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Conflicts increased in Africa shortly after COVID-19 lockdowns, but welfare assistance reduced fatalities

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Understanding how rises in local prices affect food-related conflicts is essential for crafting adequate social welfare responses, particularly in settings with an already high level of food vulnerability. We contribute to the literature by examining how rises in local food prices and the lockdowns implemented to contain the first wave of the COVID-19 pandemic affected conflict. We analyze real-time conflict data for 24 African countries during 2015–2020, welfare responses to COVID-19, changes in local food prices, and georeferenced data on areas with cultivation, oil, mines, all associated with differentiated risk of conflict. We find that the probability of experiencing food-related conflicts, food looting, riots, and violence against civilians increased shortly after the first strict lockdowns of 2020. Increases in local prices led to increases in violence against civilians. However, countries that timely provided more welfare assistance saw a reduction in the probability of experiencing these conflicts and in the number of associated fatalities. Our results suggest that providing urgent aid and assistance to those who need it can help reduce violence and save lives.
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Conflicts increased in Africa shortly after COVID-19 lockdowns, but welfare
assistance reduced fatalities
Roxana Gutiérrez-Romero
PII: S0264-9993(22)00234-6
DOI: https://doi.org/10.1016/j.econmod.2022.105991
Reference: ECMODE 105991
To appear in: Economic Modelling
Received Date: 21 March 2022
Revised Date: 22 June 2022
Accepted Date: 3 August 2022
Please cite this article as: Gutiérrez-Romero, R., Conflicts increased in Africa shortly after COVID-19
lockdowns, but welfare assistance reduced fatalities, Economic Modelling (2022), doi: https://
doi.org/10.1016/j.econmod.2022.105991.
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© 2022 Published by Elsevier B.V.
Conflicts increased in Africa shortly after COVID-19 lockdowns, but welfare assistance
reduced fatalities
Roxana Gutiérrez-Romero
Understanding how rises in local prices affect food-related conflicts is essential for
crafting adequate social welfare responses, particularly in settings with an already high
level of food vulnerability. We contribute to the literature by examining how rises in
local food prices and the lockdowns implemented to contain the first wave of the COVID-
19 pandemic affected conflict. We analyze real-time conflict data for 24 African
countries during 2015-2020, welfare responses to COVID-19, changes in local food
prices, and georeferenced data on areas with cultivation, oil, mines, all associated with
differentiated risk of conflict. We find that the probability of experiencing food-related
conflicts, food looting, riots, and violence against civilians increased shortly after the
first strict lockdowns of 2020. Increases in local prices led to increases in violence against
civilians. However, countries that timely provided more welfare assistance saw a
reduction in the probability of experiencing these conflicts and in the number of
associated fatalities. Our results suggest that providing urgent aid and assistance to those
who need it can help reduce violence and save lives.
Keywords: riots, violence against civilians; food-related conflict; food insecurity; effects
of welfare assistance; Africa; COVID-19.
JEL codes: D74, Q11, Q18, I38, J08.
Queen Mary University of London, School of Business and Management, London, UK.
r.gutierrez@qmul.ac.uk. I am grateful for the constructive and insightful comments
provided by the participants of the 16th annual Households in Conflict Network (HiCN)
workshop, in particular Tilman Brück, Leticia Pieraerts and Philip Verwimp.
1
Conflicts increased in Africa shortly after COVID-19 lockdowns, but welfare assistance
reduced fatalities
Understanding how rises in local prices affect food-related conflicts is essential for
crafting adequate social welfare responses, particularly in settings with an already high
level of food vulnerability. We contribute to the literature by examining how rises in
local food prices and the lockdowns implemented to contain the first wave of the COVID-
19 pandemic affected conflict. We analyze real-time conflict data for 24 African
countries during 2015-2020, welfare responses to COVID-19, changes in local food
prices, and georeferenced data on areas with cultivation, oil, mines, all associated with
differentiated risk of conflict. We find that the probability of experiencing food-related
conflicts, food looting, riots, and violence against civilians increased shortly after the
first strict lockdowns of 2020. Increases in local prices led to increases in violence against
civilians. However, countries that timely provided more welfare assistance saw a
reduction in the probability of experiencing these conflicts and in the number of
associated fatalities. Our results suggest that providing urgent aid and assistance to those
who need it can help reduce violence and save lives.
Keywords: riots; violence against civilians; food-related conflict; food insecurity; effects
of welfare assistance; Africa; COVID-19.
JEL codes: D74, Q11, Q18, I38, J08.
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1. Introduction
In response to the ongoing pandemic, several governments have implemented social distancing
measures and lockdowns. Although these measures have shown to be effective in curbing the
spread of the novel coronavirus, they have also caused significant economic, social and
political disruption (Barrett, 2020; Senghore et al., 2020). The socio-economic disruption in
the developing world risks increasing the already high levels of poverty even further. For
instance, right before the pandemic outbreak, one in every five people was suffering from
severe food insecurity in Africa, affecting nearly 277 million people. These vulnerable people
had run out of food, most likely experienced hunger, even gone for days without eating, putting
their well-being in great danger (FAO et al., 2019). As a result of the pandemic, several
forecasts predict that between 60-240 million people worldwide could be pushed into poverty,
depending on the efficiency in providing urgent and adequate relief to vulnerable citizens and
struggling businesses (Gutiérrez-Romero and Ahamed, 2021; Sumner et al., 2020). The sudden
loss of jobs and livelihoods for millions of people has caused food shortages and inflation - an
explosive combination for uprisings.
Major food supply chains have been a catalytic feature of many historical conflicts
ranging from the French Revolution until the violent unrest that eventually led to the Arab
Spring (Barrett, 2020). Aid, food programs, and cash transfers have also historically been
implemented to mitigate the risk of conflict, resulting in mixed findings. Sufficiently tailored
conditional cash transfers have been shown to demobilize combatants (Crost et al., 2016; Pena
et al., 2017). However, insurgent groups can also sabotage welfare interventions to maintain
their influence and further increase violence and conflicts (Berman et al., 2011; Nunn and Qian,
2014). Given that the ongoing pandemic has spread so quickly around the globe, it is likely that
some of the COVID-19 welfare assistance could be misused for clientelism and electoral
purposes, putting at risk its efficiency for reducing conflicts (Birch et al., 2020).
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This paper analyses three questions. First, we test whether social distancing measures,
and the lockdowns implemented to contain the first wave of the COVID-19 pandemic increased
the risks of internal conflicts. Second, we test the well-established hypothesis in the literature
on whether rises in local commodity prices can contribute to rises in food-related conflicts and
violence against civilians. Third, we test the role of social welfare programs, implemented after
COVID-19, in helping to reduce the risk of conflict increasing in the analyzed countries. We
focus on the 24 African countries for which we have monthly data on local prices and real-time
conflict data reported in the Armed Conflict Location and Event Data Project (ACLED) from
January 1 2015, until May 2 2020. We analyze riots, food-related conflicts and violence against
civilians, which refers to organized armed groups deliberately inflicting violence upon
unarmed non-combatants civilians in non-related political violence.
To assess the role of food vulnerability and conflict, we construct a monthly index of
local prices based on data from the Global Food Prices Database, which provides monthly
commodity prices at a sub-country level in nearly 1,000 local markets. We also construct an
overall welfare and labor index based on 12 different types of interventions implemented
worldwide to deal with COVID-19. We standardize this welfare and labor index to range from
0 (no intervention) up to 1 (the country has simultaneously implemented all 12 types of
interventions). These 12 types of intervention, compiled by Gentilini et al. (2020), can broadly
be grouped as those providing social assistance interventions, social insurance policies and
labor market interventions. In our regression analysis we also control for a wide range of
georeferenced socio-economic controls at the sub-country level for each country. The controls
at cell level of 55x55 km within each country are nightlight, mobile phone coverage of 2G-3G,
population size, percentage of mountains, percentage of forests, petroleum fields, mines,
diamond mines, electricity coverage, primary roads coverage, and infant mortality rate. We
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also use the size of area and percentage of cultivated land, at district-level, as well as an index
of ethnic diversity at country level.
We use panel random effects to estimate to what extent change in local prices and
COVID-19 interventions (e.g. social distancing, lockdown, welfare and labor policies) affected
the risk of internal conflicts. This panel random effects specification will yield unbiased effects
assuming that there are no strong sources of endogeneity. These specifications could be biased
if the analyzed government policies against COVID-19 were put into place in anticipation or
response to ongoing conflict. In other words, our result will be biased if government response
to the pandemic is not exogenous or independent from existing conflicts within each country.
To address this endogeneity concern, as a robustness check, we also use panel specifications
with instrumental variables.
As instruments we use the International Monetary Fund (IMF) overall commodity
monthly price index which helps to denote the severity of external fluctuations that might affect
how countries adopt different welfare and labor COVID-19 policies. We also include a series
of dummy variables denoting whether the country is a former British, French, Portuguese,
German, Belgian or American Colonization Society colony. We add information about the
former colonizer as the extent of the generosity of the COVID-19 response packages depend
on existing welfare structures and institutions likely shaped by colonial heritage (Nash and
Patel, 2019). We also use the male mortality rate attributed to household and ambient air
pollution and the percentage of diabetes prevalence among the adult population. These health
indicators are known to increase the risk of experiencing severe COVID-19 symptoms
(Fattorini and Regoli, 2020; Hussain et al., 2020), thus likely to influence the state’s decision
as to when to impose lockdowns.
The paper offers three key findings. First, there is no evidence that the early social distancing
measures implemented to mitigate the spread of COVID-19 fueled conflicts. These early
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interventions focused on containing the spread of COVID-19 without implementing partial or
full lockdowns. Examples of these early social distancing measures include banning some
international flights, banning large gatherings, closing restaurants, nightclubs, etc. In contrast,
full local lockdowns increased the probability of the analyzed countries experiencing riots,
food-related conflicts, and violence against civilians despite the global call for a ceasefire
during the ongoing pandemic (UN News, 2020). Second, food vulnerability, proxied by
increases in local prices is not associated to experiencing more riots or food-related incidents.
However, rises in local prices increased the probability of countries experiencing violence
against civilians, the state being involved in either instigating or responding to contain violence
against civilians, and the number of fatalities of food-related conflicts. For instance, a 10%
increase in the local price index is associated with a 0.7 percentage point increase in violence
against civilians.
Third, we also find that the urgent welfare and labor anti-poverty initiatives
implemented in response to COVID-19 reduced the probability of conflicts from emerging and
the associated fatalities. A wide range of COVID-welfare response policies were implemented,
with at least five simultaneous anti-poverty initiatives in the most active countries analyzed
here. Thus, it is not possible to disentangle in our analysis which specific action (if cash
transfers, relief for utility bills, extended pension benefits, etc.) was the most efficient in
reducing conflict. We nonetheless can ascertain that for every additional COVID-welfare
measure implemented, the probability of experiencing violence against civilians, riots and
food-related conflicts declined by approximately 0.2 percentage points. Our evidence resonates
with other studies that suggest welfare assistance can reduce the incidence of violent conflicts
if adequately tailored to local contexts (Crost et al., 2016). Thus, our findings offer important
insights on managing the short- and likely long-term effects of the pandemic on poverty and
conflict.
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2. Literature review
2.1 Social unrest and crime during lockdowns
The immediate concern for millions of people living in poverty during the first phase of the
pandemic, before the availability of vaccines for COVID-19, was not the new coronavirus
disease itself but surviving the economic hardship imposed by the lockdowns. In early March
2020, lockdowns were implemented worldwide. Shortly after, some crimes and conflicts
declined substantially such as urban crimes, although others increased such as domestic
violence and cybercrimes, exhibiting an important variation across cities worldwide (Nivette
et al., 2021).
1
In some sub-Saharan Africa countries such as the Democratic Republic of Congo,
Kenya, Uganda, and South Africa, there were reports that the police and army used in some
instances excessive force against citizens to implement lockdowns and disperse people to
reduce crowding (Bujakera and Ayenat, 2020). Even Senegal, where clashes between police
and civilians are rare, the first night of a national curfew was met with resistance from some
citizens leading to violence (AFP, 2020; France 24, 2020). There were also reports of clashes
over food shortages in countries such as Lesotho, South Africa, and Zimbabwe, as citizens that
suddenly lost their livelihoods due to lockdowns desperately tried to get access to food parcels
handed out by authorities (Burke, 2020). This type of food vulnerability and heavy-handed
1
For instance, robbery, assault and urban crime decreased soon after the lockdowns in the
Americas, Europe, the Middle East and Asia, as the restrictions imposed on population mobility
facilitated the spotting and arresting of suspects (Nivette et al., 2021; The Economist, 2020).
In contrast, increase in domestic violence after lockdowns was associated with increased
economic uncertainty, additional stress induced by prolonged co-habiting in confined spaces,
and reduced options for support (Usher et al., 2020).
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implementation of lockdowns is a volatile combination that risks increasing grievances and
social unrest.
2.2 Food vulnerability
Lockdowns also imposed tight mobility restrictions on workers in various sectors, including
farmers whose efforts to deliver essential food and basic staples were hampered in at least 33
of Africa’s 54 countries (Mutsaka, 2020). Although the first wave of the pandemic did not
disrupt the harvest per se, farmers in Africa were reported with rotting crops as lorries failed to
arrive due to lockdown restrictions (Barrett, 2020; George, 2020). The first lockdowns also
shut down many informal food markets where people earn their daily living, leaving large
segments of the population without the necessary provisions and with real prospects of having
not enough to eat. Moreover, school closures also affected nearly 370 million children
worldwide who risked missing out on school meals provided by the World Food Program
(WFM, 2020).
An extensive literature has described how sudden food insecurity can lead directly or
indirectly to violent riots and social unrest (Brück and d’Errico, 2019; Jones et al., 2017;
Raleigh et al., 2015; Rezaeedaryakenari et al., 2020). According to this literature, at least three
critical channels explain how food vulnerability could increase riots and violence against
civilians. These mechanisms are also likely to play a role during the ongoing pandemic. First,
at the individual level, food vulnerability deprives people of their most basic human rights,
increases grievances and highlights differences in food entitlements among the population
(Hendrix and Brinkman, 2013; Jones et al., 2017). Survival instincts and grievances reduce the
opportunity costs of engaging in riots, food looting or joining rebel groups that criminal groups
exploit. For instance, during the pandemic, mafias in Italy and Mexico offered food and
“COVID-19 support packages” to potential recruits (Jones and Hale, 2020; Tondo, 2020).
