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A “Good” Samaritan? The Geopolitics of Russia’s Covid-19 Assistance 

Mariya Omelicheva1 
National War College 

  
 

 

 

Abstract 

Between March and December 2020, more than three dozen states received various types of 

COVID-19 assistance from Moscow. The Russian government emphasized a humanitarian 

character of what has become the largest package of emergency aid since Russia's independence. 

The Western governments and commentators cautioned that Moscow had strategic and nefarious 

motives in choosing the recipients of its COVID-19 aid. This study theorizes humanitarian aid 

allocations by authoritarian states and tests theoretical expectations using novel data on Russia's 

COVID-19 aid allocations. Far from being driven exclusively by humanitarian concerns, Russia 

has used humanitarian assistance to project power on the global stage and support diverse political 

objectives. Moscow's use of humanitarian aid for geopolitical benefits has not been a critical 

disruptor in the humanitarian system by itself; however, jointly with other instruments of foreign 

policy, Russia's approaches to foreign assistance can be detrimental to the future of the 

international humanitarian system. 

 

  

  

 
1Dr. Mariya Y. Omelicheva is a Professor of Strategy at the National War College in Washington, DC.  All opinions 

expressed in the article are her own and do not represent an official position of the US government or US 

Department of Defense. 



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Introduction 

In late March 2020, several IL-76 military planes began taking off from an airbase in the Moscow 

region, heading to the COVID-19-stricken regions of Italy. The launch of a humanitarian operation 

dubbed “From Russia with Love” followed a telephone conversation between Russia's President 

Vladimir Putin and Italian Prime Minister Giuseppe Conte. Framed by the Russian media as a 

goodwill gesture in support of the Italian people, the whole operation, which included the transfer 

of medical equipment, supplies, doctors, nurses, and decontamination personnel, received a large 

dose of criticism within the Western media space. La Stampa, one of the oldest Italian newspapers, 

published a report questioning Russia’s motives, impugning the utility of aid, and warning of 

security breaches due to the involvement of the Russian military. La Stampa's assessments were 

cited widely in online and print media worldwide (BBC News 2020).  

 

Moscow’s decisions to send COVID-19 assistance to Italy, Serbia, Iran, Venezuela, and select 

countries of the Maghreb region in Africa, while sidelining other countries have raised questions 

about the Kremlin's rationale for choosing where to send its aid. The Western media has accused 

Russia of using the “politics of generosity” and “mask diplomacy” to blanket its unchecked 

appetite for influence in “good Samaritan” terms (Giusti and Ambrosetti 2022). However, the 

extent to which Moscow’s geostrategic considerations have polluted the principles of neutrality, 

impartiality, and need in its humanitarian aid allocations has not yet received a systematic 

examination. This is partly due to the lack of comprehensive data on the levels of Russia's 

development and humanitarian aid. In 2007, Russia was invited to join the Organization for 

Economic Co-operation and Development (OECD). As part of the accession process, Russia was 

reporting its official development assistance to OECD’s Development Assistance Committee 

(DAC). The accession process was postponed in the wake of Russia’s annexation of Crimea in 

2014 and fully terminated following Moscow’s invasion of Ukraine in February 2022. As a 

consequence, there has been limited information on Russia’s humanitarian and development 

assistance.  

 

The COVID-19 pandemic, which left no country unscathed, provided an opportunity for collecting 

open-source data on Russia’s COVID-19 assistance. The Russian government's information blitz 

to publicize its humanitarian efforts simplified the process of tracking down its transfers of 

personal protective equipment (PPE) and medical supplies. The goal of this article is to theorize 

and statistically analyze Russia’s COVID-19 aid allocations in 2020. 

 

There are several reasons for studying Russia’s motives for humanitarian assistance. The demand 

for humanitarian aid will continue to rise due to intensifying climate change-induced natural 

disasters as well as conflict-related complex emergencies. However, the growing demand for aid 

is unmatched by its supply. The scarcity of funding to respond to crises has rekindled the old debate 

about states’ motives for humanitarian aid. The surge of the so-called “new”2 or “emerging” donors 

in the humanitarian milieus has also reinvigorated a conversation about the impact of domestic 

factors on the foreign policies of donor countries. While some humanitarian stakeholders have 

 
2 The term “new donor” is somewhat misleading. Many so-called “new” donors, including Russia and China, have 

acted as providers of assistance for decades. Some, like Russia, have re-emerged in the development and humanitarian 

assistance scene after a period of hiatus. What unites “new” donors is that they operate largely outside the regulatory 

and reporting frameworks of the OECD Development Assistance Committee (DAC). 



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welcomed and encouraged “new” donors’ contributions to humanitarian causes, others have 

warned that their interests and practices are often contrary to the humanitarian principles and, 

therefore, threatening to the integrity of the humanitarian system itself (Fuchs and Klann 2013).  

 

This apprehension of the “new” donors’ foreign aid has been reinforced by the evidence of the 

leading authoritarian governments, like those in Russia and China, hijacking the concept of “soft 

power,” to boost their global image, influence global public opinion, and increase their leverage 

over other states (Walker 2016). Development and humanitarian assistance, which have been 

viewed as important instruments of “soft power” projection, have been added to the modern 

authoritarian toolkit (Blair, Marty, and Roessler 2022). For example, Russia’s Foreign Minister 

Sergei Lavrov has repeatedly alluded to his country’s participation in international aid programs 

as one of Russia’s “soft power” tools (Kiseleva 2015). 

