The other virus: Covid-19 and violence against civilians - Peace ...
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Peace Econ. Peace Sci. Pub. Pol. 2020; 26(3): 20200039
Charles H. Anderton*
The other virus: Covid-19 and violence
against civilians
https://doi.org/10.1515/peps-2020-0039
Received July 8, 2020; accepted July 14, 2020
Abstract: The article analyzes how Covid-19 might affect the risk of violence
against civilians (VAC) in two ways. First, I glean from Armed Conflict Location and
Event Data the quantity and types of Covid-related VAC attacks. Second, I present
possible economic channels by which Covid could affect mass atrocity risk. I apply
the channels to identify possible Covid-related economic risks for VAC in Demo-
cratic Republic of the Congo.
Keywords: coronavirus, mass atrocity, violence against civilians, Democratic Re-
public of the Congo, early warning
1 Introduction
Covid-19 has featured direct pandemic-related violence against civilians (VAC)
motivated by fear, greed, or suspicion (xenophobia) of people from identity
groups. For example, news outlets have reported Covid-related attacks against
Asians in parts of the world, Muslims in India, LGBTQ people in Uganda, and
political opponents of the Venezuelan regime. Covid-related VAC can also occur
through indirect channels as the pandemic disrupts economic, political, and social
activities, which can elevate VAC risk. For example, in the Early Warning Project’s
(2020) risk assessments for mass atrocities, low GDP per capita growth correlates to
greater mass atrocity risk. Hence, as the pandemic disrupts GDP per capita, and is
linked to xenophobia in various places, mass atrocity risk in some countries could
rise.
The spread of fear, greed, and xenophobia during pandemics represents the
“other virus” in this article. I address how severe this other virus might be for
*Corresponding author: Charles H. Anderton, Professor, Department of Economics and
Accounding, and Distinguished Professor of Ethics and Society, College of the Holy Cross,
Worcester, MA, USA, E-mail: canderto@holycross.edu2 C. H. Anderton
civilians in two ways. First, I glean from Armed Conflict Location and Event Data
(ACLED) the quantity and types of Covid-related VAC attacks. Second, I present
possible economic channels by which Covid could affect mass atrocity risk.1 I apply
the channels to identify possible Covid-related economic risks for VAC in Demo-
cratic Republic of the Congo.
2 Direct Covid-Related Violence Against Civilians
2.1 Worldwide
I compile ACLED data on Covid-related violence against civilians (VAC) from
March 1 to June 30, 2020 for almost 150 countries.2 VAC encompasses “violent
events where an organised armed group deliberately inflicts violence upon un-
armed non-combatants” (ACLED 2019, 11). VAC perpetrators include state forces
(military and police), nonstate groups (militias, protestors, rioters/mobs), supra-
national organizations (IGOs such as alliances), and private citizens. Judgement is
required to determine from ACLED’s notes whether a VAC event is Covid-related.
For example, consider the notes for a March 29, 2020 VAC event (id 7059889): “two
soldiers assaulted a street vendor … under the pretext of compliance with the
guidelines … decreed by the Angolan state to contain the coronavirus pandemic.” I
coded the event as Covid-related because the assault seemed related to corona-
virus guidelines. Consider, however, notes for an April 28, 2020 event (id 7083259):
In “Rio de Janeiro, a real estate agent was killed … by the Military Police … when
he was carrying a donation to a friend who was suffering the economic effects of
the coronavirus pandemic. Initially, the Military Police officer declared that he was
a suspect and a shoot-out broke out.” In this case, the man was seemingly attacked
because he was a suspect (presumably in a crime). That the man was bringing help
to a friend owing to the coronavirus seems tangential to the event, so I did not code
it as Covid-related.
Table 1 summarizes Covid-related VAC for the period. Of the 7,373 VAC
events, 440 (6.0%) were Covid-related. Of the 8949 estimated civilian fatalities
from the events, 105 (1.2%) were Covid-related. Covid-related VAC represents a
new form of VAC in the world, although less severe regarding fatalities per
case.3 Table 1 also shows that Covid-related VAC occurs disproportionately in
1 Scholars from their respective fields can do a better job than me on political, social, and other
impact channels.
2 Words like “coronavirus” and “Covid-19” begin to appear in ACLED notes in March 2020.The other virus: Covid-19 and violence against civilians 3
Table : Covid-related violence against civilians, March –June , .
