number 1 under this
goal states: “by 2030, end hunger and ensure access by all people,
in particular, the poor and the people in vulnerable situations,
including infants, to safe, nutritious and sufficient food all year
round.” Target 2 states: “by 2030, end all forms of malnutrition,
including achieving, by 2025, the internationally agreed targets
on stunting and wasting in children under 5 years of age, and
address the nutritional needs of adolescent girls, pregnant and
lactating women, and older persons” (7). Guided by this, the goal
on the key priority area of Health and Population Management
of the Malawi Growth and Development Strategy (MGDS III), is
to “Improve health and quality of the population for sustainable
socio-economic development” and one of the outcomes is
“reduced morbidity and mortality due to malnutrition” through
“promoting dietary diversity and consumption of high nutrient
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Mnyanga et al. Coping and COVID-19 in Malawi
value by addressing the production and marketing bottlenecks
particularly of fruits” (9). Therefore, this study established the
role of safety nets in offsetting household reduction in food
consumption as a response to a shock, since this mechanism
has a negative implication on household nutrition status. This
also impacts the nutrition status for school-going children, which
eventually impacts their learning outcomes and, hence, the
potential for a vicious circle of ex-ante child poverty.
All in all, the study contributes to the existing literature on
the importance of safety nets during a crisis in achieving the
goal of poverty eradication in the medium term through building
household resilience to shocks. It also assessed the role of safety
nets in enabling household investments to achieve the long-term
goal of transforming the Malawi nation into an upper middle-
income nation by 2063 as per the aspiration of Malawi vision
2063. This was achieved through an empirical assessment of
the impact that safety nets have on the following household
coping mechanisms in response to a shock during the pandemic:
engaging in additional income-generating activities, receiving
assistance from friends and family; reducing food consumption;
relying on savings; and failure to cope. In addition, it is the first
study within the region and Malawi in particular, which assess the
coping aspect in the times of COVID-19.
METHODS
Data
The paper used survey data of Malawi High Frequency Phone
Survey on Covid-19 (HFPS-Covid-19) that was conducted by
NSO with support from the World Bank. The survey aimed
to monitor the socio-economic impacts of Covid-19 pandemic
on households in Malawi. This paper used the second and the
third waves of the survey. The study also drew some variables
from the fifth Integrated Household Panel Survey. The data
is readily available for public use and free to download from
the World Bank website. https://microdata.worldbank.org/index.
php/catalog/3766.
Variables
In the second and third waves, the households were asked
if they experienced any shock(s) due to Covid-19. Each
household would list the shock(s) and one coping mechanism
they used in response to each. The study mainly focused
on five coping mechanisms that were mostly reported by
the households. Therefore, the dependent variables were these
coping mechanisms: engaging in additional income generating
activities, receiving assistance from friends and family, reducing
food consumption, relying on savings, as well as doing nothing.
These variables took a value of 1 if the household adopted that
particular mechanism and a 0 otherwise.
As explanatory variables, the models included the variable
on whether the household was a beneficiary of any social safety
net program. The variable was binary, bearing the value of 1
for a beneficiary and a 0 for a non-beneficiary. The models
also included variables on household demographic characteristics
of; size, number of dependents, and number of household
members above 18 years old. Also included were household
head characteristics of: age, sex (which took the value of 0 if
TABLE 1 | Descriptive statistics.
Variable Obs. Mean Std. Dev. Min Max.
Safety nets beneficiary 3,140 5% 0.213 0 1
Age of head 3,140 42 14.225 16 98
Female head 3,140 21% 0.405 0 1
Household size 3,140 5 2.274 1 19
Number of dependents 3,140 2 1.532 0 12
People Above 18 years old 3,140 2 1.164 1 9
Head has no education 3,140 3% 0.169 0 1
Head has primary education 3,140 51% 0.5 0 1
Head has secondary education 3,140 37% 0.484 0 1
Head has tertiary education 3,140 9% 0.287 0 1
Head in married 3,140 77% 0.423 0 1
Agricultural sector 3,140 25% 0.432 0 1
Wealth quintile 1 3,140 32% 0.467 0 1
Wealth quintile 2 3,140 26% 0.44 0 1
Wealth quintile 3 3,140 20% 0.398 0 1
Wealth quintile 4 3,140 13% 0.337 0 1
Wealth quintile 5 3,140 9% 0.285 0 1
Urban 3,140 37% 0.482 0 1
Northern region 3,140 15% 0.356 0 1
Central region 3,140 42% 0.494 0 1
Southern region 3,140 43% 0.495 0 1
Idiosyncratic shock 3,140 51% 0.5 0 1
Economic shock 3,140 72% 0.448 0 1
Health shock 3,140 9% 0.289 0 1
Socio political shock 3,140 17% 0.378 0 1
Engaging in other activities 3,140 2% 0.135 0 1
Receiving assistance from
friends and family
3,140 2% 0.126 0 1
Reducing food consumption 3,140 3% 0.156 0 1
Relying on savings 3,140 9% 0.284 0 1
Doing nothing 3,140 39% 0.487 0 1
the household head was male and the value of 1 if the head
was female), their education level, their sector of employment
(which took the value of 0 if the household head was employed
in a non-agricultural sector and 1 if they were employed in the
agricultural sector), and marital status (which took the value of
1 for married household heads and 0 for unmarried household
heads). Other household characteristics in the model included
the wealth category of the household, its place of residence (which
took the value of 0 for households in the rural area and 1 for
those in the urban area), as well, as region. On top of that, the
shock variables categorized into economic shocks, health shocks
and socio-political shocks were also included. Also considered in
the study as one of the explanatory variables was the scope of the
shocks, i.e., whether the shock was an idiosyncratic or covariate.