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Similar tactics exploiting food vulnerability have been adopted in the sub-Saharan African
context (Humphreys and Weinstein, 2008). In South Africa, for instance, gangs negotiated an
unprecedented truce in Cape Town to stop their conflicts and provided food to households in
townships (BBC, 2020).
Second, at the rebel group level, food vulnerabilities also directly impact the group’s
ability to mobilize resources to support activities. Some rebel groups might have lost
substantial revenues from the sudden drop in prices of natural resources, which they might have
illegal access to, such as oil. With such falls in profits, rebel groups have higher incentives to
victimize ordinary citizens seeking resource appropriation, such as food. The areas with the
largest share of cultivation are most susceptible to such rebel tactics, particularly during food
shortages (Rezaeedaryakenari et al., 2020). Third, at the national level, the government has a
crucial role in dealing with food vulnerability and food-related conflicts. Governments might
use excessive violence against civilians to prevent further violent clashes and enforce strict
lockdowns, depending on their ability to provide adequate and urgent humanitarian support to
struggling families during quarantines and tactfully manage potential unrest.
2
Based on the
above discussion, we formulate the following two hypotheses on conflict.
H1: The lockdowns implemented to contain the first wave of the pandemic increased the
probability of riots, violence against civilians and food-related conflicts.
2
Theoretically, producers could benefit from an increase in prices, but most producers are net
consumers of food in the African context. Hence rises in local and international prices make
producers worse off, given the higher net cost of the food basket (Lee and Ndulo, 2011). This
negative effect is likely to be more dominant for most African states since they are neither
major importers nor exporters (Raleigh et al., 2015).
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H2: Rises in local commodity prices increased the probability of riots, violence against
civilians and food-related conflicts.
2.3 COVID-19 welfare assistance
From the vast literature on conflict we know a great deal about how economic crises and shocks
increase civil conflicts, riots and violence against civilians (Blattman and Miguel, 2010; Miguel
et al., 2004). Related literature offers mixed evidence on the extent to which foreign aid can
reduce the incidence of conflicts. Various studies have found that aid can reduce conflicts as it
increases popular support for governments and increases the cost of opportunity of joining
rebel and insurgent groups (Berman et al., 2011; de Ree and Nillesen, 2009; Nielsen et al.,
2011). However, other studies have also found that (food) aid can increase the incidence and
the duration of civil conflicts (Nunn and Qian, 2014). Anti-poverty transfers such as
community-driven programs and food aid supplies have also been found to increase the
intensity of conflicts (Crost et al., 2014) as insurgent groups sabotage these programs to prevent
weakening their ability to recruit future members. A similar positive association has been found
between increased conflict and rural employment programs (Khanna and Zimmermann, 2014).
A small but growing strand of the literature has also studied the link between
conditional cash transfers and conflict. The evidence is again somewhat mixed. The literature
suggests that anti-poverty programs that are sufficiently tailored to local contexts can reduce
the capacity of insurgents to recruit combatants from villages (Labonne, 2013), and increase
the cost of opportunity of joining illegal activities in settings with long-entrenched civil
conflicts (Pena et al., 2017). Nonetheless, it is unclear the extent to which countries with high
rates of extreme poverty and exacerbated food vulnerability due to lockdowns will respond to
the urgent and wide range of welfare and labor COVID-19 assistance packages. Many of the
urgent welfare packages introduced are unconditional cash transfers that have been shown to
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reduce food vulnerabilities and poverty in sub-Saharan Africa and other developing regions,
but with a lesser-known effect on conflict (Chakrabarti et al., 2020; Tiwari et al., 2016). Based
on this analysis, we draw the following hypothesis.
H3: Rapid welfare and labor response to COVID-19 reduced the probability of riots, violence
against civilians and food-related conflicts.
3. Data
3.1 Data on conflict
To analyze conflict events we use the Armed Conflict Location and Event Data Project
(ACLED). ACLED provides georeferenced data at the sub-country level. This information is
available by day and month on all reported political violence and protests around the globe.
Specifically, ACLED provides information about six types of conflicts: battles, explosions
(suicide bombs, grenades), violence against civilians, protests, riots and strategic developments
(e.g. non-violent actions on agreements, arrests and disrupted weapons use). The sources of
ACLED include government reports, local media, humanitarian agencies, and research
publications (Raleigh and Dowd, 2016).
We analyze exclusively three of types of conflict events: Riots, violence against
civilians, and food-related conflicts. Our period of analysis is limited to January 1 2015 to May
2 2020. Riots, as defined by ACLED, are violent forms of demonstrations. Violence against
civilians is defined as an armed or violent group deliberately attacking unarmed civilians in
non-related political violence (Raleigh and Dowd, 2016). Governments, rebels, militias, and
rioters can be involved in these violent acts against civilians, including attacks, abductions,
forced disappearances, and sexual violence. We identify food-related conflicts based on the
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detailed description of each of the events reported in ACLED. These events refer to conflicts
related to food including, livestock, cattle, and agriculture.
We analyze all the riots, violence against civilians and food-related conflicts reported
in ACLED on a daily basis. This fine level of granularity as to when and where the conflicts
took place allows us to exploit the variation with which early social distancing measures,
lockdowns and welfare/labor COVID-19 policy responses were implemented across countries
during the first wave of the pandemic.
3.2 Dates of early social distancing and full lockdowns
We take into account the exact date on which the first social distancing measure was
implemented in each of our analyzed countries. These earlier social distancing measures
consisted mostly of banning some international flights, having additional health screening at
borders, banning large gatherings, closing restaurants, night clubs, etc. We also include the date
in which full lockdowns were introduced in each country. The dates from the first early social
distancing and lockdowns are taken from the publicly available data on COVID-19
Government Response Tracker (OxCGRT) by Hale et al., (2020).
3
At the time of writing this
3
OxCGRT provides information on eight types of social distancing measures implemented in
response to COVID-19 across 149 countries, since January 2020. These measures include
international travel restrictions, limitations on internal movement, closure of schools, closure
of workplaces, cancellations of public events, restrictions of large gatherings, requirements to
stay at home, and restrictions on public transport. OxCGRT includes the date on when each of
the social distancing measures were implemented. This database also provides an ordinal value
of 1-4 to each of the eight social distancing implemented that helps to ascertain the level of
their severity (Hale et al., 2020).
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paper, OxCGRT did not include data on social distancing measures for 13 African countries
(Benin, Burundi, Central African Republic, Equatorial Guinea, Eritrea, Guinea, Guinea-Bissau,
Ivory Coast, Liberia, Republic of Congo, Senegal, Somalia and Togo).
4
For all these 13
countries, we took information on the exact date of early social distancing and lockdowns from
ACAPS (2020). From this database, we also took the period of the lockdown in Nigeria. Table
A1, in the Appendix, lists the date when early social distancing and lockdowns were introduced
for each of the analyzed countries.
3.3 Constructing a monthly local index of prices at the market level
To measure the link between food vulnerability and conflict we use the Global Food Prices
Database (WFP) data. This dataset reports monthly commodity prices at a sub-country level,
across 985 local markets, in 23 African countries from the 1990s until May 2020, for which
there is also information on conflicts in ACLED. We add information for Zimbabwe not
included in WFP from the USAID FEWS-NET dataset that provides monthly local food prices.
We focus our analysis on the 24 African countries listed in Table A1 that shows the countries
for which we have data on local prices from January 1 2015 until May 2 2020.
There is a wide range of variance in the type of goods that each local market sells. This
variance in goods reflects partly differences in consumers diet, staple food, preferences,
budget, trading barriers, and seasonal produce in each area. This variance in produce sold at
local level should be considered if seeking to assess how changes in local prices affect
consumers and local violence. An alternative approach could be to set the same basket of staple
4
We do not have data on local prices for all these additional countries, but the dates of their
lockdowns help doing the preliminary spatial analysis as well as the regression discontinuity
plots presented.
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goods such as wheat, rice, beans, etc. across all countries analyzed. Unfortunately, such a
homogenous basket of staple food will not represent the wide variance in commodity goods
consumed within and across countries. For that reason, the literature has preferred to analyze
instead the change in prices of the most frequent commodity sold within each local market
(Raleigh et al., 2015). We follow such an approach here by constructing an index of monthly
prices of the most frequent commodity within each market.
5
In our econometric analysis, we
take January 2015 as the base for the index for each market, which allows us to assess to what
extent the index of local prices has changed since then on a monthly basis. For each conflict
reported in ACLED, we add the local price index of their closest food market within the same
month, year and country where the conflict took place.
3.4 Constructing an index of welfare and labor COVID-19 policy
By the period of our analysis, May 1 2020, a total of 159 countries had implemented some sort
of welfare and labor COVID-19 policy. We constructed an overall welfare and labor index
based on 12 different types of interventions implemented worldwide to deal with COVID-19.
These interventions can be grouped broadly into three broad categories. The first one, social
assistance interventions include: cash-based transfers, public works, in-kind/school feeding
and utility/financial support. The second, social insurance policies include: paid
leave/unemployment, health insurance support, pensions and disability benefits and social
security contributions. The last one, labor market interventions, include wage subsidy, training,
5
For each market we construct a consumer price index as the sum of the total expenditure of
most common items sold by multiplying price by quantity and adding them. The basket
compared in each market is such that it can be comparable over time. Then we divide the
monthly consumer price index by the value of the index in the base year (January 2015).
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labor regulation and reduced work time subsidy. We use a simple additive unweighted index
to measure the whole range of various welfare and labor COVID-19 policy responses.
6
We
standardize this index to range from 0 (no intervention) up to 1 (the country has simultaneously
implemented all 12 types of interventions). In practice, the overall index ranges from 0 to
slightly above 0.4 (with five ongoing policies).
We use Gentilini et al., (2020) to identify which welfare and labor policy each of the
countries analyzed had implemented in response to COVID. We use this source as it offers the
most extensive list of actions and programs taken in each country. Gentilini et al., (2020) do
not report the date on when these interventions were first put into place. So instead, we identify
the date when the first economic intervention against COVID-19 was implemented, as reported
by Hale et al., (2020).
7
6
Various methods can be used to create composite indices such as additive, multiplicative and
weighting some aspects with principal components analysis (Hale et al., 2020). We use the
additive method as there are few interventions which might not merit using principal
component analysis. We are not interested either in which policy explains the most variance in
responses, rather to simply come with an index that measures the whole range of interventions
in each country, which has the advantage of being simpler to interpret.
7
Hale et al., (2020) also report the type of economic interventions that countries implemented
to help their population in response to COVID-19, but in a much more aggregated way than
Gentilini et al., (2020), and aggregating these actions broadly as income support, debt and
contract relief for households, and other fiscal measures.
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Fig. 1. COVID-19 policy response index across Africa, as of May 1 2020
Note. Own estimates using Gentilini et al., (2020).
Table A2 in Appendix lists the welfare and labor policies implemented in each of the
24 African countries analyzed. From the 19 countries with an ongoing COVID-19 welfare and
labor policy, 12 have provided cash transfers (among other policies), while the other seven
have provided utility and financial support. Labor interventions are the least used thus far.
Among the 24 countries analyzed, only Egypt has adopted recent labor regulations.
3.5 Other controls
We also include a wide range of control variables to mitigate potential confounding or
unobserved characteristics based on the extensive literature on conflict. In Table A3, we list
the sources, sub-country level for each variable particularly if taken from satellite data which
allowed us to get controls for small cell grid of 55x55km within each country. Focusing on
small-area cells within countries, instead of administrative boundaries, offers wider array of
socio-economic, population, and other geographical characteristics, that might explain the
0.1 .2 .3 .4
Index of welfare and labour COVID-19 policy response
Burundi
Cameroon
Central African Republic
Democratic Republic of Congo
Djibouti
Equatorial Guinea
Eritrea
Gabon
Ivory Coast
Lesotho
Mozambique
Tanzania
Zambia
eSwatini
Angola
Benin
Chad
Gambia
Guinea-Bissau
Libya
Malawi
Niger
Sierra Leone
Somalia
South Sudan
Sudan
Togo
Zimbabwe
Botswana
Egypt
Guinea
Kenya
Liberia
Mali
Mauritania
Namibia
Republic of Congo
Senegal
Burkina Faso
Ghana
Madagascar
Morocco
Nigeria
Uganda
Ethiopia
Rwanda
South Africa
Algeria
Tunisia
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reasons behind local conflict events at small-area scale reported in ACLED. The controls at
cell level, drawn from the publicly available georeferenced data from Manacorda and Tesei
(2020) within each country are: mobile phone coverage of 2G-3G, population size, percentage
of mountains, percentage of forests, petroleum fields, mines, diamond mines, electricity
coverage, primary roads coverage, and infant mortality rate. Mobile phone coverage has been
found crucial for political mobilization and riots as it facilitates mass political mobilization
(Manacorda and Tesei, 2020). Similarly, poor density of roads has been found important for
triggering conflicts in Sub-Saharan Africa as it hinders the ability of the security forces to
quickly react to outbursts of violence and discourage rebel and communal conflicts (Detges,
2016). Uneven provision of infrastructure could trigger social unrest as it also signals
favouritism to certain regions by the government (Burgess et al., 2015). Similarly, high infant
mortality could increase conflict due to increased grievances among the population (Collier
and Hoeffler, 2014). Population size and mountains are important controls used in the empirical
conflict literature (Miguel et al., 2004). These variables reflect the size of the potential
population that can engage in conflict, and potential areas where rebels could hide if conflict
is triggered (Collier and Hoeffler, 2014). All of our other natural resource controls help us to
assess the increased risk of conflict due to greed, increased in the presence of natural resources
(Berman et al., 2017; Collier and Hoeffler, 2014; Fenske and Zurimendi, 2017).
We also use a proxy for indicator of wealth, at the cell level for each country. Given
the lack of detailed income or consumption by household surveys in the region, we use satellite
data on nightlight. Nightlight has been used by several other studies as a proxy of economic
activity, functionality of critical infrastructure, and income levels, particularly for countries
where there is no reliable data at small-area level (Alsan, 2015; Bonardi et al., 2021; Sathe et
al., 2021). We use the monthly average of the stable nightlight luminosity from the DMSP-
OLS Nighttime Light at the district level, from the USA Air Force Weather Agency. To avoid
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potential endogeneity issues due to reverse causality, we use the monthly nightlight for 2015
only.