 

There are indeed several reasons for why “new” donors’ decisions about aid can be different from 

those of “traditional” donors. First, many “new” donors are not bound by the regulatory framework 

of the DAC, which serves as the major agency to set aid agendas and shape the direction of the 

global aid flow. This means that “new” donors are not fully bound by the DAC humanitarian 

principles and aid reporting requirements. This, in turn, offers these countries a greater opportunity 

to use aid in their interests. Second, the majority of “new” donors are below the levels of economic 

development of DAC donors, especially in per capita terms. This also provides an incentive for 

“new” donors to pursue their self-interest more than DAC donors. Third, the official aid 

philosophies of many “new” donors (with the exception of Russia) include a principle of non-

interference in the domestic affairs of the recipient states (Fuchs and Klann 2013).  

 

This study theorizes these differences in aid motivations by tracing them to certain characteristics 

of democratic versus authoritarian regimes and tests theoretical expectations using novel data on 

Russia’s COVID-19 aid allocations. It begins with an overview of the literature on humanitarian 

aid, followed by the presentation of a theoretical framework for authoritarian aid allocations. The 

last section describes the research design and methodology for the study, followed by the 

presentation and discussion of findings. 

 

 

Determinants of Humanitarian Aid 

 

International aid broadly falls into three categories: development assistance, military aid, and 

humanitarian assistance (also known as emergency relief aid). Development assistance consists of 

financial flows, technical assistance, debt relief operations, and other activities aimed at promoting 

economic development and the welfare of developing countries. The major objective of 

development assistance is to meet United Nations’ (UN) Sustainable Development Goal 1 – End 

Poverty in All Its Forms Everywhere (United Nations 2018), although its various programs and 

activities contribute to political, environmental, and social developments of the recipient states. 

Military aid encompasses the transfer of military articles, services, and financial flows in support 

of sales of military equipment and training as well as military education. These types of military 

assistance are designed to foster international stability, enhance the national security of donor 

states, assist recipient governments, deter or combat security threats, and share the burden for 

mutual defence. For some governments, military aid is also a means of diplomacy to cultivate 



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goodwill and even persuade foreign governments to take action consistent with the donors’ 

interests.  

 

Contrary to development and security assistance that tends to be pre-planned, long-term, and 

targets underlying structural issues that hinder countries’ development and limit their capacity to 

address economic and security challenges, humanitarian assistance is designed to offer short-term 

rapid relief in response to an incident or event threatening human lives. Its goal is to alleviate 

human suffering in the wake of natural disasters, technological catastrophes, and conflicts often 

classified as “complex emergencies,” which often combine natural disasters and conflict thus 

exacerbating vulnerabilities of those affected (Fink and Silvia Redaelli 2009). 

 

The COVID-19 pandemic has not only aggravated the existing humanitarian emergencies 

triggered by armed conflict and natural disasters, it has also disrupted livelihoods and caused 

economic devastation and deaths in countries previously unaffected by humanitarian disasters. 

According to the UN Under-Secretary-General for Humanitarian Affairs and Emergency Relief 

Mark Lowcock, in 2020, the COVID-19 pandemic pushed the number of people needing 

humanitarian assistance by 40 percent compared to 2019 (UN OCHA 2021). Another 40 percent 

increase in humanitarian aid was projected for 2021 due to the combination of the virus and its 

secondary impacts, such as falling incomes, rising food prices, and interrupted vaccination 

programs (World Health Organization 2020). International donors have been challenged to come 

up with additional funding for critical response efforts to the outbreak while fighting the health 

crises at home. Some of this additional humanitarian aid has been channelled toward providing 

basic subsidence needs (food, shelter, etc.) in complex emergencies exacerbated by the pandemic. 

Other types of assistance have been used to save peoples’ lives threatened by the COVID-19 virus 

by providing support for testing and disease-surveillance capacities of countries, the supply of 

medical and personal protection equipment to improve these countries’ rapid response capacity, 

and vaccine efforts.  

 

Humanitarian assistance has been regarded as a distinct form of assistance due to its ethical 

foundation in humanitarian law. Reflected in the UN Resolution A/RES/46/182, “humanity, 

neutrality and impartiality” have been the guiding principles of humanitarian assistance (United 

Nations 1991). These principles seek to remove political conditionality from donor governments’ 

considerations of humanitarian assistance. The scholarship on humanitarian aid (the majority of 

which has been conducted on traditional donors such as the US) has found the ongoing importance 

of need as a driver for humanitarian aid decisions but also pointed out other motives, including 

countries’ self-interest for explaining their aid allocations (Boussalis and Caryn Peiffer 2011; 

Kevlihan, DeRouen, and Biglasier 2014). Drawing on the studies of foreign aid, it is possible to 

classify states’ motivations for supplying emergency aid into the needs-based, interest-based, and 

merit-based logics (Dollar and Levin 2006).  

 

The need-based logic is consistent with the humanitarian principles of emergency relief. Donors’ 

decisions concerning assistance are determined by the needs of recipient countries, usually 

measured by the total number of people affected by a natural disaster or the number of casualties 

and displaced persons in conflict (Drury, Olson, and Van Belle 2005; Fink and Redaelli 2009; 

Raschky and Schwindt 2012). The greater the human suffering, the more humanitarian aid is 

expected to alleviate it (Fuchs and Klan 2013).  



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Under the interest-based framework, humanitarian aid is deemed to be similar to other types of 

assistance and therefore influenced by a range of geostrategic considerations (Alesina and Dollar 

2000; Drury, Olson, and Van Belle 2005; van der Veen 2011). Some studies have found that donors 

are driven by political and security interests and provide assistance to their allies, former colonies, 

and politically-aligned governments (Alesina and Dollar 2000; Drury and Olson 1998; Boussalis 

and Peiffer 2011). Still, other studies have shown the impact of commercial interests and donors’ 

own political processes on aid allocation, such as election cycles and the budgetary situation 

(Eisensee and Strömberg 2007; Dreher, Nennenkamp, and Thiele 2011; Fleck and Kilby 2006; 

Fuchs and Vadlamannati 2013; Hoeffler and Outram 2011). 