This variable took the value of 1 if the shock was idiosyncratic
and 0 otherwise.
Data Analysis
Firstly, descriptive statistics and cross-tabulations were
computed to understand the characteristics of the sample
for this study. These are presented in Tables 1–3. Then after the
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Mnyanga et al. Coping and COVID-19 in Malawi
TABLE 2 | Distribution of coping mechanisms.
Engaging Receiving Reducing Relying Doing
in other activities assistance consumption on savings nothing
No. % No. % No. % No. % No. %
Sex of head
Male 47 81 27 53 63 81 229 83 937 77
Female 11 19 24 47 15 19 48 17 272 23
Total 58 100 51 100 78 100 277 100 1,209 100
Education
No education 0 0 1 2 3 4 4 1 49 4
Primary 37 64 27 53 33 42 124 45 673 56
Secondary 16 28 22 43 35 45 111 40 414 34
Tertiary 5 8 1 2 7 9 38 14 73 6
Total 58 100 51 100 78 100 277 100 1,209 100
Marital status
Unmarried 11 19 23 45 14 18 57 21 291 24
Married 47 81 28 55 64 82 220 79 918 76
Total 58 100 51 100 78 100 277 100 1,209 100
Employment sector
Non-agriculture 43 74 43 84 69 89 240 87 815 67
Agriculture 15 26 8 16 9 11 37 13 394 33
Total 58 100 51 100 78 100 277 100 1,209 100
Region
North 20 35 18 35 12 15 25 9 149 12
Central 20 35 16 32 9 12 129 47 519 43
Southern 18 30 17 33 57 73 123 44 541 45
Total 58 100 51 100 78 100 277 100 1,209 100
Residence
Rural 35 60 27 53 46 59 157 57 814 67
Urban 23 40 24 47 32 41 120 43 395 33
Total 58 100 51 100 78 100 277 100 1,209 100
Wealth
Wealth quintile 1 10 17 13 26 38 49 105 38 334 28
Wealth quintile 2 18 31 15 29 17 22 79 29 302 25
Wealth quintile 3 15 26 10 20 5 6 47 17 266 22
Wealth quintile 4 11 19 10 20 8 10 27 9 180 15
Wealth quintile 5 4 7 3 5 10 13 19 7 127 10
Total 58 100 51 100 78 100 277 100 1,209 100
descriptive analysis, the econometric analysis was done. Five
Random Effects Probit Models were run, because of the binary
nature of the outcome variables, one for each coping mechanism.
On top of these, gender and regional Random Effects Models
were also run to establish if there are heterogeneities in terms of
gender and region. Results for the various econometric analysis
done, are shown in Tables 4–8.
RESULTS
Descriptive Statistics
As shown in Table 1, about 5% of the households in the
sample benefited from at least one of the social protection
programs. These included free food, Social Cash Transfers
(SCTs), CUCI, other cash transfers, as well as other in-kind
transfers (excluding food). The table also indicates that the
average age of heads of the households that were included in the
sample was 42, with the youngest head being 16 and the oldest
being 98 years old.
On top of that, the sample largely comprised male headed
households, with about 21% of the households being headed
by a female. The table also shows that 77% of the household
heads in this sample were married while the rest were unmarried.
The unmarried category included those who had never been
married, those who divorced/separated, and those who were
widowed. In terms of household size, the average number
of members per household in this sample was five. The
smallest household had one member, and the largest had 19
household members.
On top of that, the average number of dependents per
household, as well as adults above 18 years old, was two. In
addition, about half of the households were headed by a member
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Mnyanga et al. Coping and COVID-19 in Malawi
with primary education (51%), almost 37% had secondary
education, whereas 9 and 3% of household heads had tertiary
and no education, respectively. The sample also comprised
households whose majority were employed in a non-agricultural
sector, with about 25% being employed in the agricultural
sector. In terms of wealth categories, a large proportion of the
households in the sample (32%) were in the lowest quintile,
TABLE 3 | Distribution of shocks.