We also use the logarithm of the cultivated district and size of the area (district) taken
from the publicly available data from Rezaeedaryakenari et al., (2020). We include these
because with economic shocks as pronounced as those seen during the pandemic, some rebel
groups might have higher incentives to victimize ordinary citizens seeking resource
appropriation, particularly food. Thus, the areas with the largest share of cultivation are most
susceptible to such rebel tactics (Rezaeedaryakenari et al., 2020). Lastly, at the country level,
we include the ethnolinguistic fractionalization index, which measures the probability that two
randomly drawn individuals within a country are not from the same ethnic group. We add this
variable as several studies have found that ethnic diversity increases the risk of incidence of
armed conflict and civil conflicts (Collier and Hoeffler, 1998; Wegenast and Basedau, 2013),
although this evidence is not conclusive across all studies (Miguel et al., 2004). Still, ethnic
diversity is an important element to consider as it could have triggered conflict if government
response to COVID-19 was biased toward certain groups.
3.6 Instrumental variables
Social distancing measures, lockdowns and welfare/labor COVID-19 policy responses have
been implemented to mitigate the spread of COVID-19, and in some countries potentially to
mitigate the risk of violence erupting or escalating. For this reason, policy responses to COVID
could be endogenous dependent on exiting conflicts, and unlikely to be exogenous
interventions. To mitigate potential endogeneity concerns our econometric specification uses
instrumental variables.
Our panel specifications use four instruments. We use the male mortality rate attributed
to household and ambient air pollution per 100,000, based on standardized age, at the national
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level for the year 2016. Another instrument is the percentage of diabetes prevalence among the
adult population (aged 20-79) at the national level over the years 2015-2019. These two health
indicators increase the risk of experiencing severe COVID-19 symptoms (Fattorini and Regoli,
2020; Hussain et al., 2020), and could have influenced governments’ decisions on when to
implement lockdowns. We also include the IMF overall commodity monthly price index over
the years 2015-2020. This index is representative of the global commodity market, including
food, agriculture, fuel and non-fuel prices, and is determined by the largest import market of a
given commodity. This overall index helps to denote the severity of external fluctuations that
might have affected how countries adopted different welfare and labor COVID-19 policies.
The extent of the generosity of these packages is likely to depend on existing welfare structures
and institutions, thus is likely shaped by colonial heritage and which country was the former
colonizer (Nash and Patel, 2019). Thus, we also include a series of dummy variables denoting
whether the country is a former British, French, Portuguese, German, Belgian or American
Colonization Society colony. Table A3 lists the sources of these instruments.
3.7 Conflicts
Before we present our econometric analysis, we make a pause to show the trend in the conflicts
reported in ACLED for the entire African continent from January 1 2015, until May 2 2020.
We present these conflicts using regression discontinuity plots. Figures 2, 3 and 4 show the
polynomial fit that represents the behavior of the underlying conditional expectation of the
outcome variable, in our case, the incidence of conflict before and after the lockdown. The red
vertical line represents the beginning of the lockdown. As standard in this kind of regression
discontinuity plots, each dot represents a collection of local sample means of the outcome
variable within each bin (Calonico et al., 2015). These figures show that riots, violence against
civilians, and food-related conflicts had a flat and slight downward trajectory in the previous
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five years before the lockdowns. However, there is a clear jump in the incidence of these
conflicts immediately after the lockdowns were implemented.
Fig. 2. ACLED’s riots before and after lockdown
Fig. 3. ACLED’s violence against civilians before and after lockdown
0.1 .2 .3 .4
-2000 -1500 -1000 -500 0
Sample average within bin Polynomial fit of order 3
Riots
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Fig. 4. ACLED’s food-related violence before and after lockdown
0.1 .2 .3 .4 .5
-2000 -1500 -1000 -500 0
Sample average within bin Polynomial fit of order 3
Food-related violence
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Fig. 5. Spatial distribution of violence against civilians, riots and food-related violence
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In Figure 5 we illustrate instead the spatial distribution of riots, violence against
civilians and food-related violence for the entire African continent. We focus on three
periods. The panel at the top shows the episodes of violence that occurred between the
date of the first lockdown of 2020 (around March 2020), and May 2020. The panel in
the middle shows the episodes of violence for exactly the same dates as the panel on
top, but for the previous year of 2019 (around March-May 2019). The panel at the
bottom shows the episodes of violence that occurred from January 1, 2015, until the
first lockdown of 2020.
Figure 5 reveals two patterns. First, regions that experienced conflicts soon after
the 2020 lockdowns tended to have conflicts in the past. This spatial correlation
suggests that there are underlying conditions in these areas that makes them more
vulnerable to violence. Second, conflicts that erupted after lockdowns are more
spatially concentrated in areas with a higher share of cultivated land (denoted by a
darker color in the right-hand maps). This spatial correlation between conflict and
cultivated land had been noted earlier, in a pre-COVID study by Rezaeedaryakenari et
al., (2020). These authors explain that areas with more cultivation tend to have more
conflict because they provide greater utility for forcible appropriation by rebels to
acquire food.
Since we are concerned with the role of food vulnerability, the rest of our
analysis focuses exclusively on the 24 African countries for which we have data on
local food prices. Table A4 summarizes the incidence of ACLED conflicts across these
24 countries from January 1 2015, until May 2 2020. During that period, there were
42,010 conflicts reported, including battles, explosions (e.g. suicide bombs, grenades),
violence against civilians, protests, riots, and strategic developments. Just over a quarter
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(28%) of these events were violence against civilians, and 13% were riots, with a
minority of food-related conflicts and food looting (2%). The state was reported to be
involved as an actor in these conflicts, either instigating or responding to contain
violence, in nearly 32% of all reported ACLED conflict cases. The state involved as an
actor refers to military, police, government or government guard interventions. The
total and average number of the fatalities per event are also reported in Table A4. There
were 169,454 fatalities associated with any conflict reported in ACLED, from January
1 2015 until May 2 2020. There were 4,552 fatalities related to riots, 50,506 fatalities
related to violence against civilians, 6,888 fatalities associated with any food-related
conflicts (including food looting), and 4,344 fatalities related to food looting.
Figure 6 plots the relationship between violence against civilians and local food
prices and the IMF global commodity index. That figure also reports the Pearson
correlation coefficient between violence against civilians and local prices (correlation
represented by parameter r) and with IMF global commodity index (correlation
represented by parameter s). Again, we focus only on the 24 countries for which we
have information on local food prices. Only for Figure 6, we aggregate the data at
monthly level for each country. We also standardize each of the three depicted variables
such that their monthly average is divided by the maximum value of each variable for
the entire series. Thus, the y-axis shows how much the monthly series fluctuates from
the highest level achieved within each country. The x-axis shows the beginning of each
year from 2015 to 2020. The labels of the x-axis for all countries are shown only for
the bottom panel of Figure 6 (to avoid over-cluttering the information).
The IMF global commodity index, denoted by the dotted line in Figure 6, is the
same for all countries, thus has the same trends for all countries. In contrast, the local
food price index exhibits different variation within countries. In some cases, like
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Mauritania, the index of local food prices has very little variation. In other countries,
such as Ghana or Zimbabwe, local prices exhibit an erratic pattern of ups and downs.
The difference in variation in local prices is likely due to a wide range of local factors
such as weather fluctuations, changes in local demand and supply of food. Figure 6 also
shows that for some countries, such as Central African Republic, Ethiopia, and Mali
there is a particularly strong correlation between local food prices and violence against
civilians. This correlation coefficient for such countries is -0.35, 0.36, and 0.40
respectively. However, there are many cases where there is a much weaker correlation
between local prices and violence against civilians. That is the case of Mauritania,
Guinea, and Malawi, with correlation coefficients is -0.04, 0.02, and 0.06 respectively.
This evidence might suggest that raises in food prices might have contributed to some
conflicts.
Note: r=correlation between incidence of violence against civilians and local food price.
s=correlation between incidence of violence against civilians and IMF commodity
price.
Fig. 6. Monthly prices and ACLED’s violence against civilians
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4. Method
We use two econometric specifications to estimate the impact of price volatility, early
social distancing measures, full lockdowns, and COVID-19 welfare policy response on
conflict. First, we use a random panel effects (RE) model, as shown in equation (1). We
use the RE model because it can simultaneously model both time-variant and time-
invariant effects (Bell and Jones 2015). The panel RE specification also has the main
advantage of handling hierarchical data, in our case, having repeated observations in
sub-country level cells, nested within countries, the higher-level fixed units. Being able
to model this kind of panel data is the reason why the RE model is also known as the
multilevel, hierarchical or mixed model.
conflictjit = + 1Sit + 2log local pricejit + 3Xji + 4Ci + (
ji +
jit) (1)
We focus on the incidence of four types of conflicts (conflictjit): riots, violence
against civilians, food-related conflict incidents and food looting that occurred at the
cell area j (with reported latitude and longitude in ACLED) located in country i at time
t (which includes the day, month and year). Our dependent variable is binary for each
of the four types of conflicts analyzed.
8
Sit is a vector that includes the three COVID-
8
We also use a linear RE model to measure the change in probability of change in
conflict as it is the best way to practically address the hierarchical structure of our panel
data, and test for endogeneity simultaneously. Unfortunately, modern statistical
software such as Stata do not have packages capable to estimate logit or probit models
using instrumental variables, and deal with hierarchical data with panel random or fixed
effects. The only way to estimate logit or probit models and test for endogeneity with
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19 interventions we focus on: the date of the first social distancing measure
implemented in the country, a lockdown dummy variable that takes the value of 0 or 1
depending on if the conflict occurred before or after the local lockdown implemented
in each country, and the welfare and labor COVID-19 policy index in country i
implemented at day, month, year t. As mentioned before, this policy index has been
normalized to take the value of 0 when no intervention has been implemented up to 1
if the country has simultaneously implemented all potential 12 welfare/labor
interventions reported worldwide post-COVID. Since this index is a continuous
variable, we can ascertain whether increases in the value of the index, leads to a change
in the probability of experiencing conflict.
9
The monthly local price index measured in logarithm (log local price) at cell j
in country i ranges from January 2015 until May 2 2020. Xji is a vector that captures our
panel data would be to pool the data and cluster the standard errors by countries.
Unfortunately, this approach is very likely to introduce important biases as we have
very few countries, 24, below the recommended number to cluster errors. Pooling the
data also has the main shortcoming of treating observations as independent of each
other. Thus, pooling the data would ignore, for instance, the date in which conflicts are
occurring over time, and would be impossible to detect within unit variation.
9
An alternative way to analyze this policy index is to construct a categorical variable,
by for instance focusing on those countries implementing exclusively social assistance,
insurance policies or labor market interventions. Unfortunately, most countries
implemented a combination of these policies simultaneously, and with different
amounts of cash or benefits. Thus, it is not possible to create meaningful mutually
exclusive categories of policy responses across countries.
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controls at the cell j located in country i and includes: the percentage of mountains,
forests, whether the cell has petroleum fields, mines, diamond mines, and area size
(district level). In addition, vector Xji includes some key variables lagged in time to
mitigate potential endogeneity issues. These lagged variables are the stable nightlight
(measured in logarithm for the year 2015), the percentage of mobile phone coverage in
2G-3G, the percentage of electricity coverage, primary roads coverage, population,
infant mortality, percentage of land cultivated. Vector Ci includes the ethnolinguistic
fractionalization index, for country i. This ethnolinguistic index is time-invariant for
countries. The reason why we are unable to include further country-fixed effects
dummy variables in the regression is they get eliminated due to multicollinearity with
our other time-invariant controls. (
ji +
jit) denotes the time-invariant and time-variant
error term. The results of the RE specifications are shown in Table 1, columns 1-4.
The RE estimates will be unbiased if there are no strong sources of endogeneity,
such as omitted variables due to unobserved heterogeneity. However, we suspect that
the RE specifications could be biased given that governments might have implemented
social distancing measures, lockdowns and COVID-19 welfare responses in
anticipation or response to ongoing conflicts. To address this endogeneity concern, we
use panel RE with instrumental variables. Equation (2) shows the first-stage regression,
where we instrument our three likely endogenous variables: the date of the first social
distancing measure, whether the conflict event occurred before or after the lockdown
and the welfare and labor COVID-19 policy index, all denoted by vector Sit. Our
instruments are represented by vector Zit. These instruments are male mortality rate
attributed to household and ambient air pollution per 100,000 (lagged for the year
2016); diabetes prevalence (% of population ages 20 to 79, years 2015-2019); the IMF
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commodity price index (years 2015-2020); and whether the country is a former British,
French, Portuguese, German, Belgian or American Colonization Society colony.
Sit = + 1Zit + 2log local pricejit + 3Xji + 4Ci + vjit (2)
Equation (3) shows the second-stage regression of estimating the impact of the
instrumented endogenous variables on the incidence of conflict. The terms (ji +
jit) in
equation (3) represent the time-invariant and time-variant error terms.
conflictjit = + 1Ŝit + 2log local pricejit + 3Xji + 4Ci + (ji +
jit) (3)
Table A5 in the Appendix shows the first-stage regression, and all instruments
are strongly correlated to the instrumented variables. The F-statistic of the excluded
instruments is well above 10 for all regressions. The results of the second-stage IV-
2SLS regression are reported in Table 1, in columns 5-8. At the bottom of that table,
the Sargan-Hanssen overidentification tests suggest that the instruments are valid. Table
1, also shows the Hausman endogeneity tests which suggest there is evidence of
endogeneity in columns 6 and 7 (violence against civilians and food-related incidents).
This endogeneity test suggest that the second-stage IV 2SLS panel RE regressions
should be preferred to the panel RE without instruments.
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5. Results
5.1 Riots, violence against civilians and food-related conflict
Table 1 shows that the early social distancing measures do not affect the probability of
conflicts occurring. That is the case if using random specifications RE with and without
instrumental variables (Table 1, columns 1-8). This non-statistically significant effect
is unsurprising since many of these early measures did not impose any mobility
restrictions on the population but mostly focused on having some travel restrictions on
journeys from abroad and avoiding overcrowding. The stricter lockdown measures to
contain the first wave of the COVID-19 pandemic yield different results. If focused on
the IV-2SLS results, Table 1, columns 5-8, show that the probability of experiencing
riots, violence against civilians, food-related conflicts and food looting increased after
lockdowns, as our earlier figures 2, 3 and 4 had shown. Thus, these findings support
our first hypothesis.
With regards to food vulnerability, we find that a 10% increase in the value of
the local price index is associated with a 0.71 percentage point increase in violence
against civilians. The same results are obtained when using the panel RE specifications
with or without instrumenting (Table 1 columns 2 and 6). To depict more clearly this
association, Figure 7 shows the marginal effects between local prices and violence and
incidence of violence against civilians. This figure shows a positive association
between rises in local prices and violence against civilians is present regardless of the
value of the index of local price, whether at low or high value across all our sample.