 

The merit-based framework is premised on the idea that donors are concerned about the 

effectiveness of aid and therefore favour the most deserving recipients with the worth determined 

by the quality of their governance, strength of institutions, and the density and effectiveness of the 

network of non-governmental (NGO) and international organizations that are present in the 

affected countries. Thus, it has been found that the presence and strength of humanitarian 

stakeholders and their infrastructure—NGOs, specialized international humanitarian agencies, 

donor states’ agencies, early warning systems, and rapid response units—play a decisive role in 

the allocation of humanitarian aid (Olsen, Carstensen, and Hoyen 2003). 

 

In addition to the aggregate levels of humanitarian assistance, the scholarship on humanitarian aid 

has looked at sector-specific types of aid and different categories of donors. The idea is that some 

sector-specific assistance, such as health aid to contain the spread of contagious and deadly 

diseases, is less afflicted by donors’ geostrategic interests (Neumayer 2005). The literature has 

also impugned the motivations of the so-called “new” donors, accused of practicing “rogue aid” 

driven by the interest in national wealth extraction and the generation of political leverage over the 

recipient states (Naím 2007). 

  

The “new” donors are a rather heterogenous group of low- and middle-income countries, autocratic 

regimes, and donors operating outside the DAC (Fuchs and Klann 2013; Dreher and Fuchs 2015; 

Kragelund 2008). Many of these donors are not “new” to the donor community; for example, China 

and Russia, launched their aid programs at the beginning of the Cold War era (Asmus, Fuchs, and 

Muller 2020). Many, like Brazil, South Africa, and India, emphasize “South-South” cooperation 

and appear to be reluctant to be seen as reproducing traditional donor-recipient hierarchies 

(Rowlands 2008). In addition, the practices of these “new” donors have been under heightened 

attention fuelled by a suspicion that these donors abuse humanitarian and development assistance 

to push their national interests (Fuchs and Klann 2013).  

 

 

Theorizing Humanitarian Aid by Authoritarian States 

 

As noted in the previous section, there is no consensus in aid scholarship on the decision-making 

logic that determines the choices of the recipients of humanitarian aid. In theory, humanitarian aid 

is supposed to be driven by the principles of neutrality and impartiality, but in practice, it has been 

affected by the same considerations of strategic interest as development assistance and military 

aid. Furthermore, the emergence or comeback of the so-called "new” donors has added a new layer 



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to the debate over the aid motivations of autocratic and less-wealthy donors. While some studies 

have dubbed assistance by authoritarian donors as “rogue aid” spurred exclusively by self-interest 

(Naím 2007), others disputed this finding, noting minimal or no differences in the aid allocation 

of autocratic and democratic donors (Dreher and Fuchs 2015; Dreher, Nennenkamp and Thiele 

2011).  

 

One of the chief conclusions that stems from the broader literature on international relations and 

foreign policy analysis is that internal differences between democratic and autocratic states bear 

important implications for their foreign policy decisions. This tradition informs the studies of 

democratic peace that have attributed states' decisions concerning the use of force to the makeup 

of democratic institutions, culture, and informational processes (Doyle 1983; Rosato 2003). 

Differences in states’ domestic makeup have been used to explain a range of strategic decisions 

(de Mesquita and Smith 2012). In line with this strand of scholarship, this study begins with the 

premise that domestic politics strongly influence foreign policy preferences, including those 

concerning donors’ decisions about humanitarian aid. The second assumption is that governments 

and individual decision-makers are rational actors seeking to maximize their utility. Their 

paramount interest is to stay in power. All actions of political elites, whether in the domestic or 

foreign policy realm, are shaped by their desire to retain political power (de Mesquita et al. 2003). 

 

While the humanitarian aid literature places a higher premium on the properties of disaster events 

and the characteristics of recipient countries, this study contends that foreign policy choices with 

regard to humanitarian aid are coloured by the governments’ interest in political survival. 

Important variations in the nature of domestic constraints placed on the democratic and autocratic 

leadership create different incentive structures for their leadership and lead to varying policy 

patterns. The key characteristic of autocratic regimes3 that distinguishes them from democratic 

states has to do with the size of the “winning coalition” that propel autocratic leaders to power and 

keep them at the helm. In autocracies, the winning coalition—a group of regime insiders, also 

known as the “selectorate,” whose support is necessary to sustain the leader in office—is small 

(Peceny and Butler 2004; de Mesquita et al. 2003). To sustain the winning coalition’s loyalty and 

support, autocratic leaders rely on the distribution of private goods and other favours doled out to 

the members of the authoritarian leaders’ inner circles.  

 

In democracies, winning coalitions are large and more diverse as far as their private interests are 

concerned, making it all but impossible for the democratic government to engage in the targeted 

distribution of goods to the individual members of the winning coalition. Subsequently, democratic 

policies are more attuned to the needs and interests of the larger swaths of the population that they 

seek to satisfy through the provision of public goods, such as social welfare, security, and 

education. This is not to say that democratic governments do not engage in the provision of private 

goods to a range of special interests, yet, greater accountability of democracies compels them to 

use resources efficiently and favour the provision of public goods in relative terms (Bader, 

Gravingholt, and Kastner 2010). 