Economic Health Socio-political
shocks shocks shocks
No. % No. % No. %
Region
North 300 13 54 19 94 17
Central 958 42 141 49 255 47
Southern 1,008 45 94 32 194 36
Total 2,266 100 289 100 543 100
Place of residence
Rural 1,475 65 195 67 402 74
Urban 791 35 94 33 141 26
Total 2,266 100 289 100 543 100
whereas the top quintile consisted of about 9% of the households
in the sample.
Regarding location, the majority of the households in this
sample lived in the rural areas, with about 37% living in the
urban areas. Furthermore, 15% of the households was in the
Northern region, 42% was in the central region and 43% was
from the southern region. This means the study was dealing with
households mainly from the central and southern regions.
In terms of shock distribution, 72% of shocks experienced by
the households were economic related shocks which included:
job loss, non-farm business closure, increase in price of
farming/business inputs, fall in the price of farming/business
output, as well increase in the price of major food items
consumed. Almost 9% of the household shocks were health
shocks which included illness, injury, or death of income-
earning member of the household. At the same time, 17%
were socio-political shocks which were disruption of farming,
livestock and fishing activities as well as theft/looting of cash
and other property. On top of that, 51% of all these shocks were
idiosyncratic in nature as they only affected one household at a
particular time.
Out of the households that experienced at least a shock in
this sample, about 39% failed to cope with the shocks, 9% relied
on their savings, 3% reduced their food consumption, whereas
TABLE 4 | Marginal effects-overall probit models.
Engaging in additional
income generating
activities
Receiving assistance
from friends and
family
Reducing food
consumption
Relying on savings Doing nothing
Safety nets beneficiary (0/1) 0.00625 (0.013) 0.0486** (0.019) −0.0284** (0.014) −0.0517*** (0.016) −0.0123 (0.037)
Age of head (log) 0.00704 (0.009) 0.00715 (0.008) −0.0186 (0.019) −0.00527 (0.018) 0.0399 (0.031)
Female (0/1) −0.00560 (0.006) 0.0192** (0.009) −0.00750 (0.016) −0.00842 (0.017) 0.0159 (0.029)
Household size (log) 0.0264** (0.011) −0.0180* (0.011) 0.0864*** (0.026) −0.000519 (0.026) −0.0501 (0.043)
Dependents −0.00390 (0.003) 0.00147 (0.003) −0.0257*** (0.007) 0.000885 (0.006) 0.0119 (0.011)
Adults above 18 years (log) −0.0232*** (0.009) 0.00442 (0.009) −0.0603*** (0.021) 0.0117 (0.021) 0.0422 (0.036)
No education −0.00312 (0.015) 0.0274 (0.025) −0.0485 (0.037) 0.0945* (0.049)
Secondary education −0.00766 (0.006) 0.00240 (0.005) 0.00652 (0.012) 0.00113 (0.012) −0.0350* (0.020)
Tertiary education 0.00941 (0.010) −0.0208 (0.013) 0.00103 (0.021) 0.0273 (0.019) −0.0922*** (0.035)
Married (0/1) 0.00489 (0.006) −0.000611 (0.006) 0.0101 (0.016) 0.00214 (0.017) −0.0156 (0.028)
Agricultural sector (0/1) −0.00101 (0.005) −0.00680 (0.005) −0.0262** (0.010) −0.0563*** (0.011) 0.0828*** (0.021)
Wealth quintile 2 0.0152** (0.006) −0.00114 (0.006) −0.0229 (0.015) 0.00312 (0.015) −0.0159 (0.024)
Wealth quintile 3 0.0178** (0.007) 0.000517 (0.007) −0.0490*** (0.014) −0.0158 (0.016) 0.0137 (0.028)
Wealth quintile 4 0.0251** (0.011) 0.00806 (0.009) −0.0336* (0.017) −0.0175 (0.019) 0.0147 (0.033)
Wealth quintile 5 0.0117 (0.012) −0.00164 (0.009) 0.00161 (0.027) −0.0235 (0.021) 0.0161 (0.035)
Urban (0/1) 0.00901 (0.007) 0.00361 (0.005) 0.00287 (0.012) −0.00648 (0.012) −0.00139 (0.021)
Central region −0.0417*** (0.012) −0.0158* (0.008) −0.0588*** (0.019) 0.0474*** (0.014) 0.0495* (0.027)
Southern region −0.0430*** (0.012) −0.0174** (0.008) 0.00352 (0.021) 0.0372*** (0.013) 0.0367 (0.027)
Idiosyncratic shock (0/1) −0.00618 (0.006) 0.00969 (0.006) −0.0340*** (0.012) 0.0343*** (0.012) 0.0517** (0.021)
Economic shock (0/1) 0.0269*** (0.004) 0.00313 (0.006) 0.0920*** (0.010) 0.363*** (0.019)
Health shock (0/1) −0.00854 (0.006) 0.0128 (0.008) −0.0600*** (0.012) −0.0645** (0.030)
Socio-political shock (0/1) −0.0147*** (0.004) −0.0126*** (0.004) −0.0589*** (0.010) 0.0611*** (0.023)
N3,048 3,140 1,638 3,140 3,140
Standard errors in parentheses.