Attacks and fatalities Victims Attackers
Total no. of attacks No. of Covid-related iden- Police
against civilians tity attacks
Total no. of civilian Racial/ethnic/religious/ Military
fatalities Indigenous forces
No. of Covid-related (.% of Gender (LGBTQ, women Political
attacks total) rape victims) militias
No. of Covid-related (.% of Police/officials/leaders Rebel/mili-
fatalities total) tant group
No. of Covid-related at- (.% of Healthcare workers Citizens
tacks in top mass Covid
killing risk countries attacks)
No. of Covid-related at- (.% of Other (foreigners, dis-
tacks in top mass Covid placed persons, migrant
killing risk countries attacks) workers, etc.)
Journalists
Source: Raleigh et al. () and ACLED at https://acleddata.com/data-export-tool/.
the top 25 and top 50 mass atrocity risk countries identified by the Early
Warning Project. The top 25 countries represent about 17% of states that ACLED
tracks, but they account for 57.5% of Covid-related VAC events. The top 50
countries (about 34% of ACLED states) account for 71.6% of Covid-related VAC
attacks.
The middle of Table 1 shows Covid-related VAC victim identity. Victim
identity is not always provided in ACLED notes, so one often cannot determine,
for example, whether looting was focused on an identity group. Hence, Table 1
likely undercounts identity-related attacks. Nevertheless, Table 1 shows that
many victim categories apply to Covid-related VAC including racial/ethnic/
religious/indigenous/gender (33 of 122 identity attacks), police or official ca-
pacity (12), healthcare worker (36), class/occupational/migrant (17), and jour-
nalist (24).
The right side of Table 1 shows Covid-related VAC attackers. Disproportion-
ately, police (320 of 440 or 72.7%) and the state involving police and/or military
forces (387 or 88.0%) conduct attacks. This suggests that new forms of training for
3 Fatalities per case is 1.2 for all VAC attacks and 0.2 for Covid-related attacks. Lack of fatalities
does not imply lack of severity. Notes for some nonfatal Covid-related attacks report beatings,
tortures, or rapes.4 C. H. Anderton
state forces, especially police, are needed in the Covid period. Next are attacks by
militia, rebel, and militant groups (38) and by regular citizens (38). Citizen attacks
are often directed against health workers owing to alleged fear that health pro-
viders spread the coronavirus. The total number of attackers (463) is greater than
the total number of attacks (440) because some attacks are conducted by more
than one perpetrator type (e.g., police and military forces), which suggests such
attacks are planned.
2.2 Country-Specific
While many countries have little Covid-related VAC, some show significant
increases in VAC owing to Covid. Table 2 shows countries with at least 10
VAC events in which at least one-third were Covid-related for the period.
Also included is data on Covid-related VAC conducted by the state (police
and/or military). For the nations in Table 2, the Covid period suggests a
significant “ramping up” of VAC, a large majority being state-perpetrated.
This is disconcerting for three reasons. First and most obviously, civilians are
attacked. Second, empirical evidence suggests that relatively mild VAC in the
present can lead to later more severe acts of VAC (Anderton and Ryan 2016).
Third, five of the eight countries in Table 2 are in the Early Warning Project’s
list of top 50 mass atrocity risk nations (Guinea, India, Kenya, Uganda,
Zimbabwe). Already at high mass atrocity risk, such countries could have
their risks aggravated by Covid-related VAC.
Table : Covid-related violence against civilians (VAC) for selected states, March –June ,
.
Country No. of VAC Covid-related VAC Covid-related VAC attacks by state Forces
attacks attacks (Police and/or military)
Guinea (.%) (%)
India (.%) (.%)
Kenya (.%) (%)
Liberia (.%) (.%)
South (.%) (.%)
Africa
Togo (.%) (%)
Uganda (.%) (.%)
Zimbabwe (.%) (%)
Source: Raleigh et al. () and ACLED at https://acleddata.com/data-export-tool/.The other virus: Covid-19 and violence against civilians 5
3 Economic Channels of Covid-Related Violence
Against Civilians
3.1 Macro-, Meso-, and Micro-Level Economic Variables
Table 3 summarizes possible Covid-19 effects on economic variables and, in turn,
how mass atrocity risk might be affected. For example, the first row under “Macro-
Level” indicates that Covid-19 harms GDP and GDP per capita (↓), which in turn
could increase mass atrocity risk (↑). Other macro, and meso- and micro-level,
variables are presented in the table.