 

Transferring these differences between autocracies and democracies to the logic of foreign policy-

making, there are several plausible theoretical explanations for the decisions about humanitarian 

 
3 It is important to note that autocracies vary on a number of dimensions that bear implications for their foreign 

policy decisions (see for example Weeks 2014).  



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aid by autocratic states. Since the autocratic incentive structure is based on the distribution of 

private benefits and the reduction of private costs to the winning coalition, autocratic donors prefer 

dealing with likeminded autocrats in the recipient states because both governments can benefit 

from the lack of accountability to their respective populations. From the autocratic donor’s 

perspective, it is easier to manipulate the autocratic recipient’s decisions affecting the distribution 

of various benefits. From the autocratic recipient’s standpoint, it is easier to get private benefits 

from the aid, investment, or trade policies of the autocratic donor in exchange for various policy 

concessions (Bader, Gravingholt, and Kastner 2010). There is strong empirical evidence that 

corroborates this theoretical claim. During the Cold War, for example, the Soviet Union's 

relationships with developing countries were guided by their shared commitment to the communist 

ideology and their discontent with Western capitalism and colonialism (Jaster 1969). This leads to 

the following expectation: 

 

H1. Ceteris paribus, autocratic donors are more likely to provide humanitarian 

assistance to other autocratic states. 

 

To recall, political survival is the dominant motive of both democratic and autocratic political 

leadership. However, the costs of losing political office are particularly acute in authoritarian 

regimes where removal from office may result in imprisonment, exile, asset forfeiture, and even 

death. Other than natural causes (death or impairment), the loss of office by an autocratic leader 

and those who rely on them typically occurs due to political destabilization – whether through 

people’s revolt or a coup by regime insiders. Autocratic regimes are, therefore, acutely sensitive 

to political instability and prioritize order and security over other considerations. This preference 

for stability and order is not limited to the domestic context of authoritarian regimes but applies to 

foreign contexts as well; however, autocrats are more concerned about the destabilization of fellow 

autocratic governments than democratic regimes. First, lacking democratic mechanisms and 

processes to respond to political crises, autocratic governments are more likely to resort to 

repressive means to quell threats to their regime. The state-led violence, in turn, can lead to 

escalation. Second, the toppling of an autocratic government presents an opportunity for installing 

a democratic regime – an undesirable prospect for other autocratic leaders. Therefore, authoritarian 

governments will be interested in the stability of autocratic governments abroad. 

 

Humanitarian disasters are the types of exogenous shocks that have been associated with the 

political destabilization of affected countries. Humanitarian disasters reduce countries’ wealth and 

deplete their resources, increasing the likelihood of political conflict (Nel and Righarts 2008; 

Homer-Dixon 2010). While humanitarian disasters affect ordinary people the most, it is the 

dissatisfied members of the winning coalition, or political elites excluded from the winning 

coalition, that mobilize people by directing their grievances against the authorities responsible for 

disaster relief. For autocratic regimes with greater economic and administrative resources, this 

moment of domestic instability in another country not only bodes potential losses, but can also 

bring about permanent changes in the government structure of the affected state that will curtail 

the channels for private benefit extraction (Bader, Gravingholt, and Kastner 2010). Furthermore, 

disaster-affected states are vulnerable to foreign intervention, including the larger donor states that 

have sought to implement their ideological (democratization) agenda with the provision of 

humanitarian aid (Curti 2001). The prospects of a regime change in disaster-stricken countries do 

not sit well with the authoritarian states. Subsequently, the latter will be attuned to the magnitude 



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of disasters and are more likely to provide aid to the most affected countries, less for humanitarian 

reasons and more due to concerns regarding the destabilization of the affected states. This 

discussion suggests the following hypothesis: 

 

H2: Ceteris paribus, autocratic states are more likely to provide humanitarian aid to 

other autocratic governments gravely affected by humanitarian disasters. 

 

Countries affected by disasters are vulnerable to political discontent to a different extent. A high 

level of development in the affected country reduces its susceptibility to political and economic 

crises precipitated by a disaster event; it also increases the country’s ability to cope with the 

consequences of the disaster (Anbarci, Escaleras, and Register 2005; Fink and Redaelli 2009; Kahn 

2005). Therefore, robust socioeconomic indicators of the affected state may reduce the risk 

associated with instability, leading to decreased humanitarian assistance.  

 

H3. Ceteris paribus, stronger socioeconomic background of the affected states will 

reduce the probability of humanitarian assistance by autocratic donors. 

 

To reiterate, autocratic stability is a function of the stability of the winning coalition that is 

sustained by the continued provision of private benefits to its members. Foreign relations—

including trade—is an important avenue for extracting rents for private distribution among the 

members of the ruling elite. The reduction in import/export flows that occurs as a consequence of 

political or economic crises in the trading partners is an unwelcome development for autocratic 

elites who get a cut from the economic interchanges. This suggests the following hypothesis: 

 

H4: Ceteris paribus, trading partners of autocratic states are more likely to receive 

humanitarian assistance. 

 

Although politics, colonial history, and a host of other reasons might prevent the autocratic states 

from trading with their closest neighbours, the easiest market access is in countries that geography 

puts nearby. Proximity, therefore, plays a role in the calculus of costs and the benefits of economic 

and humanitarian engagements by the autocratic states. Logistical costs can be minimized by 

helping recipients within closer proximity to autocratic states' borders (Fuchs and Klann 2013). 

When it comes to the consequences of disasters, including pandemics, autocratic donors will be 

interested in containing the destabilizing spread of disease from nations in their neighbourhood by 

offering humanitarian assistance toward the treatment and control of that disease (Boussalis and 

Peiffer 2011).  