*p<0.1, **p<0.05, ***p<0.01.
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Mnyanga et al. Coping and COVID-19 in Malawi
TABLE 5 | Northern region regression results on safety nets.
Engaging in additional
income generating
activities
Receiving assistance
from friends and
family
Reducing food
consumption
Relying on savings Doing nothing
Safety nets beneficiary 0.00598 (0.013) 0.0482** (0.019) −0.0253 (0.016) −0.0521*** (0.016) −0.0134 (0.036)
Controls Yes Yes Yes Yes Yes
N3,048 3,140 1,638 3,140 3,140
Standard errors in parentheses.
*p<0.1, **p<0.05, ***p<0.01.
Full table is in Appendix.
TABLE 6 | Central region regression results on safety nets.
Engaging in additional
income generating
activities
Receiving assistance
from friends and
family
Reducing food
consumption
Relying on savings Doing nothing
Safety nets beneficiary 0.00412 (0.012) 0.0489** (0.019) −0.0284** (0.014) −0.0515*** (0.016) −0.0119 (0.037)
Controls Yes Yes Yes Yes Yes
N3,048 3,140 1,638 3,140 3,140
Standard errors in parentheses.
*p<0.1, **p<0.05, ***p<0.01.
Full table is in Appendix.
TABLE 7 | Southern region regression results on safety nets.
Engaging in additional
income generating
activities
Receiving assistance
from friends and
family
Reducing food
consumption
Relying on savings Doing nothing
Safety nets beneficiary 0.00745 (0.013) 0.0508** (0.020) −0.0255 (0.016) −0.0524*** (0.016) −0.0139 (0.036)
Controls Yes Yes Yes Yes Yes
N3,048 3,140 1,638 3,140 3,140
Standard errors in parentheses.
*p<0.1, **p<0.05, ***p<0.01.
Full table is in Appendix.
TABLE 8 | Male head regression results on safety nets.
Engaging in additional
income generating
activities
Receiving assistance
from friends and
family
Reducing food
consumption
Relying on savings Doing nothing
Safety nets beneficiary 0.00625 (0.013) 0.0486** (0.019) −0.0284** (0.014) −0.0517*** (0.016) −0.0123 (0.037)
Controls Yes Yes Yes Yes Yes
N3,048 3,140 1,638 3,140 3,140
Standard errors in parentheses.
*p<0.1, **p<0.05, ***p<0.01.
Full table is in Appendix.
2% engaged themselves in other income generating activities,
and another 2% received assistance from friends and family.
This means the rest used other mechanisms not included in this
analysis. This implies that a good number of households were
indeed failing to cope with various shocks during this Covid-
19 pandemic either because of the magnitude of the shocks or
they did not have the capacity to respond to the shocks using any
other mechanism.
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Mnyanga et al. Coping and COVID-19 in Malawi
TABLE 9 | Female head regression results on safety nets.
Engaging in additional
income generating
activities
Receiving assistance
from friends and
family
Reducing food
consumption
Relying on savings Doing nothing
Safety nets beneficiary 0.00625 (0.013) 0.0486** (0.019) −0.0284** (0.014) −0.0517*** (0.016) −0.0123 (0.037)
Controls Yes Yes Yes Yes Yes
N3,048 3,140 1,638 3,140 3,140
Standard errors in parentheses.
*p<0.1, **p<0.05, ***p<0.01.
Full table is in Appendix.
Distribution of Coping Mechanisms
Table 2 indicates that the majority of households who indicated
to have been engaged in other income generating activities,
received assistance from friends and family, reduced food
consumption, relied on savings or failed to cope after
experiencing a shock were headed by a male member.
The majority of households headed by someone with primary
education were using all the mechanisms except reducing food
consumption in response to a shock. On top of that, the
majority of those relying on savings were employed in the non-
agricultural sector.
In terms of location, those who relied on savings and those
who failed to cope with the shocks were mainly from the central
and southern region. In contrast, most of those who engaged in
additional income-generating activities and those who received
assistance were from the northern region. On top of that, more
than half of those who reduced their food consumption were
from the country’s southern region. In addition to that, all the five
mechanisms used in the analysis were mainly used by households
that lived in the rural areas.
Those who reduced food consumption relied on savings, and
failed to cope in response to a shock were mainly the poorest
(lowest quintile). At the same time, the majority of those who
engaged in other income generating activities and those who
received assistance from friends and family were in the second
lowest quintile of wealth.
Distribution of Shocks
In terms of distribution of the three types of shocks, households
in the central, southern, and rural areas had been hard hit
compared to those in the north and in the urban areas. This is
shown in Table 3.