10
10
It is worth noting that the logarithm of a positive number may be negative or zero. In
our case, the logarithm of the local price index ranges from -5.0 to nearly 9.0.
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Our evidence also suggests that rises in local prices is not associated with riots,
food-related incidents or food looting. The same results are obtained when using the
RE specifications with or without instrumenting (Table 1 columns 1, 3, 4, 5, 7 and 8).
Thus, we find mixed support for our second hypothesis. Our findings suggest that riots
obey factors other than rises local prices. For instance, riots are more likely to occur in
more urbanized settings with higher levels of stable nightlight, electricity coverage,
population, and mobile phone coverage (Table 1, columns 1 and 5). The reason why
these urbanized areas might be more likely to experience riots can perhaps be found in
Manacorda and Tesei (2020). These authors in a pre-COVID study in sub-Saharan
Africa conclude that mobile phone coverage is instrumental for mass mobilizations. But
this type of mobilization only occurs during economic downturns as it accentuates
existing grievances and the cost of participation fall. Our results also suggest that riots
also are more likely to emerge in areas with potential grievances, as proxied by less
density of primary roads as this low density could reflect low government spending in
public infrastructure or across groups (Burgess et al., 2015).
Table 1 also suggests that violence against civilians seems to be concentrated in
less urbanized settings as they have lower levels of stable nightlight, less electricity
coverage, primary roads, but more cultivated land and mines (Table 1, columns 2 and
6). Similarly, food-related incidents and food looting are more likely to occur in areas
with a greater density of cultivated land (column 7 and 8), as Figure 5 suggested earlier.
This is an important finding. Although the volatility of local prices is not associated
with food-related conflicts, these high cultivated areas are more at risk of experiencing
these events. Earlier literature has suggested that highly cultivated areas are at higher
risk of conflict as rebel groups have increased incentives to victimize ordinary citizens
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seeking resource appropriation, such as food, when the profits of these rebel groups fall
(Rezaeedaryakenari et al., 2020).
The IV-2SLS specifications show that the welfare and labor COVID-19 policy
index has reduced the probability of riots, violence against civilians and food-related
conflicts, including food looting. Thus, our findings support our third hypothesis. For
instance, Figures 8, 9 and 10 show the marginal effect of the probability of experiencing
riots, violence against civilians, and food-related conflicts with the welfare and labor
COVID-19 policy index values. These marginal effects depict the IV-2SLS
specifications in Table 1, columns 5-7. The effect of the index is negative and linearly
associated with the probability of experiencing riots. Specifically, with a 0.1 unit
increase in the welfare/labor COVID-19 policy index, the likelihood of experiencing
these conflicts declines by nearly 0.2 percentage points.
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Table 1
COVID-19 interventions, local prices and conflict
Note: Significant at the *** p<0.01, ** p<0.05 and * p<0.1 levels.
(1) (2) (3) (4) (5) (6) (7) (8)
Riots
Violence against
civilians
Food-related
incidents
Food looting Riots
Violence against
civilians
Food-related
incidents
Food looting
First social distancing im plemented 0.002 -0.001 -0.000 -0.000 0.002 -0 .003 -0.001 -0.000
(0.001) (0.001) (0.000) (0.000) (0.004) (0.003) (0.001) (0.001)
Strict lockdown 0.020* 0.078*** 0.02 1*** 0.009** 0.154*** 0 .110* 0.127*** 0.050 ***
(0.010) (0.014) (0.005) (0.004) (0.045) (0.060) (0.022) (0.017)
Index of welfare and labor COVID19 response -0.048 0.066 -0.048** -0.030* -0.666** -2.124*** -0.886*** -0.394***
(0.048) (0.063) (0.023) (0.018) (0.282) (0.378) (0.138) (0.108)
Log index local market price -0.004 0 .073*** -0.001 -0.00 0 -0.004 0.071*** -0.000 -0.000
(0.005) (0.006) (0.002) (0.002) (0.005) (0.006) (0.002) (0.002)
Log stable nightlight (year 2015 ) 0.012 *** -0.051*** 0.002 0.00 2 0.013*** -0.049 *** 0.003* 0.003*
(0.004) (0.005) (0.002) (0.001) (0.004) (0.005) (0.002) (0.001)
Log mobile phone coverage 2G-3G 0.027*** -0.00 5 -0.003*** -0.003*** 0.027*** -0.005 -0.003** -0.003***
(0.002) (0.003) (0.001) (0.001) (0.002) (0.003) (0.001) (0.001)
% Mountains -0.056*** 0.035*** -0.01 0*** -0.009*** -0.057*** 0.029*** -0.01 1*** -0.009***
(0.008) (0.010) (0.004) (0.003) (0.008) (0.010) (0.004) (0.003)
% Forests 0.035*** -0.039*** -0.044*** -0.031*** 0.035*** -0.043 *** -0.044*** -0.031***
(0.010) (0.013) (0.005) (0.004) (0.010) (0.014) (0.005) (0.004)
Petroleum fields 0.023** 0.062*** -0.00 7 -0.004 0.02 1** 0.05 5*** -0.010** -0.00 5
(0.009) (0.012) (0.004) (0.004) (0.009) (0.013) (0.005) (0.004)
Mines -0.010*** 0.021*** -0.002 -0.001 -0.010*** 0 .020*** -0.00 3* -0.001
(0.003) (0.004) (0.001) (0.001) (0.003) (0.004) (0.001) (0.001)
Diamond mines 0.003 -0.017** -0.001 -0.001 0.004 -0.015 ** -0.000 -0.00 0
(0.005) (0.007) (0.003) (0.002) (0.005) (0.007) (0.003) (0.002)
Size of area 0.000 0.000** 0.000*** 0.000*** 0.000 0.000 ** 0 .000*** 0.000***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Electricity 0.045*** -0.061*** -0.00 9*** -0.006*** 0 .046*** -0.063*** -0.0 09*** -0.006***
(0.006) (0.007) (0.003) (0.002) (0.006) (0.007) (0.003) (0.002)
Primary roads -0.011*** -0.016*** -0.003** -0 .003*** -0.011*** -0.019*** -0.003** -0.003***
(0.003) (0.004) (0.001) (0.001) (0.003) (0.004) (0.001) (0.001)
Log population 0.010*** -0.003 -0.00 2* -0.001 0.010*** -0.001 -0.001 -0.00 1
(0.002) (0.003) (0.001) (0.001) (0.002) (0.003) (0.001) (0.001)
Log infant mortality rate 0.000 -0.140*** 0.035*** 0.026*** 0.002 -0.147*** 0.0 39*** 0.029***
(0.011) (0.014) (0.005) (0.004) (0.011) (0.015) (0.005) (0.004)
Log cultivated -0.001 0 .025*** 0.014*** 0.010*** -0.001 0.027*** 0.015*** 0.010***
(0.004) (0.005) (0.002) (0.001) (0.004) (0.005) (0.002) (0.001)
Ethnolinguistic fractionalization index 0.107 -0.125 -0.014 -0.017 0.110 -0.186 -0.030 -0.027
(0.137) (0.100) (0.024) (0.016) (0.242) (0.184) (0.048) (0.036)
Constant -37.788 22.619 6.272 5.268 -41.348 57.267 13.611 10.661
(29.795) (21.332) (4.964) (3.256) (94.867) (71.291) (18.369) (13.672)
Observations 42,010 42,010 42,010 42,010 42,010 42,010 42,010 42,010
Number of countries 24 24 24 24 24 24 24 24
Test of overidentification restrictions:
Sargan-Hanssen statistics Chi -sq(1) 2.134 9.463 4 .772 3.615
P-value 0.907 0.149 0 .573 0.730
Hausman test
Chi2 11.350 167.050 46.530 21.680
Prob>chi2 0.838 0.000 0 .000 0.198
Panel Random Effects (RE)
Panel RE IV specifications
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Fig. 7. Food-related violence and the logarithm of local price index
Fig. 8. Riots and the welfare/labor COVID-19 policy index
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Fig. 9. Violence against civilians and the welfare/labor COVID-19 policy index
Fig. 10. Food-related violence and the welfare/labor COVID-19 policy index
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6. Robustness checks
We perform two robustness checks to validate the strength of the results. First, we
assess whether lockdowns and COVID-19 welfare policy responses affect the number
of fatalities associated with the conflicts analyzed. Second, we assess what factors
increase the probability of the state being directly involved in riots, violence against
civilians and food-related conflicts either instigating or responding to contain violence.
6.1 Fatalities
We next analyze the total number of fatalities. Our new dependent variable is the
number of fatalities reported in ACLED from January 1 2015 until May 2 2020,
associated with any conflict. We also analyze the number of fatalities exclusively
related to riots, violence against civilians and food-related conflicts (including food
looting). As before, we use two specifications: panel random effects (RE) and panel
random effects with IV-2SLS. Table 2 reports the results. At the bottom of Table 2, we
show the Sargan-Hanssen overidentification test and the Hausman endogeneity tests.
The first-stage regression results are the same as already reported in Table A5 since we
have the same endogenous variables as in the earlier IV RE specification. These first-
stage regressions suggest the instruments are relevant and valid. Again, we find
evidence of endogeneity, particularly for all ACLED fatalities and fatalities due to
violence against civilians (Table 2, columns 5 and 7).
The IV-2SLS specifications show that early social distancing measures have no
increased association with fatalities (Table 2, columns 5-8). However, the number of
fatalities increased substantially after lockdowns for all ACLED fatalities (column 5)
and fatalities associated with violence against civilians (column 7). There is no evidence
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of increased fatalities associated with food-related conflict. For this type of conflict, we
added any fatalities related to food looting.
There is evidence that countries with a higher welfare and labor COVID-19
policy index experienced lower levels of overall ACLED’s fatalities and a lower level
of fatalities due to violence against civilians (Table 2, columns 5 and 7). Figure 11
shows these marginal effects. For instance, the number of total fatalities decreases by
nearly five casualties when comparing a country with no welfare and labor COVID-19
policy response versus one with an index of 0.4.
As mentioned earlier (Table 1), higher local prices are not associated with a
higher probability of experiencing food-related conflicts. However, Table 2 reveals
there is a statistically significant association between rises in local prices and fatalities
derived from food-related conflicts. For instance, for a 10% increase in the local price
index (measured in natural logarithm) the number of food-related conflict fatalities
increase nearly by 0.35 percentage point increase. Very similar results are obtained
when using the RE specifications with or without instrumenting (columns 4 and 8).
Figure 12 illustrates these marginal effects between rises in local prices and number of
food-related conflict fatalities. The positive association between these variables is
positive, regardless of the initial value of the index of local price.
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Panel A
Panel B
Fig. 11. Overall fatalities and fatalities due to violence against
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Fig. 12. Food-related fatalities and logarithm of local price index
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Table 2
COVID-19 interventions, local prices and fatalities
Note. Significant at the *** p<0.01, ** p<0.05 and * p<0.1 levels.
6.2 The state as an actor in riots, violence against civilians and food-related
conflicts
To conclude our analysis, we analyze the conflicts in which the state has been directly
involved as an actor (either instigating or responding to contain violence). As before,
we focus on riots, violence against civilians, and food-related conflicts. We identify
whether the state was involved as an actor, whether in its capacity as the military, the
police, the government or government guards, according to ACLED’s database.
(1) (2) (3) (4) (5) (6) (7) (8)
Fatalaties of:
Any ACLED
conflict
Riots
Violence against
civilians
Food-related
conflict
Any ACLED
conflict
Riots
Violence against
civilians
Food-related
conflict
First social distancing imp lemented -0.024*** 0.001 -0.014*** -0.002*** -0.033 0.000 -0.007 -0.002***
(0.007) (0.001) (0.002) (0.000) (0.035) (0.002) (0.017) (0.001)
Strict lockdown 0.310 0.017 -0.111 0.021 5 .997*** 0.19 2 2.396*** 0.061
(0.277) (0.029) (0.208) (0.035) (1.206) (0.129) (0.911) (0.167)
Index of welfare and labor COVID19 response -2.057 -0.052 -0.864 0.102 -26.693*** -0.345 -13.333** -0.80 9
(1.266) (0.134) (0.948) (0.160) (7.537) (0.804) (5.692) (1.048)
Log index local market price -0.240** -0.018 0.051 0.033 *** -0.240* -0.01 9 -0.020 0.035**
(0.120) (0.013) (0.070) (0.012) (0.125) (0.013) (0.093) (0.014)
Log stable nightlight (year 2015) -0.037 0.010 0.103 0.001 -0.001 0.010 0.145** 0.011
(0.095) (0.010) (0.065) (0.011) (0.100) (0.010) (0.074) (0.012)
Log mobile phone coverage 2G-3G -0.692*** 0.017*** -0.105*** -0.043*** -0.708*** 0.018*** -0.085* -0.040***
(0.059) (0.006) (0.038) (0.006) (0.061) (0.006) (0.045) (0.007)
% Mountains 0.113 -0.053*** -0.149 -0.016 0.077 -0.055*** -0.15 5 0.001
(0.197) (0.021) (0.123) (0.021) (0.205) (0.021) (0.153) (0.024)
% Forests -2.189*** -0.010 -0.433 *** -0.157*** -2.174*** -0.016 -0.754*** -0.166***
(0.258) (0.027) (0.161) (0.027) (0.272) (0.028) (0.202) (0.031)
Petroleum fields -1.674*** -0.045* -0.424** -0.042 -1.809 *** -0.045 * -0.698*** -0.063**
(0.244) (0.026) (0.169) (0.028) (0.251) (0.026) (0.187) (0.030)
Mines 0.098 -0.015* 0.018 -0.013 0.094 -0.015* 0.113* -0.010
(0.078) (0.008) (0.057) (0.010) (0.079) (0.008) (0.059) (0.010)
Diamond mines 0.312** -0.004 0.243** 0.009 0.3 60** -0.003 0 .289*** 0.01 5
(0.137) (0.014) (0.101) (0.017) (0.141) (0.015) (0.105) (0.017)
Size of area 0.000*** 0.000 0.0 00*** 0.000* 0.000*** 0.000 0 .000*** 0.000**
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Electricity 0.012 0.067*** -0.018 -0.040** 0 .072 0.066*** 0.014 -0.036**
(0.142) (0.015) (0.093) (0.016) (0.147) (0.015) (0.110) (0.017)
Primary roads 0.062 -0.02 1*** -0.043 -0.011** 0.06 3 -0.018** -0.034 -0.016**
(0.069) (0.007) (0.033) (0.006) (0.074) (0.008) (0.055) (0.007)
Log population -0.433*** 0.005 -0.175*** 0.000 -0.444*** 0.004 -0.26 5*** -0.004
(0.051) (0.005) (0.035) (0.006) (0.053) (0.006) (0.039) (0.006)
Log infant mortality rate 1.139*** 0.0 40* 0.343 *** 0.038** 1.294*** 0.04 6 -0.08 7 0.071***
(0.238) (0.024) (0.092) (0.015) (0.282) (0.028) (0.205) (0.026)
Log cultivated 0.351*** 0.002 0.396*** 0.034 *** 0.321 *** 0.001 0.211*** 0 .029**
(0.099) (0.010) (0.064) (0.011) (0.103) (0.011) (0.077) (0.012)
Ethnolinguistic fractionalization index 0.321 -0.013 -0.113 -0.09 3** 0.137 -0.024 0.105 -0.143**
(0.723) (0.068) (0.218) (0.036) (2.036) (0.118) (1.058) (0.065)
Constant 538.073*** -15.911 302.793*** 39.856*** 725.467 -10.325 153.514 53.191***
(143.598) (13.415) (39.689) (6.589) (759.140) (40.408) (376.244) (18.644)
Observations 42,010 42,010 42,010 42,010 42,010 42,010 42,010 42,010
Number of countries 24 24 24 24 24 24 24 24
Test of overidentification restrictions:
Sargan-Hanssen statistics Chi-sq(1 ) 0.499 3.081 2.140 4.716
P-value 0.998 0.799 0.906 0 .581
Hausman test
Chi2 40.080 11.960 104.890 18.850
Prob>chi2 0.001 0.803 0.000 0 .337
Panel Random Effects (RE)
Panel RE IV specifications
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We present two specifications, panel RE without and with IV-2SLS. Table 3
presents the results of both specifications. The first-stage IV-2SLS regression results
are the same as already reported in Table A5 since we have the same endogenous
variables as in the earlier IV specifications.