 

H5: Ceteris paribus, autocratic states are more likely to provide humanitarian aid to 

the affected countries in their neighbourhood. 

 

Lastly, aid-related decisions are not made in isolation from considerations about the broader 

strategic context of the autocratic states, which include national security interests, economic 

interests, and broader political interests, all of which have bearing on the stability of the ruling 

administration. Therefore, humanitarian assistance can be used to signal the autocratic donor’s 

broader interests; for example, it can be used to reward countries willing to align their policies 

with the interests of the donor states. Alternatively, it can be used in support of commitments made 



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by the autocratic donors to members of alliances. In the case of Russia, multiple observers have 

noted that Russia used its COVID-19 assistance to reward countries that were Moscow’s customers 

in the arms trade. Russia’s arms sales have been its main export commodity, apart from oil and 

gas exports. Arms transfers have been key to Moscow’s image as a world power and an essential 

tool for projecting its global influence.  

 

H6a: Autocratic donors are likely to use humanitarian aid allocations to reward 

countries who support the donors’ foreign policies.  

H6b: Autocratic donors are likely to use humanitarian aid allocations to countries 

who are members of alliances with the donor states. 

H6c: The customers of Russia’s arms sales are more likely to be the recipients of 

its COVID-19 assistance. 

 

 

Research Design 

 

Academic and policy communities began taking an interest in Russia's role in the donor 

community following Moscow’s highly publicized involvement in the Syrian and Venezuelan 

humanitarian crises and its COVID-19 aid to Italy. Russia has re-emerged as a donor following 

the aid hiatus of the 1990s when it was a net recipient rather than a contributor of development 

and humanitarian assistance. Yet the history of Russia’s aid programs goes back to the post-World 

War II period when the Soviet regime invested lavishly in various infrastructure projects in South 

Asia and the Middle East (Berliner 1958; Rai 2018). The levels of aid, which fluctuated during the 

Soviet period, declined precipitously in the years preceding the Soviet Union’s dissolution. In the 

1990s, Russia curtailed its aid activities; they picked up again in the 2000s following a decade-

long economic growth spurred by the high petroleum prices. 

 

It was also in the 2000s that Russia began establishing a regulatory and institutional framework 

for development and humanitarian assistance. In 2007, Moscow adopted its first concept on 

development assistance reflecting the Millennium Development Goals (Rakhmangulov 2010). The 

document was updated in 2014 with a presidential decree that approved the current concept in the 

area of international development assistance (President of the Russian Federation 2014). There is 

no single federal agency responsible for the implementation of assistance policies. The Ministry 

of Finance, the Ministry for Civil Defense, Emergencies, and the Elimination of Consequences of 

Natural Disasters, Defense Ministry, the Federal Agency for Commonwealth of Independent States 

Affairs, Compatriots Living Abroad, and International Humanitarian Cooperation 

(Rossotrudnichestvo) along with a number of public foundations and state-backed NGOs are 

engaged in the development and humanitarian aid administration (Velikaya 2018). 

 

When the COVID-19 pandemic spread around the world, Russia began sending COVID-19 test 

kits, masks, protective gear, and medical personnel to countries that reported spikes in the spread 

of infections. The Russian government presented its foreign aid as an act of generosity delivered 

at times of hardship to countries in need (Zykov 2020). For example, following Moscow’s delivery 

of COVID-19 aid to Italy, Russian diplomatic Twitter went into overdrive publicizing the shipment 

as Russia’s “humanitarian gesture” under the hashtag #RussiaHelps (Gigitashvili 2020). Russia's 

own concept of international assistance exhibits a mix of goals ranging from humanitarian 



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objectives, such as the elimination of natural disasters' consequences, to pragmatic political aims, 

which include bolstering Russia’s influence on global processes (President of the Russian 

Federation 2014). 

 

To collect data on Russia’s COVID-19 donations (most of which were in-kind), a search algorithm 

was created and applied to the LexusUni and additional digital sources to identify open-source 

data on the transfers of COVID-19 aid by Moscow. Each donation was coded as an event with 

the date, donor, recipient, type and volume of donation, and other characteristics of the event 

recorded. For a COVID-19 donation to be recorded in the dataset, it had to be confirmed in at 

least two different sources. To standardize donations into their dollar equivalents, a reference 

list of values for purchasing various types of medical equipment and supplies was defined  

(see Appendix I Table 4 for examples of conversion and total level of donations supplied  by 

Russia). The reference list contained the lowest and highest known market values for each 

product that were used to convert the known quantities of such items as COVID-19 tests, PPE, 

and ventilators into their dollar equivalents. The lowest and highest totals (based on the lowest 

and highest market values respectively) were calculated and aggregated over the year for each 

recipient of COVID-19 aid (see Appendix I Table 3). Afterwards, a mean value was identified 

for each state year. Analyses were conducted on the mean as well as the lowest and highest 

values of COVID-19 assistance.4  

 

Aid scholarship identifies two stages of decision-making in aid allocation (Boussalis and 

Peiffer 2011; Kevlihan, DeRouen, and Biglaiser 2014). First is the "selection" stage, where a 

donor decides which countries will receive aid and which ones will be bypassed. The second 

is the "outcome" stage, and it involves decisions about the amount of aid distributed to states 

identified as aid recipients in the first round. Consistent with the literature, this study tests the 

determinants of Russia’s COVID-19 aid in two stages. In the first (selection) stage, the 

dependent variable is whether the COVID-afflicted country received any aid from Moscow at 

all. Countries selected for COVID-19 assistance by Russia were coded as “1 and “0” 

otherwise. In the second (outcome) stage, the dependent variable is the total volume (in USD) 

of COVID-19 aid allocated by Russia.   