Econometric Results
Moving away from the descriptive analysis, we present the
regression results in the following sections. The results are
presented as marginal effects and shown in Table 4. It had been
established through this analysis that being a beneficiary of any
safety net program had a significant impact on the following
coping mechanisms to shocks during Covid-19 pandemic;
receiving assistance from friends and family, reducing food
consumption and relying on savings. The first and final columns
of the table indicate no significant impact of safety nets programs
on household engagement in additional income generating
activities as a coping mechanism and on failure to cope after
experiencing a shock during the pandemic.
As compared to a non-beneficiary household, a beneficiary
household of any social safety net program was 5% more likely
to rely on assistance from friends and family, 3% less likely to
reduce food consumption, and 5% less likely to rely on savings in
response to a shock.
Other factors that affected the household probability of
adopting a specific coping mechanism included; sex of household
head, household size, number of dependents in a household,
number of household members above 18 years old, education of
household head, sector of employment, and region. In addition to
these variables, the scope as well as the type of shock experienced
by a household also had an impact on the adoption of a specific
coping mechanism. When hit by a shock, a female-headed
household was about 2% more likely to receive assistance from
friends and family as compared to a male-headed household, all
things being the same.
As the household size increased, the household was more
likely to engage in additional income-generating activities, less
likely to receive assistance from friends and family, and more
likely to reduce food consumption in response to a shock. On
top of that, a unit increase in the number of dependents in
a household was associated with less likelihood of reducing
food consumption as a coping mechanism to shocks, all things
being equal. On top of that, an increase in household members
above 18 years was associated with less likelihood of engaging
in additional income generating activities, and less likelihood of
reducing food consumption in response to a shock. In addition,
households for non-educated heads were more likely to fail
to cope with any shock during the pandemic than those with
primary education. Whereas, household heads with secondary
and tertiary education were less likely to fail to cope with the
shocks than those with primary education, all things being
the same.
Compared to those employed in the non-agricultural sector,
those in the agriculture sector were less likely to reduce food
consumption or rely on savings. On the other hand, these
households were more likely to fail to cope, unlike those in the
non-agricultural sector. Compared to those in the lowest quintile
of wealth (the poorest); the affluent households were more likely
to engage in other income generating activities in response to a
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Mnyanga et al. Coping and COVID-19 in Malawi
shock. The latter were also less likely to reduce food consumption
as a coping mechanism.
In terms of regions, households in the central region were
more likely to respond to a shock by relying on savings and
more likely to fail to cope than those in the northern region.
Households in the southern region were also more likely to
rely on savings when a shock hits as compared to those in the
northern region, all things being the same. On the other hand,
those in the central and southern regions were less likely to
engage in additional income generating activities and receive
assistance from friends and family as compared to those in the
northern region. In addition, those in the central region were
also less likely to reduce food consumption in response to a shock
unlike those in the northern region.
Idiosyncratic shocks were more likely to trigger reliance on
savings and less likely to reduce food consumption as coping
responses to shocks compared with covariate shocks. On top
of that, if hit by an idiosyncratic shock during the pandemic, a
household was more likely to fail to cope as compared to being hit
by a covariate shock. In terms of shock types, economic shocks
were more likely to trigger engagement in additional income
generating activities as a coping response, whereas socio-political
shocks were less likely to trigger this coping mechanism. On top
of that, economic shocks were more likely to trigger reliance on
savings and a high likelihood of failure to cope. On the other
hand, when faced with a health shock, a household was less likely
to rely on savings or fail to cope. Besides, households that faced
a socio-political shock were more likely to fail to cope with the
shock but less likely to receive assistance from friends and family
or rely on savings.
To assess heterogeneity in the results across regions, regional
regressions were run and the results are as presented in
Tables 5–7.
We found safety nets to be associated with a high likelihood
of receiving assistance from friends and family as a household
coping mechanism to shocks across all three regions. However,
the results showed a significantly larger difference in the
likelihood of seeking assistance between beneficiaries of social
protection programs and non-beneficiaries in the southern
region than the differences that existed in the other regions.
There was also a significantly larger impact in the southern
region. The beneficiary’s likelihood of relying on savings was
much lower than that of the non-beneficiary compared to the
other regions.
A significantly large difference in the likelihood of reducing
food consumption as a shock response also existed in the central
region, unlike the other regions. In the area, any social protection
program beneficiary was about 3% less likely to reduce food
consumption in response to a shock than a non-beneficiary.
Whereas, in the other regions, the probabilities’ differences were
not statistically significant. Full regional regression tables are in
the Appendix.
Heterogeneity across genders was also assessed, and the results
are as presented in Tables 8,9
Gender regressions showed safety nets having a significant
impact on receiving assistance from friends and family, reducing
food consumption, and relying on savings for both households
headed by males and females. The results showed no differences
across the genders. Full gender regression tables are also in
the Appendix.