Table 3
COVID-19 interventions, local prices and the state as perpetrator of violence.
Note. Significant at the *** p<0.01, ** p<0.05 and * p<0.1 levels.
The Sargan-Hanssen overidentification tests show that the instruments satisfy
the overidentification restrictions (see bottom of Table 3). Also, the Hausman tests
(1) (2) (3) (4) (5) (6) (7) (8)
State (military, policy, gard or government) involved as actor in:
Any ACLED
conflict
Riots
Violence
against civilians
Food-related
conflict
Any ACLED
conflict
Riots
Violence
against civilians
Food-related
conflict
First social distancing i mplemented -0.002*** 0.0 01 -0.000 -0 .000 -0.001 0 .001 0.000 -0.000
(0.001) (0.001) (0.000) (0.000) (0.002) (0.002) (0.001) (0.000)
Strict lockdown 0.071*** 0.015** 0.064*** 0.006 ** -0.300*** 0.00 1 -0 .086*** 0.018*
(0.015) (0.007) (0.007) (0.002) (0.065) (0.029) (0.031) (0.011)
Index of welfare and labor COVID19 response 0.111 -0.023 0.057* -0.002 2 .563*** 0.104 0.486** -0.126*
(0.068) (0.031) (0.033) (0.011) (0.407) (0.184) (0.195) (0.066)
Log index local market price -0.016** -0.003 0.010*** 0 .001 -0.01 7*** -0.003 0.009*** 0 .001
(0.007) (0.003) (0.003) (0.001) (0.007) (0.003) (0.003) (0.001)
Log stable nightlight (year 201 5) 0.030*** 0.017*** -0.001 -0.001 0.025*** 0.017 *** -0.002 0.00 0
(0.005) (0.002) (0.003) (0.001) (0.005) (0.002) (0.003) (0.001)
Log mobile phone coverage 2G-3G -0.049*** 0.011*** 0.002 -0.000 -0.049*** 0.011*** 0.002 -0.000
(0.003) (0.001) (0.002) (0.001) (0.003) (0.001) (0.002) (0.001)
% Mountains 0.047*** -0.028*** 0.035*** -0.000 0.044*** -0.028*** 0.033*** -0.000
(0.011) (0.005) (0.005) (0.002) (0.011) (0.005) (0.005) (0.002)
% Forests -0.059*** -0.004 0.034 *** -0.008*** -0.064*** -0.005 0.033 *** -0.008***
(0.014) (0.007) (0.007) (0.002) (0.015) (0.007) (0.007) (0.002)
Petroleum fields -0.107*** -0.012** 0.003 -0.003 -0.098*** -0.012* 0.005 -0.004
(0.013) (0.006) (0.006) (0.002) (0.014) (0.006) (0.007) (0.002)
Mines -0.007* -0.003* -0.004* -0.000 -0.005 -0.00 3* -0.003 * -0.000
(0.004) (0.002) (0.002) (0.001) (0.004) (0.002) (0.002) (0.001)
Diamond mines 0.019** -0.000 0.003 0.001 0.01 4* -0 .001 0.002 0.001
(0.007) (0.003) (0.004) (0.001) (0.008) (0.003) (0.004) (0.001)
Size of area -0.000 0.000*** -0.000*** 0.000 -0.000 0.000*** -0.000 *** 0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Electricity 0.012 0.028*** 0 .005 -0.004*** 0.005 0.028*** 0.003 -0.004***
(0.008) (0.004) (0.004) (0.001) (0.008) (0.004) (0.004) (0.001)
Primary roads 0.021*** -0 .000 0.004* -0.000 0.026 *** -0 .000 0.004* 0.000
(0.004) (0.002) (0.002) (0.001) (0.004) (0.002) (0.002) (0.001)
Log population -0.026*** -0.001 0.000 0.000 -0.02 7*** -0.001 0.000 -0.000
(0.003) (0.001) (0.001) (0.000) (0.003) (0.001) (0.001) (0.000)
Log infant mortality rate 0.079*** 0.001 -0.018** 0.006*** 0.063 *** 0.001 -0.027*** 0.008***
(0.014) (0.007) (0.007) (0.002) (0.015) (0.007) (0.007) (0.002)
Log cultivated -0.027*** -0.00 4* -0.017*** 0 .001 -0.03 1*** -0.004* -0.017*** 0.002**
(0.005) (0.003) (0.003) (0.001) (0.006) (0.003) (0.003) (0.001)
Ethnolinguistic fractionalization index -0.015 0 .058 -0.019 -0.00 7* 0.037 0.06 5 0.000 -0 .005
(0.056) (0.058) (0.031) (0.004) (0.114) (0.088) (0.075) (0.011)
Constant 4 3.927*** -17.621 10.2 71 1.157 20.167 -2 1.776 -2.595 -0.004
(11.585) (12.437) (6.442) (0.816) (42.825) (33.827) (28.750) (4.000)
Observations 42,010 42,010 42,010 42,010 42,010 42,010 42,010 42,010
Number of countries 24 24 24 24 24 24 24 24
Test of overidentification restrictions:
Sargan-Hanssen statistics Chi-sq(1) 9.349 1.865 3.949 5.014
P-value 0.155 0.932 0.684 0.542
Hausman test
Chi2 62.150 3.360 68.150 32.720
Prob>chi2 0.000 1.000 0.000 0.012
Panel RE IV specifications
Panel RE IV specifications
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suggest the IV-2SLS panel RE results should be preferred to those of panel RE results.
According to the IV-2S2LS panel RE specifications, the instances where the state is
involved in food-related conflicts have increased since the local lockdowns (Table 3,
column 8). However, in countries with a higher index of welfare and labor COVID-
policy response, the state is less likely to be involved as an actor in food-related
conflicts. In contrast, in these countries, the state is more likely to be involved in
violence against civilians (column 7), but perhaps in enforcing lockdowns or preventing
clashes.
Table 3, column 7, also shows that rises in local prices is associated with a slight
increase of the state being involved as an actor in violence against civilians. This effect
is statistically significant, and is similar whether using the RE without or with IV
(columns 3 and 7). For instance, a 10% rise in the index of a local price, leads to a rise
of nearly 0.09 percentage point increase in the probability of the state being involved
in violence against civilian conflicts. However, there is no evidence to suggest that a
rise in local prices is associated with the state being involved as an actor for riots and
food-related conflicts.
7. Conclusion
We analyzed the impact of lockdowns, food vulnerability, welfare, and labor COVID-
19 policy responses on conflict. Our IV-2SLS panel random effects specifications
revealed that riots, violence against civilians and food-related conflicts increased after
lockdowns. We also found that food insecurity, measured in terms of rises in local
prices, is associated with a higher probability of a country experiencing violence against
civilians, the state being involved in violence against civilians, and a higher number of
fatalities associated with food-related conflicts.
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Our findings also revealed that increases in local prices are associated with
increases in violence against civilians, the state being involved in instigating or
responding to contain violence against civilians, and the number of fatalities of food-
related conflicts. This vulnerability to local prices can be explained by the fact that most
of the food consumed in Africa (90%) comes from domestic producers (Raleigh et al.,
2015) and most producers in Africa are net food consumers (Lee and Ndulo, 2011).
Nonetheless, we found no statistically significant association between rises in local
prices and incidence of riots or incidence of food-related conflicts. Our analysis
revealed that other factors increased the probability of experiencing these conflicts.
Riots are more likely to emerge in areas with poorer density of roads, which can proxy
grievances, and with high mobile phone coverage, thus an easier way to mass mobilize
people (Manacorda and Tesei, 2020). We also found that areas experiencing more
food-related conflicts are those with higher density of cultivated land. These findings
support pre-COVID research that suggests these areas have increased risks of rebel
groups instigating violence to steal food (Rezaeedaryakenari et al., 2020).
The implications of our analysis are important from a public policy perspective.
Food vulnerability and price volatility are an explosive combination for certain types
of conflicts. Another key finding is that countries with a higher index of welfare and
labor COVID-19 policy response are less likely to have suffered these conflicts and less
likely to have experienced fatalities as a result of violence against civilians. Since the
lockdowns, governments have been more heavily involved as actors in food-related
conflicts. However, countries with a higher welfare and labor COVID-19 policy index
are also less likely to intervene in food-related conflicts directly. Overall, our results
also indicate that providing urgent welfare assistance can reduce the probability of
countries experiencing riots, violence against civilians, food-related conflicts, and their
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associated casualties. Although the association found is weak, the findings suggest that
urgent state interventions can reduce food vulnerability and prevent major social unrest.
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Appendix
Table A1
Countries analyzed with data on local food prices at sub-level until 2020
Sources. Conflict events, ACLED. Dates on social distancing and lockdowns own
estimates using ACAPS (2020) and Hale et al. (2020).
Country
Number of conflict
events in ACLED
Percent of conflict
events in ACLED
Date of first social
distancing
Date of start of local
lockdown
Algeria 4,558 10.85 10-Mar-20 10-Mar-20
Angola 301 0.72 06-Feb-20 20-Mar-20
Benin 169 0.4 03-Mar-20 19-Mar-20
Burkina Faso 2,013 4.79 01-Jan-20 12-Mar-20
Burundi 5,525 13.15 06-Mar-20 12-Mar-20
Cameroon 2,619 6.23 01-Jan-20 18-Mar-20
Central African Republic 458 1.09 29-Jan-20 13-Mar-20
Democratic Republic of Congo 5,630 13.4 20-Feb-20 18-Mar-20
Ethiopia 1,389 3.31 16-Mar-20 16-Mar-20
Gabon 155 0.37 07-Feb-20 13-Mar-20
Ghana 715 1.7 24-Jan-20 16-Mar-20
Guinea 886 2.11 29-Feb-20 26-Mar-20
Kenya 2,528 6.02 20-Jan-20 13-Mar-20
Lesotho 39 0.09 06-Mar-20 18-Mar-20
Liberia 340 0.81 09-Mar-20 11-Apr-20
Madagascar 771 1.84 15-Mar-20 20-Mar-20
Malawi 405 0.96 16-Mar-20 16-Mar-20
Mali 1,206 2.87 19-Mar-20 19-Mar-20
Mauritania 42 0.1 05-Feb-20 16-Mar-20
Namibia 242 0.58 01-Mar-20 17-Mar-20
Niger 737 1.75 13-Mar-20 13-Mar-20
Nigeria 9,824 23.38 01-Jan-20 29/03/2020
Rwanda 93 0.22 27-Jan-20 08-Mar-20
Zimbabwe 1,365 3.25 28-Jan-20 17-Mar-20
Total ACLED events 42,010 100
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Table A2
Welfare and labor COVID-19 policy response of 24 countries analyzed
Note. - No program implemented until May 1 2020. Source: Gentilini et al. (2020).
SOCIAL INSURANCE LABOR MARKETS
Overall Cash- Public In-kind (in- Utility and Paid Health Pensions Social security Labor Reduced
COVID-19 Public based Works kind/school financial leave/ insurance and disability co ntributions Wag e Activation regula tion work time
index transfers feeding) support unemployment support benefits
(waiver/subsidy)
(waiver/subsidy) (training) adjustment subsidy
Algeria 0.417 1 0 1 0 1 0 1 1 0 0 0 0
Angola 0.083 1 0 0 0 0 0 0 0 0 0 0 0
Benin 0.083 0 0 0 1 0 0 0 0 0 0 0 0
Burkina Faso 0 .250 1 0 1 1 0 0 0 0 0 0 0 0
Burundi 0.000 - - - - - - - - - - - -
Cameroon 0.083 0 0 0 1 0 0 0 0 0 0 0 0
Central African Republic 0.000 - - - - - - - - - - - -
Democratic Republic of Congo 0.000 - - - - - - - - - - - -
Ethiopia 0.333 0 1 1 1 0 0 0 0 0 0 1 0
Gabon 0 .000 - - - - - - - - - - - -
Ghana 0.250 0 0 1 1 0 0 1 0 0 0 0 0
Guinea 0.167 1 0 1 0 0 0 0 0 0 0 0 0
Kenya 0.167 1 0 0 1 0 0 0 0 0 0 0 0
Lesotho 0.000 - - - - - - - - - - - -
Liberia 0.167 0 0 1 1 0 0 0 0 0 0 0 0
Madagascar 0.250 1 0 1 0 0 0 0 1 0 0 0 0
Malawi 0.08 3 1 0 0 0 0 0 0 0 0 0 0 0
Mali 0.167 0 0 1 1 0 0 0 0 0 0 0 0
Mauritania 0.167 1 0 0 1 0 0 0 0 0 0 0 0
Namibia 0 .167 1 0 0 1 0 0 0 0 0 0 0 0
Niger 0.083 0 0 0 1 0 0 0 0 0 0 0 0
Nigeria 0.250 1 0 1 1 0 0 0 0 0 0 0 0
Rwanda 0.333 1 0 1 1 0 0 0 1 0 0 0 0
Zimbabwe 0.083 1 0 0 0 0 0 0 0 0 0 0 0
SOCIAL ASSISTANCE
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Table A3
Data sources
Variable Description Time Boundary Source
Riots
Violent events where demonstrators or mobs engage in disrupti ve acts or
disorganised acts of violence against property or people.