While some variables in Table 3 can be easily interpreted, others require
explanation. For example, Covid-induced strains on government spending may
lead to fewer resources to provide safety to civilians in remote locations (↓), sup-
port peacekeeping operations (↓), and provide foreign aid (↓). The first two of these
channels could elevate mass atrocity risk, but the third could increase, decrease, or
Table : Hypothesized links between Covid-, economic variables, and mass atrocity.
Economic variables Hypothesized Impacton Mass atrocity
risk
Macro-Level
↓ GDP, GDP per capita, development (level, growth) ↑
↑ Unemployment ↑
Government spending/Budget deficits
↓ Non-Covid safety/security ↑
↓ Peacekeeping operations support ↑
↓ Foreign aid ↑/↓
↓ Trade and foreign direct investment ↑
↑ Inequality (especially horizontal) ↑
↑ Economic discrimination (incl. econ. scapegoating) ↑
Meso-Level
↓/↑ Social and antisocial capital ↑/↓
↓ IGO, INGO, NGO peace/humanitarian networking ↑
Micro-Level
Natural resource price/production shocks
↑ Oil shocks ↑/↓
↑ Minerals shocks ↑/↓
↑ Agricultural price/production shocks ↑/↓
↑ Refugee/IDP shocks ↑
↑ Migrant worker shocks ↑
↑ Family-level shocks (e.g., work, education, etc.) ↑
Strategic and tactical grassroots shifts by nonstate ↑/↓
actors6 C. H. Anderton
leave mass atrocity risk unchanged depending foreign aid context (e.g., whether
aid supports a mass atrocity-perpetrating actor or not). At the “Meso-Level”, Covid-
19 can disrupt networks of people producing goods and, in mass atrocity contexts,
rescuing vulnerable civilians and conducting other forms of peace promotion (e.g.,
by IGOs, etc.). Such networks can be conceptualized as social capital, diminution
of which could raise mass atrocity risk. Of course, mass atrocity architects and
perpetrators have networks of harming, which can be classified as antisocial
capital. If Covid-19 disrupts these networks, it would reduce mass atrocity risk.
Under “Micro-Level”, many shocks can be expected to increase mass atrocity risk
but, as will be seen for the Democratic Republic of the Congo below, mineral and
agricultural price shocks can increase or decrease mass atrocity risk depending on
context.
Most relationships between economic variables and mass atrocity risk in Ta-
ble 3 have received some empirical support in the literature. This does not mean
that every mass atrocity empirical study finds, for example, that less GDP, less
trade, etc. elevate mass atrocity risk, but some have. For example, the Early
Warning Project’s (2020) empirical analysis of mass atrocity risk finds that states
with low GDP per capita growth and low trade openness are at greater risk, all else
equal. As another example, some country-specific empirical studies find that
economic shocks to agriculture increase mass atrocity risk (e.g., Verpoorten 2012;
Gangopadhyay 2016).4
3.2 A Case Example: Democratic Republic of the Congo
Democratic Republic of the Congo (DRC) has the fifth highest risk for mass atrocity
in 2020 according to the Early Warning Project. One macroeconomic factor for
mass atrocity risk in Table 3 is development, which is sometimes proxied by infant
mortality rate. In the Early Warning Project’s statistical model, DRC’s high infant
mortality rate leads to high mass atrocity risk, all else equal. Two other macro-
economic variables that contribute to mass atrocity risk in the Early Warning
Project model are low GDP per capita growth and low trade openness. Covid-19’s
depressing effect on GDP and trade, and its increasing effect on mortality,
including infant mortality, could increase DRC’s mass atrocity risk.
There are also micro-level channels in Table 3 by which Covid could increase
violence against civilians (VAC). Research now exists on direct and indirect effects
of concentrations of natural resources such as diamonds and minerals (e.g., gold,
tantalum, tin, and tungsten) on rebel group VAC in DRC. The effects are not always
4 For a more extensive survey of empirical work on economic risk factors for mass atrocity, see
Anderton and Brauer (2020).The other virus: Covid-19 and violence against civilians 7
as obvious as first generation research on “lootable” resources and civil conflict
suggests, namely, that such resources increase the risk of civil violence and VAC.