  

Independent Variables 

 

The scholarship on humanitarian assistance typically measures the magnitude of a disaster by 

the number of fatalities or the number of people being affected. It is expected that more 

catastrophic events attract more post-disaster aid. Consistent with the aid literature, the total 

number of deaths due to COVID-19 appears to be an appropriate measure of the severity of 

the pandemic. However, the different approaches to counting COVID-related deaths used by 

 
4 The prices were not adjusted for inflation for the following reasons. The span of the study is short (10 months of 

2020). During this time, Russia experienced a 3.38% annual inflation. However, a bulk of its COVID-19 donations 

were allocated early in 2020, and I assume that the change in prices for Russian PPE and medical supplies would be 

insignificant to affect the results of the study. There is, however, a related challenge of using in-kind donations for 

inferring a country’s motivations for aid, namely, the structure of donations. Arguably, airlifting military medical 

personnel (which is difficult to translate into a dollar value) signifies a higher priority of a recipient state for Russia 

than a country that receives gloves and masks. I acknowledge this as one of the data limitations and invite further 

research into the ways in which the structure of in-kind donations reflects a donor’s motivations for aid. 



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countries raise concerns about the reliability of this measure.5  Therefore, this article opted 

for the COVID-19 mortality rate to be displayed in percentage terms as a measure of the 

pandemic’s severity in a country. The metric is calculated as a ratio of the total deaths to the 

number of infected individuals. Since Russia allocated most of its COVID-19 assistance in 

the early stages of the pandemic—in late spring and early summer of 2020—the half-year 

total deaths estimate, as of July 1, 2020, was used in the study. Our World in Data is the 

source of data that was consulted on the number of COVID-19 deaths (Ritchie et al. 2020). It 

is expected that the higher number of deaths due to COVID -19 will be associated with both a 

decision to provide aid and the higher volume of aid disbursed to the affected country. 

Socioeconomic background that conditions decisions about aid allocation is typically 

measured by a country’s GDP per capita. This study uses Gross Domestic Product per capita 

in 2019, measured in millions of USD logged in the statistical analysis of Russia’s COVID-

19 assistance; this data comes from the World Bank (2019). 

 

Similar to other disasters, the COVID-19 pandemic affected economic relations, including 

trade, among countries. It was hypothesized that autocratic donors were sensitive to losses 

due to interruptions in economic exchanges and would be motivated to assist countries with 

greater economic ties to the donor state. The study uses the total volume of trade with Russia 

in 2019 measured in thousands of US dollars; this data comes from the UN Statistics Division 

(2020). 

 

The proximity of recipient states to Russia was measured by the distance from Moscow to the 

country's capital in km (Gleditsch and Ward 2001). The expectation is that the shorter the 

distance of a country’s capital to Moscow, the more likely it is to receive Russia’s COVID -

19 aid. To measure the regime type, this study uses V -Dem’s interval-scale index for liberal 

democracy for 2019 (Coppedge 2020). The expectation is that states with higher values of 

liberal democracy are less likely to receive COVID-19 aid from Moscow. 

 

To create an interaction term for testing Hypothesis 2 , which expects Russia to provide more 

assistance to autocratic counterparts severely affected by the COVID -19 pandemic, this study 

created two binary variables: one measuring whether or not the country’s COVID -19 

mortality rate fell below or above the median and another one measuring whether the country 

was a full liberal democracy (scoring 0.6 or above on a scale from 0 to 10). Countries with 

higher (above median) mortality rates due to COVID-19 and full liberal democracies were 

coded as “1” and “0” otherwise. An interaction terms of the two binary variables was used in 

the equation.  

 

The political affinity of a country with Russia (i.e., the extent to which the country supports 

Russia’s foreign policy) can be proxied by their voting alignment in the UN General 

Assembly. The study used the roll-call votes for the UN General Assembly Resolution 74/17 

on the militarization of Crimea, adopted on December 9, 2019  (UN General Assembly 2019). 

 
5 To illustrate this problem, the Central Asian republics of Kazakhstan and Kyrgyzstan used to exclude atypically 

high death numbers due to pneumonia from the COVID-19 death counts at the beginning of the pandemic. Both 

governments changed their methodology in summer 2020 that resulted in a spike of deaths reported due to COVID-

19. 



12                     Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 
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 12 

The resolution condemned Russia’s temporary occupation of the Crimean Peninsula . All votes 

in favour of these resolutions were coded as "1"; all other votes were coded as “2 .”6 

 

Membership in an alliance (including nonaggression pacts or a treaty containing a stipulation 

of nonaggression) was measured as a binary variable with "1" de noting membership in an 

alliance with Russia and "0" otherwise (Leeds, Ritter, and McLaughlin 2002). To separate 

membership in an alliance with Russia from the proximity variable, another binary variable 

denoting whether a country shared a land border with Russia was added for the robustness 

checks to select models. This study also included a binary variable denoting whether a country 

was a recipient of arms sales from Russia since 2014 ("yes" = 1 and "no" = 0). The source of 

the data was the Stockholm International Peace Research Institute  (SIPRI) Arms Transfers 

Database (SIPRI 2020). Lastly, the total population at the end of 2019 (logged) is also 

included among the control variables (UN Population Division 2019).  

 

The study estimated Russia’s decisions about COVID-19 aid allocations using Heckman’s (1979) 

two-step estimator, which allows for the correlation of the error terms of two decisions at 

hand. The sample includes all states in the year 2020 (but excludes unrecognized territories —

Abkhazia, Transdniestria, South Ossetia, Palestine—to which Moscow also provided 

assistance). Russia’s aid to the Kremlin-controlled territories in eastern Ukraine and a highly-

contested transfer of medical supplies and equipment to the US were excluded from the study. 