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and longitude. Armed Conflict Location and Event Data Project (ACLED).
Violence against civilians
Violent events where an organised armed group deliberately inflicts violence upon
unarmed non-combatants).
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and longitude. ACLED.
Food-related conflict Any violent event related to food, including livestock, agriculture, cattle.
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and longitude. Own construction using violent description provided by ACLED.
Food looting Any looting event related to food.
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and longitude. Own construction using violent description provided by ACLED.
Fatalities
The total number of deaths arising from a conflict. Separate variables are provided
for number of fatalities related to riots, violence against civilians, food-related
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and longitude. Own construction using violent description provided by ACLED.
State involved as actor
The state is explicitly mentioned as an actor in the violent event in the form of t he
army, police, guard, or government.
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and longitude. Own construction using violent description provided by ACLED.
Date of social distancing and lockdowns
Date of when the first social distancing measure, and first lockdown was
implemented.
Exact day of implementation
during January-May 2020
Country-level Own construction using Hale et al. (2020) and ACAPS (202 0).
Index of welfare and labor COVID-19 response
We construct an overall welfare and labor index based on these 12 different types
of interventions implemented worldwide to deal with COVID-19. These can be
grouped into three broad categories. The first one, social assistance interventions
include: cash-based transfers, public works, in-kind/school feeding and
utility/financial support. The second, social insurance policies include: paid
leave/unemployment, health insurance support, pensions and disability benefits
and social security contributions. The last one, labor market int erventions: include
wage subsidy, training, labor regulation and reduced work time subsidy. The index
ranges from 0 (no intervention) up to 1 (the country has simultaneously
implemented all 12 types of interventions).
Varies according it changes
during January-May 2020
Country-level Own construction using Gentilini et al. (2020).
Date of start of welfare/labor COVID-19 response
Date of when welfare and labor social welfare response were first implemented in
the country.
Exact day of implementation
during January-May 2020
Country-level Own construction using Hale et al. (2020).
Index local market price Monthly local price index of the most frequent commodity in each market.
Montly basis during
January 2015-May 2 20 20
Data available at market level. The local parket price index
is attached to each conflict event according to nearest
Own construction using the Global Food Prices Database (WFP) and for
Zimbabwe only the USAID FEWS-NET.
Log stable nightlight (year 2015) Average level of nightlight luminosity. Average level for year 2015 District level USA Air Force Weather Agency.
Cultivated land Mean level of cultivated land by dis trict. District level
Rezaeedaryakenari, Landis and Thies' (2020). Publicly available data. They used
the Global Agro-Ecological Zones (GAEZ) of Food and Agricultural Organization
(FAO).
Size of area (district)
Geographic area in thousands of square kilometers for each
district.
Time-invariant in dataset. District level Rezaeedaryakenari, Landis and Thies' (2020). Publicly available data.
Log mobile phone coverage Coverage of mobile phone coverage 2G-3G at cell level. Time-invariant in dataset. 55 x 55 k m cells within country.
Manacorda and Tesei’s (2020) publicly available data. They used the Global
System for Mobile Communications (GSM) Association.
% Mountains Percentage of cell covered by mountains. Time-invariant in dataset. 55 x 55 km cells within country. Manacorda and Tesei’s (2020) publicly available data. They used UNEP-WCMC.
% Forests Percentage of cell covered by forests. Time-invariant in dataset. 55 x 55 km cells within country. Manacorda and Tesei’s (2020) publicly available data. They used GLOBCover.
Petroleum fields Dummy variable indicating if in the cell there are petroleum fields. Time-invariant in dat aset. 55 x 55 km cells within country. Manacorda and Tesei’s (2020) publicly available data. They used PRIO.
Mines Dumm y variable indicating if in the cell there are mines. Time-invariant in dataset. 55 x 55 km cells within count ry.
Manacorda and Tesei’s (2020) publicly available data. They used USA
Geological Survey.
Diamond mines Dummy variable indicating if in the cell there are diamond mines. Time-invariant in datas et. 55 x 55 km cells within country. Manacorda and Tesei’s (2020) publicly available data. They used PRIO.
Electricity
Km of electrical grid.
Time-invariant in dataset. 55 x 55 km cells within country .
Manacorda and Tesei’s (2020) publicly available data. They used the Africa
Infraestructure Country diagnostic (ADB).
Primary roads Km of primary roads by cell. Time-invariant in dataset. 55 x 55 km cells within country .
Manacorda and Tesei’s (2020) publicly available data. They used the Africa
Infraestructure Country diagnostic (ADB).
Population Population size by cell. 55 x 55 km cells within count ry. Manacorda and Tesei’s (2020) publicly available data. They used SEDAC/NASA.
Log infant mortality rate
the number of children that die under one year of age in a given year, per 1,000
live births.
District level Manacorda and Tesei’s (2020) publicly available data. They used SEDAC/NASA.
Ethnolinguistic fractionalization index
The ethnic fractionalization index corresponds to the probability that two
randomly drawn individuals within a country are not from the same ethnic group
in 2013.
Time-invariant in dataset. Country-level Alt as Maradov Mira.
Male mortality rate attributed to hous ehold and ambient
air pollution.
Male mortality rate attributed to hous ehold and ambient air pollution per
100,000, based on standardized age.
Year 2016 Country-level World Bank data repository.
Adult diabetes prevalence
Percentage of diabetes prevalence among the adult population (aged 20-79) at the
national level over the years 2010-2019 .
Yearly 2015-2019 Country-level World Bank data repository.
IMF global commodity price
IMF all commodity price index. Value represents the benchmark prices which are
representative of the global market. They are determined by the largest exporter
of a given commodity.
Monthly during 2015 -2020 Global-level IMF data repository.
Colonial heritage
Indicates whether country is a former British, French, Portuguese, German, Belgian
or American Colonization Society colony.
Time-invariant. Country-level Own estimates using historical records.
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Table A4
Summary statistics of countries analyzed
Variable Total Mean Std. Dev. Total Mean Std. Dev. Total Mean Std. Dev.
Riots 12572 0.13 0.33 524 0 .08 0.28 346 0.135 0.342
Violence against civilians 24745 0.28 0.45 1304 0.23 0.42 854 0.384 0.487
Food-related incidents 2871 0.02 0.16 174 0 .03 0.17 160 0.047 0.211
Food looting 1798 0.02 0.12 110 0.02 0.13 107 0.026 0.160
Fatalaties any ACLED conflict 169454 1.66 8.59 6489 1.08 3.71 4616 1.894 6.172
Fatalalties to riots 4552 0.06 0.89 272 0 .04 0.36 134 0.065 0.415
Fatalaties to violence against civilians 50506 0.69 6.37 1816 0.38 1.81 1236 0.583 2.360
Fatalaties to food-related conflict 6888 0.05 1.08 235 0.04 0.78 290 0.092 2.482
Fatalities to food looting 4344 0.03 0.80 154 0 .03 0.71 225 0.077 2.447
State involved as actor in any A CLED conflict 40237 0.32 0.47 2083 0.26 0.44 1548 0.404 0.491
State involved as actor in riot s 4710 0.05 0.21 180 0.03 0.17 157 0.056 0.231
State involved as actor in viol ence against civilians 5309 0.05 0.22 225 0.03 0.17 279 0.114 0.318
State involved as actor in food-related conflict 691 0.01 0.07 23 0.00 0.06 41 0.011 0.106
State involved as actor in food loot ing 396 0.00 0 .05 10 0 .00 0.04 26 0.007 0.082
Controls and instrum ents
Log index local market price 4.82 0.49 4.74 0 .40 4.768 0 .411
Adult diabetes prevalence (% of population ages 20 to 79) 4.26 1.73 5.01 1.70 4.696 1 .739
IMF global commodity price 113.80 1 1.68 116.60 2.78 86.988 4.413
Log stable nightlight, year 2015 1.92 0.72
Log mobile phone coverage 2G-3G -0.52 0.93
% Mountains 0.33 0.34
% Forests 0.24 0.22
Petroleum fields 0.06 0.20
Mines 0.30 0.63
Diamond mines 0.04 0.32
Size of area 2989.24 613.91
Electricity 0.44 0.44
Primary roads 1.88 1.66
Log population 12.86 1.39
Log infant mortality rate 2.11 0.43
Log cultivated 3.89 0.65
Ethnolinguistic fractionalization index 0.61 0.29
Index of welfare and labor COVID-19 response 0.01 0.04
Male mortality rate attribut ed to household and ambient air
pollution, age-standarised, year 2016
192.60 79.43
Number of observations 42010 3134 1330
Number of countries 24 24 24
1 January 2015-6 M ay2020
1 October-31 December 2019
After lockdown in 2020
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Table A5
First-stage IV regression of Tables 1, 2 and 3
Note. Significant at the *** p<0.01, ** p<0.05 and * p<0.1 levels.
(1) (2) (3)
First social dist ancing S trict lockdown Index welfare/labor
Male mortality rate attributed to household and
ambient air pollution male
-0.120*** 0.000** -0.000***
(0.001) (0.000) (0.000)
Diabetes prevalence (% of population ages 20 t o
79) -3.189*** 0.005*** -0.003***
(0.054) (0.001) (0.000)
Former colony (never colonised reference group):
British -43.649*** 0.037*** 0.009***
(0.480) (0.006) (0.001)
French -14.998*** 0.069*** 0.020***
(0.476) (0.006) (0.001)
Portuguese -36.827*** 0.037*** 0.013***
(0.892) (0.011) (0.002)
German -45.109*** 0 .063*** 0.007***
(0.554) (0.007) (0.002)
Belgium -16.255*** 0.049*** 0.012***
(0.485) (0.006) (0.001)
American Colonization Society 12.062*** 0.023** 0.020 ***
(0.876) (0.011) (0.002)
IMF all commodity price -0.021*** -0.006*** -0.001***
(0.006) (0.000) (0.000)
Log index local market price 1.497*** 0.017*** 0.001***
(0.160) (0.002) (0.000)
Log stable nightlight (year 201 5) 4.162*** -0.011*** -0.000
(0.139) (0.002) (0.000)
Log mobile phone coverage 2G-3G -2.230*** 0.002 -0.000
(0.084) (0.001) (0.000)
% Mountains 6.402*** 0.006* 0 .000
(0.294) (0.004) (0.001)
% Forests -7.042*** -0.003 -0.004***
(0.356) (0.004) (0.001)
Petroleum fields 6.402*** -0.011** 0.001
(0.361) (0.004) (0.001)
Mines 1.129*** 0.006 *** 0.001**
(0.118) (0.001) (0.000)
Diamond mines 2.604*** -0.001 0.001**
(0.207) (0.002) (0.001)
Size of area -0.000 -0.000 -0.000
(0.000) (0.000) (0.000)
Electricity -3.413*** -0.002 -0.002***
(0.202) (0.002) (0.001)
Primary roads 1.49 8*** -0.003*** -0.001***
(0.075) (0.001) (0.000)
Log population -2.710*** 0.002*** 0.001***
(0.074) (0.001) (0.000)
Log infant mortality rat e -0.279 0 .012*** -0.015***
(0.282) (0.003) (0.001)
Log cultivated 3.22 3*** 0.004** 0.003 ***
(0.137) (0.002) (0.000)
Ethnolinguistic fractionalisation i ndex -19.39 2*** 0.036*** 0.011 ***
(0.499) (0.006) (0.001)
Observations 42,010 42,010 42,010
R-squared 0.817 0.186 0.113
F-statistic of excluded instruments 1039.23 421 1.99 520.55
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(1) (2) (3) (4) (5) (6) (7) (8)
Riots
Violence against
civilians
Food-related
incidents
Food looting Riots
Violence against
civilians
Food-related
incidents
Food looting
First social distancing implemented 0.002 -0.001 -0.000 -0.000 0.002 -0.003 -0.001 -0.000
(0.001) (0.001) (0.000) (0.000) (0.004) (0.003) (0.001) (0.001)
Strict lockdown 0.020* 0.078*** 0.021*** 0.009** 0.154*** 0.110* 0.127*** 0.050***
(0.010) (0.014) (0.005) (0.004) (0.045) (0.060) (0.022) (0.017)
Index of welfare and labor COVID19 response -0.048 0.066 -0.048** -0.030* -0.666** -2.124*** -0.886*** -0.394***
(0.048) (0.063) (0.023) (0.018) (0.282) (0.378) (0.138) (0.108)
Log index local market price -0.004 0.073*** -0.001 -0.000 -0.004 0.071*** -0.000 -0.000
(0.005) (0.006) (0.002) (0.002) (0.005) (0.006) (0.002) (0.002)