DISCUSSION
Interesting results emanate from the study. Firstly, we established
that households that benefited from various safety nets programs
during the pandemic were less likely to reduce food consumption
and rely on savings. Thus, the findings support what was also
established in Ethiopia and other African countries (21). This
may suggest a positive development, given that by affecting
households in that way, it may help them accumulate some
wealth, thereby reducing some perpetual poverty. Even though
such a positive development was observed, the safety net
recipients were also more likely to rely on remittances from
friends and family. The results appear to be contrary to what we
may expect in normalcy. Furthermore, the result is in contrast to
what was established in other studies where it has been shown
that the safety nets are associated with reduced dependence on
partners and relatives compared to not having the safety nets (22).
Based on the above, we may speculate that this was such a case
since most of the times, the households that are included in these
programs hardly make enough incomes from which they can
save (23,24). When hit by any shock, such households rely more
on assistance, whether formal safety net programs or informal
remittances from friends and family. A finding that supports
what was established in the context of other shocks in the
Philippines and other developing countries, where these coping
acted as insurance (25). Furthermore, the results also imply
that the assistance being received was mainly being channeled
into household consumption and not necessarily into household
investment since we did not find evidence that the beneficiaries
were more likely to engage in additional income generating
activities than non-beneficiaries. This finding, therefore, is in line
with the literature that established the cushioning effect of these
safety nets (26–28). This may be because most of the targeted
beneficiaries were urban poor, who were probably living hand to
mouth situation, and that Covid-19 exacerbated the situation.
Apart from the result narrated, education also played an
important role. We noted that households headed by an
uneducated member were more likely to fail to cope with
the shocks during the pandemic. Most of the time, they
hardly have reliable incomes or connections to enable them
to use other coping mechanisms. On the other hand, those
with secondary and tertiary education had enough financial
as well as human capital that enabled them to cope with
the shocks in one way or the other. These findings were
contrary to those by (29) who found that household head’s
education was not significantly related to any specific shock
response behavior.
In this study, those in the third and fourth quintiles of wealth
(the rich) were less likely to reduce food consumption in response
to a shock than those in the lowest quintile (the poor). This
supports the findings by (13) and can be explained by the fact that
the former make enough incomes from other income generating
activities enabling them to cover for their food consumption
needs during a shock unlike the poor. This was also evidenced
Frontiers in Public Health | www.frontiersin.org 8February 2022 | Volume 9 | Article 806738
Mnyanga et al. Coping and COVID-19 in Malawi
from the results that those in the second to the upper quintiles
were more likely to engage in additional income generating
activities in response to shocks as compared to the poorest since
they have enabling financial and other resources.
The impact of household demographic characteristics implies
that a household would reduce its food consumption in
response to a shock if its size increased unless the additional
member was a dependent. This is plausible as dependents
have a lot of nutritional requirements, unlike others. And
also, the household was likely to engage in additional income-
generating activities if the additional member was below
the age of 18. This is so since such members require a
lot of support from the elder members, thereby forcing
them to look for other activities where they can generate
income from.
The finding that idiosyncratic shocks were more likely to
trigger failure to cope was in contrast with a risk-sharing theory
which stipulates that households have a wide range of coping
mechanisms during an idiosyncratic shock unlike a covariate
shock (30). On the other hand, this was plausible during this
pandemic since many households had been affected by at least
one shock, which made it hard for households to use other
mechanisms of borrowing or seeking assistance from friends
and family, rather they relied more on their own savings. This
means that each household was in this pandemic and facing its
impacts alone.
The paper has also established that there are regional
differences in terms of coping mechanisms adopted by
households during the pandemic. Unlike in other regions, there
is a significant impact of safety nets on reduction of food
consumption as a coping mechanism to shocks by households
in the central region. Beneficiaries in this region are less likely
to reduce food consumption as a shock response than non-
beneficiaries. This may be explained by the fact that the highest
proportion (55.8%) of people in the central region are poor
unlike the other regions (31). Malawi Poverty Estimates 2020
by the NSO also established that the central region has the
highest ultra-poverty rate (25.4%) and poverty is also deeper in
this region at 20.1% as compared to other regions. Therefore,
any levels of remittances in this region may likely have a
significant impact on household food consumption unlike in
other regions. We recognize the limitations of the study. First,
the study is based on recall data, and hence bias may be an issue.
Secondly, the methodology used does not address endogeneity
and should be interpreted in the current methodology (32–
34). Given our results, these results have some implications for
research. Firstly, future studies should try to use other quasi-
experimental methods such as an instrumental variable approach
to see if the findings remain robust to the method of estimation.
In our case, the lack of a proper instrument in the data made
this a problem for us. Despite these limitations, the study has
important implications for policy. The government of Malawi
should increase the level and size of the transfers being used as
they benefit the recipients, but they are not adequate to enable
them to invest.
CONCLUSION
We have established through this study a significant impact of
safety nets on the increasing probability of household reliance
on remittances from friends and family and decreasing the
probability of food reduction and reliance on savings as coping
mechanisms to shocks. The safety nets programs during Covid-
19 pandemic likely improved the beneficiaries’ nutrition status.