For example, there are many artisanal mines in DRC, i.e., minerals that can be
mined labor-intensively using simple tools such as shovels and pick-axes (Krauser
2020, 4). Entry barriers are low for such operations, as distinct from high-barrier-to-
entry mining requiring industrial-scale machines. Hence, rebel groups can easily
enter and threaten civilians to extract value from the mines. Krauser (2020),
however, presents empirical evidence that locations in DRC with artisanal mines
are generally less likely to display rebel VAC. According to Krauser, civilians and a
rebel group in a mine location will tend to reach a truce in which the rebels tax
mine production in return for protection for the miners against looting and VAC by
other actors. The protection racket is beneficial to the rebels because it does not
need to carry out costly VAC and tax revenues are acquired. In Olson’s (1993)
theory, the rebel group becomes a “stationary bandit.” The miners gain too. They
are protected from VAC because the rebel group will not generally attack them
because it would disrupt tax revenues. Moreover, as long as the tax rate is not too
high, mining is profitable. When rebel groups contest each other for control of
mines, VAC can flare up, but otherwise the protection rackets tend to preserve a
tolerable peace.
But mineral prices can experience price shocks owing to government policies
or economic shocks. For example, the Dodd–Frank Act (passed by the US Congress
in 2010) required manufacturers to assess the mineral content of their products.
According to Cuvelier et al. (2014, 10), the legislation led to “… unemployment,
school abandonment, armed group recruitment, criminality, insecurity and
indebtedness” in DRC. Krauser (2020, 6) maintains that as the “prices of these
products [minerals] dwindled, rebels were deprived of important revenues. This, in
turn, decreased their appeals to protect miners in return for taxation and made
violent looting of civilians more profitable.” My point here is not to critique the
Dodd–Frank Act, but to highlight empirical evidence that declines in mineral
prices could elevate VAC risk in DRC. The indirect link of VAC in DRC with Covid-19
is that the pandemic is likely depressing demand and prices for most minerals, at
least in the short run. Gold, as a safe haven asset, is an exception. But prices for
other minerals, such as the three T’s mined in DRC (tin, tungsten, and tantalum),
initially trended downward since early 2020.5
5 The tin price (per metric ton) reached a recent high of $17,850 on January 20, 2020, and a recent
low of $13,250 on March 23, 2020 (Bloomberg 2020). The tungsten powder price fell from $34 (per
kg) in February 2020 to $31.60 on March 24, 2020 (Powder Metallurgy Review 2020). For the 90 and
180 days leading up to June 10, 2020, the US dollar price for tantalum delivery to the USA fell 0.87
and 1.01%, respectively (Asian Metal 2020).8 C. H. Anderton
Furthermore, research by Armand, Atwell, and Gomes (2020) suggests agri-
cultural channels by which Covid-19 could elevate Lord’s Resistance Army (LRA)
VAC in several African nations, including DRC. Armand, Atwell, and Gomes find
that radio messages that encourage defections from the LRA are effective, which
leads to less LRA VAC. The effects of radio, however, are contextual; the prices of
two commodities, cotton and groundnut, affect the results. The effects of the two
commodities work in opposite directions. Cotton is labor-intensive. When cotton
prices rise, returns to workers in cotton also rise, thus reducing incentives for
people to work for the LRA. This in turn reduces LRA VAC. Groundnut according to
Armand, Atwell, and Gomes, however, is highly lootable by the LRA. When
groundnut prices rise, LRA looting increases and associated VAC also rises. Data
show that global cotton prices per pound fell from $0.791 on January 2, 2020 to a
local low of $0.635 on April 1, 2020 (Federal Reserve Bank of St. Louis 2020a).
Meanwhile, over this same period, groundnut prices per metric ton rose from
$1890.50 to $1914.50 (Federal Reserve Bank of St. Louis 2020b). There is evidence
that Covid-19 hurt the price of cotton, but not all commodity prices have been
pushed down. According to Purdy (2020) peanut-related products have been in
high demand during the Covid-19 period. Based on Armand, Atwell, and Gomes’s
empirical evidence, each of these Covid-related price moves could elevate LRA
harm to civilians.