Log stable nightlight (year 2015) 0.012*** -0.051*** 0.002 0.002 0.013*** -0.049*** 0.003* 0.003*
(0.004) (0.005) (0.002) (0.001) (0.004) (0.005) (0.002) (0.001)
Log mobile phone coverage 2G-3G 0.027*** -0.005 -0.003*** -0.003*** 0.027*** -0.005 -0.003** -0.003***
(0.002) (0.003) (0.001) (0.001) (0.002) (0.003) (0.001) (0.001)
% Mountains -0.056*** 0.035*** -0.010*** -0.009*** -0.057*** 0.029*** -0.011*** -0.009***
(0.008) (0.010) (0.004) (0.003) (0.008) (0.010) (0.004) (0.003)
% Forests 0.035*** -0.039*** -0.044*** -0.031*** 0.035*** -0.043*** -0.044*** -0.031***
(0.010) (0.013) (0.005) (0.004) (0.010) (0.014) (0.005) (0.004)
Petroleum fields 0.023** 0.062*** -0.007 -0.004 0.021** 0.055*** -0.010** -0.005
(0.009) (0.012) (0.004) (0.004) (0.009) (0.013) (0.005) (0.004)
Mines -0.010*** 0.021*** -0.002 -0.001 -0.010*** 0.020*** -0.003* -0.001
(0.003) (0.004) (0.001) (0.001) (0.003) (0.004) (0.001) (0.001)
Diamond mines 0.003 -0.017** -0.001 -0.001 0.004 -0.015** -0.000 -0.000
(0.005) (0.007) (0.003) (0.002) (0.005) (0.007) (0.003) (0.002)
Size of area 0.000 0.000** 0.000*** 0.000*** 0.000 0.000** 0.000*** 0.000***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Electricity 0.045*** -0.061*** -0.009*** -0.006*** 0.046*** -0.063*** -0.009*** -0.006***
(0.006) (0.007) (0.003) (0.002) (0.006) (0.007) (0.003) (0.002)
Primary roads -0.011*** -0.016*** -0.003** -0.003*** -0.011*** -0.019*** -0.003** -0.003***
(0.003) (0.004) (0.001) (0.001) (0.003) (0.004) (0.001) (0.001)
Log population 0.010*** -0.003 -0.002* -0.001 0.010*** -0.001 -0.001 -0.001
(0.002) (0.003) (0.001) (0.001) (0.002) (0.003) (0.001) (0.001)
Log infant mortality rate 0.000 -0.140*** 0.035*** 0.026*** 0.002 -0.147*** 0.039*** 0.029***
(0.011) (0.014) (0.005) (0.004) (0.011) (0.015) (0.005) (0.004)
Log cultivated -0.001 0.025*** 0.014*** 0.010*** -0.001 0.027*** 0.015*** 0.010***
Panel Random Effects (RE)
Panel RE IV specifications
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(0.004) (0.005) (0.002) (0.001) (0.004) (0.005) (0.002) (0.001)
Ethnolinguistic fractionalization index 0.107 -0.125 -0.014 -0.017 0.110 -0.186 -0.030 -0.027
(0.137) (0.100) (0.024) (0.016) (0.242) (0.184) (0.048) (0.036)
Constant -37.788 22.619 6.272 5.268 -41.348 57.267 13.611 10.661
(29.795) (21.332) (4.964) (3.256) (94.867) (71.291) (18.369) (13.672)
Observations 42,010 42,010 42,010 42,010 42,010 42,010 42,010 42,010
Number of countries 24 24 24 24 24 24 24 24
Test of overidentification restrictions:
Sargan-Hanssen statistics Chi-sq(1) 2.134 9.463 4.772 3.615
P-value 0.907 0.149 0.573 0.730
Hausman test
Chi2 11.350 167.050 46.530 21.680
Prob>chi2 0.838 0.000 0.000 0.198
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(1) (2) (3) (4) (5) (6) (7) (8)
Fatalaties of:
Any ACLED
conflict
Riots
Violence against
civilians
Food-related
conflict
Any ACLED
conflict
Riots
Violence against
civilians
Food-related
conflict
First social distancing implemented -0.024*** 0.001 -0.014*** -0.002*** -0.033 0.000 -0.007 -0.002***
(0.007) (0.001) (0.002) (0.000) (0.035) (0.002) (0.017) (0.001)
Strict lockdown 0.310 0.017 -0.111 0.021 5.997*** 0.192 2.396*** 0.061
(0.277) (0.029) (0.208) (0.035) (1.206) (0.129) (0.911) (0.167)
Index of welfare and labor COVID19 response -2.057 -0.052 -0.864 0.102 -26.693*** -0.345 -13.333** -0.809
(1.266) (0.134) (0.948) (0.160) (7.537) (0.804) (5.692) (1.048)
Log index local market price -0.240** -0.018 0.051 0.033*** -0.240* -0.019 -0.020 0.035**
(0.120) (0.013) (0.070) (0.012) (0.125) (0.013) (0.093) (0.014)
Log stable nightlight (year 2015) -0.037 0.010 0.103 0.001 -0.001 0.010 0.145** 0.011
(0.095) (0.010) (0.065) (0.011) (0.100) (0.010) (0.074) (0.012)
Log mobile phone coverage 2G-3G -0.692*** 0.017*** -0.105*** -0.043*** -0.708*** 0.018*** -0.085* -0.040***
(0.059) (0.006) (0.038) (0.006) (0.061) (0.006) (0.045) (0.007)
% Mountains 0.113 -0.053*** -0.149 -0.016 0.077 -0.055*** -0.155 0.001
(0.197) (0.021) (0.123) (0.021) (0.205) (0.021) (0.153) (0.024)
% Forests -2.189*** -0.010 -0.433*** -0.157*** -2.174*** -0.016 -0.754*** -0.166***
(0.258) (0.027) (0.161) (0.027) (0.272) (0.028) (0.202) (0.031)
Petroleum fields -1.674*** -0.045* -0.424** -0.042 -1.809*** -0.045* -0.698*** -0.063**
(0.244) (0.026) (0.169) (0.028) (0.251) (0.026) (0.187) (0.030)
Mines 0.098 -0.015* 0.018 -0.013 0.094 -0.015* 0.113* -0.010
(0.078) (0.008) (0.057) (0.010) (0.079) (0.008) (0.059) (0.010)
Diamond mines 0.312** -0.004 0.243** 0.009 0.360** -0.003 0.289*** 0.015
(0.137) (0.014) (0.101) (0.017) (0.141) (0.015) (0.105) (0.017)
Size of area 0.000*** 0.000 0.000*** 0.000* 0.000*** 0.000 0.000*** 0.000**
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Electricity 0.012 0.067*** -0.018 -0.040** 0.072 0.066*** 0.014 -0.036**
(0.142) (0.015) (0.093) (0.016) (0.147) (0.015) (0.110) (0.017)
Primary roads 0.062 -0.021*** -0.043 -0.011** 0.063 -0.018** -0.034 -0.016**
(0.069) (0.007) (0.033) (0.006) (0.074) (0.008) (0.055) (0.007)
Log population -0.433*** 0.005 -0.175*** 0.000 -0.444*** 0.004 -0.265*** -0.004
(0.051) (0.005) (0.035) (0.006) (0.053) (0.006) (0.039) (0.006)
Log infant mortality rate 1.139*** 0.040* 0.343*** 0.038** 1.294*** 0.046 -0.087 0.071***
(0.238) (0.024) (0.092) (0.015) (0.282) (0.028) (0.205) (0.026)
Log cultivated 0.351*** 0.002 0.396*** 0.034*** 0.321*** 0.001 0.211*** 0.029**
(0.099) (0.010) (0.064) (0.011) (0.103) (0.011) (0.077) (0.012)
Ethnolinguistic fractionalization index 0.321 -0.013 -0.113 -0.093** 0.137 -0.024 0.105 -0.143**
(0.723) (0.068) (0.218) (0.036) (2.036) (0.118) (1.058) (0.065)
Constant 538.073*** -15.911 302.793*** 39.856*** 725.467 -10.325 153.514 53.191***
(143.598) (13.415) (39.689) (6.589) (759.140) (40.408) (376.244) (18.644)
Observations 42,010 42,010 42,010 42,010 42,010 42,010 42,010 42,010
Number of countries 24 24 24 24 24 24 24 24
Test of overidentification restrictions:
Sargan-Hanssen statistics Chi-sq(1) 0.499 3.081 2.140 4.716
P-value 0.998 0.799 0.906 0.581
Hausman test
Chi2 40.080 11.960 104.890 18.850
Prob>chi2 0.001 0.803 0.000 0.337
Panel Random Effects (RE)
Panel RE IV specifications
Journal Pre-proof
(1) (2) (3) (4) (5) (6) (7) (8)
State (military, policy, gard or government) involved as actor in:
Any ACLED
conflict
Riots
Violence
against
Food-
related
Any ACLED
conflict
Riots
Violence
against
Food-
related
First social distancing implemented -0.002*** 0.001 -0.000 -0.000 -0.001 0.001 0.000 -0.000
(0.001) (0.001) (0.000) (0.000) (0.002) (0.002) (0.001) (0.000)
Strict lockdown 0.071*** 0.015** 0.064*** 0.006** -0.300*** 0.001 -0.086*** 0.018*
(0.015) (0.007) (0.007) (0.002) (0.065) (0.029) (0.031) (0.011)
Index of welfare and labor COVID19 response 0.111 -0.023 0.057* -0.002 2.563*** 0.104 0.486** -0.126*
(0.068) (0.031) (0.033) (0.011) (0.407) (0.184) (0.195) (0.066)
Log index local market price -0.016** -0.003 0.010*** 0.001 -0.017*** -0.003 0.009*** 0.001
(0.007) (0.003) (0.003) (0.001) (0.007) (0.003) (0.003) (0.001)
Log stable nightlight (year 2015) 0.030*** 0.017*** -0.001 -0.001 0.025*** 0.017*** -0.002 0.000
(0.005) (0.002) (0.003) (0.001) (0.005) (0.002) (0.003) (0.001)
Log mobile phone coverage 2G-3G -0.049*** 0.011*** 0.002 -0.000 -0.049*** 0.011*** 0.002 -0.000
(0.003) (0.001) (0.002) (0.001) (0.003) (0.001) (0.002) (0.001)
% Mountains 0.047*** -0.028*** 0.035*** -0.000 0.044*** -0.028*** 0.033*** -0.000
(0.011) (0.005) (0.005) (0.002) (0.011) (0.005) (0.005) (0.002)
% Forests -0.059*** -0.004 0.034*** -0.008*** -0.064*** -0.005 0.033*** -0.008***
(0.014) (0.007) (0.007) (0.002) (0.015) (0.007) (0.007) (0.002)
Petroleum fields -0.107*** -0.012** 0.003 -0.003 -0.098*** -0.012* 0.005 -0.004
(0.013) (0.006) (0.006) (0.002) (0.014) (0.006) (0.007) (0.002)
Mines -0.007* -0.003* -0.004* -0.000 -0.005 -0.003* -0.003* -0.000
(0.004) (0.002) (0.002) (0.001) (0.004) (0.002) (0.002) (0.001)
Diamond mines 0.019** -0.000 0.003 0.001 0.014* -0.001 0.002 0.001
(0.007) (0.003) (0.004) (0.001) (0.008) (0.003) (0.004) (0.001)
Size of area -0.000 0.000*** -0.000*** 0.000 -0.000 0.000*** -0.000*** 0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Electricity 0.012 0.028*** 0.005 -0.004*** 0.005 0.028*** 0.003 -0.004***
(0.008) (0.004) (0.004) (0.001) (0.008) (0.004) (0.004) (0.001)
Primary roads 0.021*** -0.000 0.004* -0.000 0.026*** -0.000 0.004* 0.000
(0.004) (0.002) (0.002) (0.001) (0.004) (0.002) (0.002) (0.001)
Log population -0.026*** -0.001 0.000 0.000 -0.027*** -0.001 0.000 -0.000
(0.003) (0.001) (0.001) (0.000) (0.003) (0.001) (0.001) (0.000)
Log infant mortality rate 0.079*** 0.001 -0.018** 0.006*** 0.063*** 0.001 -0.027*** 0.008***
(0.014) (0.007) (0.007) (0.002) (0.015) (0.007) (0.007) (0.002)
Log cultivated -0.027*** -0.004* -0.017*** 0.001 -0.031*** -0.004* -0.017*** 0.002**
(0.005) (0.003) (0.003) (0.001) (0.006) (0.003) (0.003) (0.001)
Ethnolinguistic fractionalization index -0.015 0.058 -0.019 -0.007* 0.037 0.065 0.000 -0.005
(0.056) (0.058) (0.031) (0.004) (0.114) (0.088) (0.075) (0.011)
Constant 43.927*** -17.621 10.271 1.157 20.167 -21.776 -2.595 -0.004
(11.585) (12.437) (6.442) (0.816) (42.825) (33.827) (28.750) (4.000)
Observations 42,010 42,010 42,010 42,010 42,010 42,010 42,010 42,010
Number of countries 24 24 24 24 24 24 24 24
Test of overidentification restrictions:
Sargan-Hanssen statistics Chi-sq(1) 9.349 1.865 3.949 5.014
P-value 0.155 0.932 0.684 0.542
Hausman test
Chi2 62.150 3.360 68.150 32.720
Prob>chi2 0.000 1.000 0.000 0.012
Panel RE IV specifications
Panel RE IV specifications
Journal Pre-proof
Country
Number of conflict
events in ACLED
Percent of conflict
events in ACLED
Date of first social
distancing
Date of start of local
lockdown
Algeria 4,558 10.85 10-Mar-20 10-Mar-20
Angola 301 0.72 06-Feb-20 20-Mar-20
Benin 169 0.4 03-Mar-20 19-Mar-20
Burkina Faso 2,013 4.79 01-Jan-20 12-Mar-20
Burundi 5,525 13.15 06-Mar-20 12-Mar-20
Cameroon 2,619 6.23 01-Jan-20 18-Mar-20
Central African Republic 458 1.09 29-Jan-20 13-Mar-20
Democratic Republic of Congo 5,630 13.4 20-Feb-20 18-Mar-20
Ethiopia 1,389 3.31 16-Mar-20 16-Mar-20
Gabon 155 0.37 07-Feb-20 13-Mar-20
Ghana 715 1.7 24-Jan-20 16-Mar-20
Guinea 886 2.11 29-Feb-20 26-Mar-20
Kenya 2,528 6.02 20-Jan-20 13-Mar-20
Lesotho 39 0.09 06-Mar-20 18-Mar-20
Liberia 340 0.81 09-Mar-20 11-Apr-20
Madagascar 771 1.84 15-Mar-20 20-Mar-20
Malawi 405 0.96 16-Mar-20 16-Mar-20
Mali 1,206 2.87 19-Mar-20 19-Mar-20
Mauritania 42 0.1 05-Feb-20 16-Mar-20
Namibia 242 0.58 01-Mar-20 17-Mar-20
Niger 737 1.75 13-Mar-20 13-Mar-20
Nigeria 9,824 23.38 01-Jan-20 29-03-2020
Rwanda 93 0.22 27-Jan-20 08-Mar-20
Zimbabwe 1,365 3.25 28-Jan-20 17-Mar-20
Total ACLED events 42,010 100
Guinea-Bissau NOT IN TABLE
CAMeroon not in figure
mauritania no conflict in figure
Journal Pre-proof
Journal Pre-proof
SOCIAL INSURANCE LABOR MARKETS
Overall Cash- Public In-kind (in- Utility and Paid Health Pensions Social security Labor Reduced
COVID-19
Public based
Works kind/school financial leave/ insurance and disability contributions Wage Activation regulation work time
index transfers feeding) support unemployment support benefits
(waiver/subsidy)
(waiver/subsidy) (training) adjustment subsidy sacash sainkind
sautilityfinance
siunempben
sihealthins sipension
sisocsecurity
slabwage
slabactivation
slabreducedwork
slabadjust slabsubsidy
Algeria 0.417 1 0 1 0 1 0 1 1 0 0 0 0
Angola 0.083 1 0 0 0 0 0 0 0 0 0 0 0
Benin 0.083 0 0 0 1 0 0 0 0 0 0 0 0
Burkina Faso 0.250 1 0 1 1 0 0 0 0 0 0 0 0
Burundi 0.000 - - - - - - - - - - - -
Cameroon 0.083 0 0 0 1 0 0 0 0 0 0 0 0
Central African Republic 0.000 - - - - - - - - - - - -
Democratic Republic of Congo
0.000 - - - - - - - - - - - -
Ethiopia 0.333 0 1 1 1 0 0 0 0 0 0 1 0
Gabon 0.000 - - - - - - - - - - - -
Ghana 0.250 0 0 1 1 0 0 1 0 0 0 0 0
Guinea 0.167 1 0 1 0 0 0 0 0 0 0 0 0
Kenya 0.167 1 0 0 1 0 0 0 0 0 0 0 0
Lesotho 0.000 - - - - - - - - - - - -
Liberia 0.167 0 0 1 1 0 0 0 0 0 0 0 0
Madagascar 0.250 1 0 1 0 0 0 0 1 0 0 0 0
Malawi 0.083 1 0 0 0 0 0 0 0 0 0 0 0
Mali 0.167 0 0 1 1 0 0 0 0 0 0 0 0
Mauritania 0.167 1 0 0 1 0 0 0 0 0 0 0 0
Namibia 0.167 1 0 0 1 0 0 0 0 0 0 0 0
Niger 0.083 0 0 0 1 0 0 0 0 0 0 0 0
Nigeria 0.250 1 0 1 1 0 0 0 0 0 0 0 0
Rwanda 0.333 1 0 1 1 0 0 0 1 0 0 0 0
Zimbabwe 0.083 1 0 0 0 0 0 0 0 0 0 0 0
Note: - No programme implemented until 1 May 2020, according to Gentilini et al. (2020).