However, these programs had no significant impact on household
engagement in other income generating activities, as well as
households’ failure to cope. This implies they had no impact
on household investment. Therefore, these safety net programs
during the pandemic have to be scaled up. The amount of funds
has to be revised upwards to enable households to have enough
incomes to cover both consumption and investment needs. This
will in turn, make vulnerable households self-reliant and reduce
their dependency on remittances from friends and family during
a crisis as this mechanism also has a negative impact on the
incomes of the households rendering this assistance, especially
during this pandemic as almost every household had been
affected in one way or the other.
In addition to that, upon investing, vulnerable households
may be able to accumulate enough asset holdings and build
resilience to future shocks. They may eventually graduate from
these social protection programs, and new vulnerable households
will be able to be recruited and assisted likewise. In turn, the goal
of poverty eradication will be achieved in Malawi.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included
in the article/Supplementary Materials. The data is available for
free download at https://microdata.worldbank.org/index.php/
catalog/3766.
AUTHOR CONTRIBUTIONS
All authors listed have made a substantial, direct, and intellectual
contribution to the work and approved it for publication.
ACKNOWLEDGMENTS
The author acknowledges the participants of the University of
Malawi Department of Economics research workshops, for their
valuable constitutions.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpubh.
2021.806738/full#supplementary-material
Frontiers in Public Health | www.frontiersin.org 9February 2022 | Volume 9 | Article 806738
Mnyanga et al. Coping and COVID-19 in Malawi
REFERENCES
1. Chirwa GC, Dulani B, Sithole L, Chunga JJ, Alfonso W, Tengatenga J. Malawi
at the crossroads: does the fear of contracting covid-19 affect the propensity to
vote? Euro J Dev Res. (2021). doi: 10.1057/s41287-020-00353-1. [Epub ahead
of print].
2. Bonaccorsi G, Pierri F, Cinelli M, Flori A, Galeazzi A, Porcelli F, et al.
Economic and social consequences of human mobility restrictions
under COVID-19. Proc Natl Acad Sci USA. (2020) 117:15530–5.
doi: 10.1073/pnas.2007658117
3. Caggiano G, Castelnuovo E, Kima R. The global effects of Covid-19-induced
uncertainty. Econ Lett. (2020) 194:109392. doi: 10.1016/j.econlet.2020.109392
4. Fornaro L, Wolf M. Covid-19 Coronavirus and Macroeconomic Policy (SSRN
Scholarly Paper ID 3560337). Social Science Research Network (2020).
Available online at: https://papers.ssrn.com/abstract=3560337
5. National Statistical Office and World Bank. Findings-From-the-Second-
Round-of-the-High-Frequency-Phone-Survey.pdf. (2020). Available online
at: https://openknowledge.worldbank.org/bitstream/handle/10986/34545/
Findings-from- the-Second- Round-of- the-High- Frequency-Phone- Survey.
pdf?sequence=1&isAllowed=y (accessed May 10, 2021).
6. National Statistical Office and World Bank. Findings-From-the-Seventh-
Round-of-the-High-Frequency-Phone-Survey.pdf. (2021). Available online
at: https://openknowledge.worldbank.org/bitstream/handle/10986/35335/
Findings-from- the-Seventh- Round-of- the-High- Frequency-Phone- Survey.
pdf?sequence=1&isAllowed=y (accessed May 10, 2021).
7. United Nations. Agenda for Sustainable Development. (2015). Available online
at: https://sustainabledevelopment.un.org/content/documents/21252030
%20Agenda%20for%20Sustainable%20Development%20web.pdf (accessed
May 10, 2021).
8. African Union and Commission. Agenda 2063 the Africa We Want. (2015).
Available online at: https://au.int/sites/default/files/documents/36204-doc-
agenda2063_popular_version_en.pdf (accessed May 10, 2021).
9. Government of Malawi. Malawi Growth and Development Strategy III. (2017).
Available online at: https://npc.mw/wp-content/uploads/2020/07/MGDS_III.
pdf (accessed May 10, 2021).
10. Government of Malawi. Malawi Vision 2063. (2020). Available online at:
https://malawi.un.org/sites/default/files/2021-01/MW2063- %20Malawi
%20Vision%202063%20Document.pdf (accessed May 10, 2021).
11. Bonfrer I, Gustafsson-Wright E. Health shocks, coping strategies and foregone
healthcare among agricultural households in Kenya. Global Public Health.
(2017) 12:1369–90. doi: 10.1080/17441692.2015.1130847
12. Nguyen T-T, Nguyen TT, Grote U. Multiple shocks and households’ choice
of coping strategies in rural Cambodia. Ecol Econ. (2020) 167:106442.
doi: 10.1016/j.ecolecon.2019.106442
13. Sparrow R, Van de Poel E, Hadiwidjaja G, Yumna A, Warda N, Suryahadi
A. Financial consequences of ill health and informal coping mechanisms in
Indonesia. In: SMERU Research Institute Working Paper. Jakarta (2013).