The Heckman model generates a Wald test statistic. When significant, it indicates that the two 

models are not independent and must be solved together. In this study’s case, the Wald test 

statistic was significant in all but the Base Model (1) with and without the binary variable 

denoting shared borders with Russia (see Table 1) suggesting that a null hypothesis of the two 

models’ independence could be rejected. The Base Model (1) was further estimated using 

logit and regression analysis separately for the selection and outcome stages, with result s 

being largely similar to that estimated by the Heckman model (see Appendix I Table 2). 

 

Table 1 contains the results of the Heckman selection estimator. Model 1 is a base model that 

uses the mean values of Russia’s COVID-19 aid (in USD) and excludes the interaction term. 

Model 2 also uses mean values of Russia’s COVID-19 aid and includes an interaction of the 

severity of the COVID-19 crisis and the type of regime among its predictors. Models 3 and 4 

use the lowest and highest estimated values of Russia’s COVID-19 aid, respectively. Since 

the distribution of the COVID-19 aid in dollar values was skewed, a logarithmic 

transformation of the variable was used in the outcome part of the equation. Table 5 

containing a variance inflation factor (VIF) appears in the Appendix. 

 

In the selection stage, the direction of the coefficients on all independent variables is consistent 

with the study’s expectations but only “UN Resolution A74/17,” “Membership in alliance,” and 

“Liberal democracy index” reached a level of statistical significance at p < 0.01 or above. 

 
6 As a robustness check, this study substituted roll-call votes for the UN General Assembly Resolution 54/17 with 

roll-call votes for the UN General Assembly Resolution 68/262 adopted on March 27, 2014 (UN General 

Assembly 2014) in response to the Russian annexation of Crimea. It was a non -binding resolution that called 

on states not to recognize changes in the status of the Crimea region, affirmed the territorial integrity of 

Ukraine, and invalidated the 2014 Crimean referendum . The findings of the models with the alternative 

measure of “political affinity” were similar to th ose reported in Table 1 and, therefore, are not included here.  



13                     Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 
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 13 

Table 1: The Heckman Model of the Determinants of Russian COVID-19 Aid 

 

 

Selection Stage  

 

 

(1)  

COVID-19 

AID Mean 

Values 

 

 

(2) 

COVID-19 

AID Mean 

Values 

 

 

(3) 

COVID-19 

AID Minimal 

Values 

 

 

(4) 

COVID-19 AID 

Maximum 

Values 

COVID-19 Deaths 

Rate 

-0.0212 

(0.043) 

0.014 

(0.052) 

0.014 

(0.052) 

0.0143 

(0.052) 

Covid deaths/liberal 

democracy 

interaction 

 0 1     1.24*      

(0.68) 

 1 0    -0.249 

(0.381) 

1 1     0.087 

(0.859) 

0 1     1.24*      

(0.683) 

 1 0   -0.249 

(0.381) 

1 1     0.087 

(0.920) 

0 1     1.24*      

(0.683) 

 1 0   -0.249 

(0.381) 

1 1    0.087 

(0.859) 

Distance to capital -0.000026 

(0.00048) 

-0.00002 

(0.00005) 

-0.00002 

(0.00005) 

-0.0002 

(0.0005) 

UN Resolution 

A74/17 

1.076** 

(0.484) 

1.24** 

(0.506) 

1.105** 

(0.505) 

1.105** 

(0.505) 

Liberal democracy 

index 

-0.945 

(0.736) 

-2.112** 

(1.000) 

-2.12* 

(1.09) 

-2.119* 

(1.09) 

Membership in 

alliance 

1.129*** 

(0.387) 

1.197*** 

(0.414) 

1.197*** 

(0.414) 

1.197*** 

(0.414) 

Total trade in 2019 

(logged)) 

-0.003 

(0.022) 

-0.009 

(0.096) 

-0.010 

(0.0234) 

-0.0096 

(0.023) 

Total population 

(logged) 

-0.100 

(0.092) 

-0.067 

(0.096) 

-0.068 

(0.096) 

-0.067 

(0.096) 

Arms Sales 

 

0.192 

(0.315 

-0.039 

(1.569) 

0.064 

0.325 

0.065 

(0.325) 

Constant  0.204 

(1.523) 

-4.008* 

(2.40) 

-0.039 

(1.569) 

-4.122 

(2.474) 

 

Outcome Stage 

 

 

(1) 

 

(2) 

 

(3)  

 

(4) 

COVID-19 Deaths 

Rate 

0.699* 

(0.360) 

0.708** 

(0.277) 

0.773*** 

(0.268) 

0.643** 

(0.285) 

Distance to capital -0.0001 

(0,0003) 

-0.0002 

(0.0002) 

-0.0002 

(0.0002) 

-0.0002 

(0.0003) 

Liberal democracy 

index 

4.021 

(6.057) 

2.347 

(4.153) 

2.193 

(4.034) 

2.692 

(4.271) 

Membership in 

alliance 

-2.016 

(3.227) 

-1.646 

(4.15) 

-1.581 

(2.124) 

-1.707 

(2.248) 

Total trade in 2019 

(logged) 

0.284 

(0.199) 

0.252* 

(0.142) 

0.318** 

(0.138) 

0.186 

(0.146) 

 



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 14 

Table 1: Cont’d 

 

GDP per Capita in 

2019 (logged) 

1.828* 

(0.968) 

1.507** 

(0.678) 

1.62** 

(0.658) 

1.393** 

(0.697) 

Total population 

(logged) 