SOCIAL ASSISTANCE
Journal Pre-proof
Variable Description Time Boundary Source
Riots
Violent events where demonstrators or mobs engage in disruptive acts or
disorganised acts of violence against property or people.
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and
longitude.
Armed Conflict Location and Event Data Project (ACLED).
Violence against civilians
Violent events where an organised armed group deliberately inflicts violence
upon unarmed non-combatants).
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and
longitude.
ACLED.
Food-related conflict Any violent event related to food, including livestock, agriculture, cattle.
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and
longitude.
Own construction using violent description provided by ACLED.
Food looting Any looting event related to food.
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and
longitude.
Own construction using violent description provided by ACLED.
Fatalities
The total number of deaths arising from a conflict. Separate variables are
provided for number of fatalities related to riots, violence against civilians,
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and
longitude.
Own construction using violent description provided by ACLED.
State involved as actor
The state is explicitly mentioned as an actor in the violent event in the form of
the army, police, guard, or government.
Daily events during January 1
2015- May 2 2020.
Georeferenced event identified with latitude and
longitude.
Own construction using violent description provided by ACLED.
Date of social distancing and lockdowns
Date of when the first social distancing measure, and first lockdown was
implemented.
Exact day of implementation
during January-May 2020
Country-level Own construction using Hale et al. (2020) and ACAPS (2020).
Index of welfare and labor COVID-19 response
We construct an overall welfare and labor index based on these 12 different
types of interventions implemented worldwide to deal with COVID-19. These
can be grouped into three broad categories. The first one, social assistance
interventions include: cash-based transfers, public works, in-kind/school
feeding and utility/financial support. The second, social insurance policies
include: paid leave/unemployment, health insurance support, pensions and
disability benefits and social security contributions. The last one, labor market
interventions: include wage subsidy, training, labor regulation and reduced
work time subsidy. The index ranges from 0 (no intervention) up to 1 (the
country has simultaneously implemented all 12 types of interventions).
Varies according it changes
during January-May 2020
Country-level Own construction using Gentilini et al. (2020).
Date of start of welfare/labor COVID-19 response
Date of when welfare and labor social welfare response were first
implemented in the country.
Exact day of implementation
during January-May 2020
Country-level Own construction using Hale et al. (2020).
Index local market price Monthly local price index of the most frequent commodity in each market.
Montly basis during
January 2015-May 2 2020
Data available at market level. The local parket price
index is attached to each conflict event according to
nearest geographical distance.
Own construction using the Global Food Prices Database (WFP) and for
Zimbabwe only the USAID FEWS-NET.
Log stable nightlight (year 2015) Average level of nightlight luminosity. Average level for year 2015 District level USA Air Force Weather Agency.
Cultivated land Mean level of cultivated land by district. District level
Rezaeedaryakenari, Landis and Thies' (2020). Publicly available data. They
used the Global Agro-Ecological Zones (GAEZ) of Food and Agricultural
Organization (FAO).
Size of area (district)
Geographic area in thousands of square kilometers for each
district.
Time-invariant in dataset. District level Rezaeedaryakenari, Landis and Thies' (2020). Publicly available data.
Log mobile phone coverage Coverage of mobile phone coverage 2G-3G at cell level. Time-invariant in dataset. 55 x 55 km cells within country.
Manacorda and Tesei’s (2020) publicly available data. They used the
Global System for Mobile Communications (GSM) Association.
% Mountains Percentage of cell covered by mountains. Time-invariant in dataset. 55 x 55 km cells within country. Manacorda and Tesei’s (2020) publicly available data. They used UNEP-WCMC.
% Forests Percentage of cell covered by forests. Time-invariant in dataset. 55 x 55 km cells within country. Manacorda and Tesei’s (2020) publicly available data. They used GLOBCover.
Petroleum fields Dummy variable indicating if in the cell there are petroleum fields. Time-invariant in dataset. 55 x 55 km cells within country. Manacorda and Tesei’s (2020) publicly available data. They used PRIO.
Mines Dummy variable indicating if in the cell there are mines. Time-invariant in dataset. 55 x 55 km cells within country.
Manacorda and Tesei’s (2020) publicly available data. They used USA
Geological Survey.
Diamond mines Dummy variable indicating if in the cell there are diamond mines. Time-invariant in dataset. 55 x 55 km cells within country. Manacorda and Tesei’s (2020) publicly available data. They used PRIO.
Electricity
Km of electrical grid.
Time-invariant in dataset. 55 x 55 km cells within country.
Manacorda and Tesei’s (2020) publicly available data. They used the Africa
Infraestructure Country diagnostic (ADB).
Primary roads Km of primary roads by cell. Time-invariant in dataset. 55 x 55 km cells within country.
Manacorda and Tesei’s (2020) publicly available data. They used the Africa
Infraestructure Country diagnostic (ADB).
Population Population size by cell. 55 x 55 km cells within country. Manacorda and Tesei’s (2020) publicly available data. They used SEDAC/NASA.
Log infant mortality rate
the number of children that die under one year of age in a given year, per
1,000 live births.
District level Manacorda and Tesei’s (2020) publicly available data. They used SEDAC/NASA.
Ethnolinguistic fractionalization index
The ethnic fractionalization index corresponds to the probability that two
randomly drawn individuals within a country are not from the same ethnic
group in 2013.
Time-invariant in dataset. Country-level Altas Maradov Mira.
Male mortality rate attributed to household and
ambient air pollution.
Male mortality rate attributed to household and ambient air pollution per
100,000, based on standardized age.
Year 2016 Country-level World Bank data repository.
Adult diabetes prevalence
Percentage of diabetes prevalence among the adult population (aged 20-79)
at the national level over the years 2010-2019.
Yearly 2015-2019 Country-level World Bank data repository.
IMF global commodity price
IMF all commodity price index. Value represents the benchmark prices which
are representative of the global market. They are determined by the largest
exporter of a given commodity.
Monthly during 2015-2020 Global-level IMF data repository.
Colonial heritage
Indicates whether country is a former British, French, Portuguese, German,
Belgian or American Colonization Society colony.
Time-invariant. Country-level Own estimates using historical records.
Journal Pre-proof
Variable Total Mean Std. Dev. Total Mean Std. Dev. Total Mean Std. Dev.
Riots 12572 0.13 0.33 524 0.08 0.28 346 0.135 0.342
Violence against civilians 24745 0.28 0.45 1304 0.23 0.42 854 0.384 0.487
Food-related incidents 2871 0.02 0.16 174 0.03 0.17 160 0.047 0.211
Food looting 1798 0.02 0.12 110 0.02 0.13 107 0.026 0.160
Fatalaties any ACLED conflict 169454 1.66 8.59 6489 1.08 3.71 4616 1.894 6.172
Fatalalties to riots 4552 0.06 0.89 272 0.04 0.36 134 0.065 0.415
Fatalaties to violence against civilians 50506 0.69 6.37 1816 0.38 1.81 1236 0.583 2.360
Fatalaties to food-related conflict 6888 0.05 1.08 235 0.04 0.78 290 0.092 2.482
Fatalities to food looting 4344 0.03 0.80 154 0.03 0.71 225 0.077 2.447
State involved as actor in any ACLED conflict 40237 0.32 0.47 2083 0.26 0.44 1548 0.404 0.491
State involved as actor in riots 4710 0.05 0.21 180 0.03 0.17 157 0.056 0.231
State involved as actor in violence against civilians 5309 0.05 0.22 225 0.03 0.17 279 0.114 0.318
State involved as actor in food-related conflict 691 0.01 0.07 23 0.00 0.06 41 0.011 0.106
State involved as actor in food looting 396 0.00 0.05 10 0.00 0.04 26 0.007 0.082
Controls and instruments
Log index local market price 4.82 0.49 4.74 0.40 4.768 0.411
Adult diabetes prevalence (% of population ages 20 to 79) 4.26 1.73 5.01 1.70 4.696 1.739
IMF global commodity price 113.80 11.68 116.60 2.78 86.988 4.413
Log stable nightlight, year 2015 1.92 0.72
Log mobile phone coverage 2G-3G -0.52 0.93
% Mountains 0.33 0.34
% Forests 0.24 0.22
Petroleum fields 0.06 0.20
Mines 0.30 0.63
Diamond mines 0.04 0.32
Size of area 2989.24 613.91
Electricity 0.44 0.44
Primary roads 1.88 1.66
Log population 12.86 1.39
Log infant mortality rate 2.11 0.43
Log cultivated 3.89 0.65
Ethnolinguistic fractionalization index 0.61 0.29
Index of welfare and labor COVID-19 response 0.01 0.04
Male mortality rate attributed to household and ambient air
pollution, age-standarised, year 2016
192.60 79.43
Number of observations 42010 3134 1330
Number of countries 24 24 24
1 January 2015-6 May2020
1 October-31 December 2019
After lockdown in 2020
Journal Pre-proof
(1) (2) (3)
First social distancing
Strict lockdown
Index
welfare/labor
Male mortality rate attributed to household and
ambient air pollution male
-0.120*** 0.000** -0.000***
(0.001) (0.000) (0.000)
Diabetes prevalence (% of population ages 20 to
79)
-3.189*** 0.005*** -0.003***
(0.054) (0.001) (0.000)
Former colony (never colonised reference group):
British -43.649*** 0.037*** 0.009***
(0.480) (0.006) (0.001)
French -14.998*** 0.069*** 0.020***
(0.476) (0.006) (0.001)
Portuguese -36.827*** 0.037*** 0.013***
(0.892) (0.011) (0.002)
German -45.109*** 0.063*** 0.007***
(0.554) (0.007) (0.002)
Belgium -16.255*** 0.049*** 0.012***
(0.485) (0.006) (0.001)
American Colonization Society 12.062*** 0.023** 0.020***
(0.876) (0.011) (0.002)
IMF all commodity price -0.021*** -0.006*** -0.001***
(0.006) (0.000) (0.000)
Log index local market price 1.497*** 0.017*** 0.001***
(0.160) (0.002) (0.000)
Log stable nightlight (year 2015) 4.162*** -0.011*** -0.000
(0.139) (0.002) (0.000)
Log mobile phone coverage 2G-3G -2.230*** 0.002 -0.000
(0.084) (0.001) (0.000)
% Mountains 6.402*** 0.006* 0.000
(0.294) (0.004) (0.001)
% Forests -7.042*** -0.003 -0.004***
(0.356) (0.004) (0.001)
Petroleum fields 6.402*** -0.011** 0.001
(0.361) (0.004) (0.001)
Mines 1.129*** 0.006*** 0.001**
(0.118) (0.001) (0.000)
Diamond mines 2.604*** -0.001 0.001**
(0.207) (0.002) (0.001)
Size of area -0.000 -0.000 -0.000
(0.000) (0.000) (0.000)
Electricity -3.413*** -0.002 -0.002***
(0.202) (0.002) (0.001)
Primary roads 1.498*** -0.003*** -0.001***
(0.075) (0.001) (0.000)
Log population -2.710*** 0.002*** 0.001***
(0.074) (0.001) (0.000)
Log infant mortality rate -0.279 0.012*** -0.015***
Journal Pre-proof
(0.282) (0.003) (0.001)
Log cultivated 3.223*** 0.004** 0.003***
(0.137) (0.002) (0.000)
Ethnolinguistic fractionalisation index -19.392*** 0.036*** 0.011***
(0.499) (0.006) (0.001)
Observations 42,010 42,010 42,010
R-squared 0.817 0.186 0.113
F-statistic of excluded instruments 1039.23 4211.99 520.55
Journal Pre-proof
Highlights
We examine the impact of lockdowns and increases in food prices on conflict.
Food-related conflicts increased after lockdowns in 24 African countries.
Increases in local prices led to rises in violence against civilians.
Providing urgent assistance for COVID-19 reduces violence and fatalities.
... The severe economic consequences from the 2020 coronavirus pandemic, climate change, and protracted conflicts have disrupted livelihoods across Sub-Saharan Africa [1]. Responses to such crises have been characterized by a growing emphasis on increasing and reallocating funding towards humanitarian interventions at the expense of planned development efforts [2]. ...
... The authors declare that they have no competing interests. 1 United Nations Childrens Fund, East and Southern Africa Regional Office, ESARO, Nairobi, Kenya. 2 World Health Organization, Regional Office for Africa, Brazaville, Congo. 3 Centre for Health Professions Education, Faculty of Health Sciences, North-West University, Potchefstroom, South Africa. ...
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