14. Yilma Z, Mebratie A, Sparrow R, Abebaw D, Dekker M, Alemu G, et al.
Coping with shocks in rural Ethiopia. J Dev Stud. (2014) 50:1009–24.
doi: 10.1080/00220388.2014.909028
15. Janssens W, Pradhan MP, De Groot R, Sidze E, Donfouet H, Abajobir A.
The short-term economic effects of COVID-19 and risk-coping strategies
of low-income households in Kenya: a rapid analysis using weekly financial
household data. In: Tinbergen Institute Disucssion Paper,40. (2020).
doi: 10.2139/ssrn.3640340
16. Adesina-Uthman GA, Obaka AI. Financial coping strategies of households
during COVID-19 induced lockdown. Empirical Econ Rev. (2020) 3:83–114.
doi: 10.29145/eer/32/030205
17. Alderman H, Yemtsov R. How can safety nets contribute to economic growth?
World Bank Econ Rev. (2014) 28:1–20. doi: 10.1093/wber/lht011
18. Begum I, Akter S, Alam M, Rahmatullah N. Social Safety Nets and Productive
Outcomes: Evidence and Implications for Bangladesh. Bangladesh Agricultural
University, Mymensingh, Bangladesh (2014).
19. Hidrobo M, Hoddinott J, Peterman A, Margolies A, Moreira V. Cash, food,
or vouchers? Evidence from a randomized experiment in northern Ecuador. J
Dev Econ. (2014) 107:144–56. doi: 10.1016/j.jdeveco.2013.11.009
20. Hidrobo M, Hoddinott J, Kumar N, Olivier M. Social protection,
food security, asset formation. World Dev. (2018) 101:88–103.
doi: 10.1016/j.worlddev.2017.08.014
21. Gilligan DO, Hoddinott J, Taffesse AS. The impact of Ethiopia’s productive
safety net programme and its linkages. J Dev Stud. (2009) 45:1684–706.
doi: 10.1080/00220380902935907
22. Peterman A, Neijhoft (Naomi) A, Cook S, Palermo TM. Understanding the
linkages between social safety nets and childhood violence: a review of the
evidence from low- and middle-income countries. Health Policy Plann. (2017)
32:1049–71. doi: 10.1093/heapol/czx033
23. Ottie-Boakye D. Coverage of non-receipt of cash transfer (Livelihood
Empowerment Against Poverty) and associated factors among older persons
in the Mampong Municipality, Ghana – a quantitative analysis. BMC Geriatr.
(2020) 20:406. doi: 10.1186/s12877-020-01786-3
24. Jimu IM, Msilimba G. Targeting practices and biases in social cash transfers:
Experiences in rural Malawi. Afr Dev. (2018) 43:65–84.
25. Adams RH Jr. Evaluating the economic impact of international
remittances on developing countries using household surveys: a literature
review. J Dev Stud. (2011) 47:809–28. doi: 10.1080/00220388.2011.
563299
26. Combes J-L, Ebeke C. Remittances and household consumption
instability in developing countries. World Dev. (2011) 39:1076–89.
doi: 10.1016/j.worlddev.2010.10.006
27. De Janvry A, Finan F, Sadoulet E, Vakis R. Can conditional cash
transfer programs serve as safety nets in keeping children at school and
from working when exposed to shocks? J Dev Econ. (2006) 79:349–73.
doi: 10.1016/j.jdeveco.2006.01.013
28. Dercon S. Income risk, coping strategies, and safety nets. World Bank Res Obs.
(2002) 17:141–66. doi: 10.1093/wbro/17.2.141
29. Börner J, Shively G, Wunder S, Wyman M. How do rural households
cope with economic shocks? Insights from global data using hierarchical
analysis. J Agric Econ. (2015) 66:392–414. doi: 10.1111/1477-9552.
12097
30. Hess GD, Shin K. Risk sharing by households within and
across regions and industries. J Monet Econ. (2000) 45:533–60.
doi: 10.1016/S0304-3932(00)00007-6
31. Government of Malawi. Malawi Poverty Report 2020. (2021). Available online
at: https://microdata.worldbank.org/index.php/catalog/3818/download/
51154
32. Khandker SR, Koolwal GB, Samad HA. Handbook on impact evaluation:
Quantitative methods and practices. World Bank Publ. (2009).
doi: 10.1596/978-0-8213-8028-4
33. Palmer-Jones R. Handbook on impact evaluation: quantitative methods and
practices, by SR Khandker, GB Koolwal and HA Samad. J Dev Effect. (2010)
2:387–90. doi: 10.1080/19439342.2010.499188
34. Angrist JD, Pischke J-S. Mostly Harmless Econometrics. Princeton, NJ:
Princeton University Press (2008).
Conflict of Interest: The authors declare that the research was conducted in the
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