4 Conclusion
I present evidence for direct and indirect (via economic channels) impacts of
Covid-19 on violence against civilians (VAC) worldwide and for specific countries. I
conclude with two points. First, the article’s results are tentative, but pointing to
possible direct and indirect channels now may lead to better “early warnings” for
mass atrocity prevention efforts. Mass atrocities rarely appear as fully scaled up or
“mass” right from the beginning. Instead, they tend to start out with relatively low-
level acts of VAC. Following ACLED’s close to real time data on VAC events
(including those spurred by Covid-19) along with data on trends in country-specific
economic (and other) variables can thus help policymakers identify possible
emerging mass atrocity risks in time for preventive efforts to make a difference.
Second, as analysis of DRC shows, Covid-19’s possible effects on economic
channels and resulting VAC can be subtle. Hence, it can be valuable to drill down
into case-specific empirical studies to better discern the importance of contextual
variables for assessing Covid-related VAC risks.The other virus: Covid-19 and violence against civilians 9
Acknowledgments: I am grateful to Jurgen Brauer, two anonymous reviewers, and
the editor for helpful comments. The usual disclaimer applies.
References
ACLED. 2019. Armed Conflict Location & Event Data Project (ACLED) Codebook. https://acleddata.
com/#/dashboard (accessed June 10, 2020).
Anderton, C. H., and J. Brauer. 2020. “Mass Atrocities and their Prevention.” Journal of Economic
Literature. https://www.aeaweb.org/articles?id=10.1257/jel.20201458&&from=f.
Anderton, C. H., and E. V. Ryan. 2016. “Habituation to Atrocity: Low-Level Violence against
Civilians as a Predictor of High-Level Attacks.” Journal of Genocide Research 16 (4): 539–62.
Armand, A., P. Atwell, and J. F. Gomes. 2020. “The Reach of Radio: Ending Civil Conflict Through
Rebel Demobilization.” American Economic Review 110 (5): 1395–1429.
Asian Metal. 2020. Ta. Available: www.asianmetal.com/TantalumPrice/Tantalum.html?f
(accessed June 10, 2020).
Bloomberg. 2020. Tin. www.bloomberg.com/quote/LMSNDS03:COM (accessed June 9, 2020).
Cuvelier, J., S. van Bockstael, K. Vlassenroot, and C. Iguma. 2014. Analyzing the Impact of the
Dodd–Frank Act on Congolese Livelihoods. New York: Social Science Research Council.
Early Warning Project. 2020. Early Warning Project. earlywarningproject.ushmm.org (accessed
June 11, 2020).
Federal Reserve Bank of St. Louis. 2020a. Global Price of Cotton. fred.stlouisfed.org/series/
PCOTTINDUSDM (accessed June 10, 2020).
Federal Reserve Bank of St. Louis. 2020b. Global Price of Groundnuts. fred.stlouisfed.org/series/
PGNUTSUSDM (accessed June 10, 2020).
Gangopadhyay, P. 2016. “Economic Foundations of Religious Killings and Genocide with Special
Reference to Pakistan, 1978–2012.” In Economic Aspects of Genocides, Other Mass Atrocities,
and their Prevention, edited by C. H. Anderton and J. Brauer, 510–35. New York: Oxford
University Press.
Krauser, M. 2020. “In the Eye of the Storm: Rebel Taxation of Artisanal Mines and Strategies of
Violence.” Journal of Conflict Resolution. https://doi.org/10.1177/0022002720916824.
Olson, M. 1993. “Dictatorship, Democracy, and Development.” The American Political Science
Review 87 (3): 567–76.
Powder Metallurgy Review. 2020. Manufacturing Slowdown and Coronavirus Impact Metal Prices.
www.pm-review.com/manufacturing-slowdown-and-coronavirus-impact-metal-prices/
(accessed June 9, 2020).
Purdy, C. 2020. “Amid Food System Instability, the Humble Peanut is a Winner.” Quartz, April 27,
2020. qz.com/1846525/the-strength-of-peanuts-during-covid-19-could-be-bad-for-fashion/
(accessed June 10, 2020).
Raleigh, C., A. Linke, H. Hegre, and J. Karlsen. 2010. “Introducing ACLED-Armed Conflict Location
and Event Data.” Journal of Peace Research 47 (5): 651–60.
Verpoorten, Marijke. 2012. “Leave None to Claim the Land: A Malthusian Catastrophe in Rwanda?”
Journal of Peace Research 49 (4): 547–63.You can also read