-0.136 

(0.636) 

-0.206 

(0.480) 

-0.364 

(0.467) 

-0.048 

(0.494) 

Arms Sales 

 

0.559 

(2.09) 

1.21 

(1.48) 

1.515 

(1.150) 

0.574 

(1.522) 

Constant 3.255 

(11.438) 

5.928 

(8.275) 

5.369 

(8.039) 

6.487 

(8.512) 

 N=153 

Selected=34 

Wald chi2(8) 

= 14.32 

Prob>chi = 

0.074 

N=153 

Selected=34 

Wald chi2(8) 

= 24.6 

Prob>chi2 = 

0.002 

N=153 

Selected=34 

Wald chi2(8) = 

28.96 

Prob>chi2 = 

0.000 

N=153 

Selected=34 

Wald chi2(8) = 

21.46 

Prob>chi2 = 

0.000 

 

To interpret the coefficients, marginal effects were calculated. The probability of being selected 

as recipients of Russia’s COVID-19 aid increases by nearly 20 percentage points across all models 

for those countries that abstained from voting or voted against the UN General Assembly 

Resolution A74/17 (compared to those who voted in support of the Resolution). In the Soviet 

tradition of “aid for votes,” Russia used aid to reward countries that expressed support to its foreign 

policy priorities by siding with Moscow on the UN’s roll call votes. 

  

Moscow also rewarded the members of alliances with aid. The likelihood for a country to be 

chosen as a recipient of Russia’s COVID-19 assistance increased by 18.4 percent if it was a 

member of an alliance with Russia. Importantly, the impact of alliances held even when controlled 

for the shared borders with Russia (see Models 3 and 4, Appendix I Table 2). Many members of 

alliances with Russia are also Moscow’s neighbours; however, there are several Russian 

neighbours—Ukraine, Georgia, Estonia, Latvia, Lithuania, Finland, Norway, and Poland, among 

others—that are not members of alliances with Russia. The border variable turned insignificant in 

the robustness checks while the alliances’ measures retained their significance, suggesting that 

autocratic donors are more likely to use humanitarian aid allocations to countries who are members 

of alliances with the donor states. Lastly, democracies in the sample were less likely to be selected 

as recipients of Russia’s COVID-19 assistance. The marginal effect for a full liberal democracy of 

being selected as a recipient of Russia’s COVID-19 aid is 19.6 percent less compared to non-

democracies. 

  

The interaction term did not return the results consistent with the expectation that autocratic states 

experiencing a severe health crisis due to the COVID-19 pandemic were more likely to be selected 

as recipients of Russia’s COVID-19 assistance. Interestingly, it is the democracies with lower than 

median mortality rates due to COVID-19 were more likely to be selected to be the recipients of 

Russian COVID-19 aid. While this finding departs from the theoretical expectations, it comports 

with the available empirical evidence of Moscow sending large amounts of medical supplies and 

PPE to Italy, Serbia, Bosnia and Herzegovina, and other democratic countries with the purported 



15                     Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 
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 15 

goal of breaking the European solidarity and changing the “hearts and minds” of the people in 

these countries to view Russia in a more favourable light. In the end, the regime variable returned 

a statistically significant coefficient in the expected direction, independent of the interaction term; 

everything else being equal, Moscow has consistently preferred other autocracies as the recipients 

of its COVID-19 assistance.  

 

The second “outcome” model corresponding to the second stage of the decision-making process 

of aid allocation returned statistically significant coefficients on two variables – the mortality rate 

due to COVID-19 and the GDP per capita. This implies that in Russia’s case, the selection or the 

gate-keeping stage of decision-making involved many more considerations concerning the choice 

of the recipients of COVID-19 assistance from Russia. When it comes to making decisions about 

the levels of assistance, countries experiencing more severe health crises, as measured by COVID-

19 mortality rates, received higher levels of assistance from Russia. While somewhat unexpected, 

the relationship was born out in practice in that Russia's assistance appeared to be tracing the 

spread of the infectious disease around the globe. In March 2020, the epicentre of the COVID -

19 outbreak shifted to Europe, with Italy experiencing higher daily death tolls than Chin a, 

and Italy received some of the highest amounts of COVID-19 aid (in USD) from the Kremlin. 

In late spring/early summer of 2020, Central Asian republics experienced the peak of the first 

wave of the pandemic. The first wave of the COVID-19 pandemic was milder in Africa than 

in the rest of the world, but the second wave that came by the end of 2020 was more 

aggressive. Russia’s assistance roughly followed the same pattern with the first aid packages 

going to Europe, then to Central Asian and African states.  

 

Countries with higher levels of GDP per capita also receive d higher amounts of COVID-19 

aid from Russia. This outcome might be accounted for by the pragmatic considerations over 

the logistics of accepting and distributing the in-kind assistance packages (better-off countries 

have more robust infrastructure for accepting and distributing aid deliveries). In addition, high 

levels of GDP per capita are also suggestive of stronger institutional and health infrastructure, 

which means that even the modest in-kind contributions of PPE and equipment may have a 

higher rate of return, which can be claimed by Russia as outcomes of its assistance. Russia’s 

total volume of aid was not conditional on the size of the countries’ population, regime, 

membership in alliance with Moscow, or proximity to Russia.  

 

Statistical findings offer several plausible explanations for Russia's COVID-19 assistance. First, 

the results attest to the importance of separating the decision-making process regarding aid into 

two stages: a “selection” stage of making a decision on the recipients of aid and an “outcome” 

stage where decisions about the amount of aid are made. Different sets of determinants appear to 

be at play at different stages of decision-making about COVID-19 assistance. In the “selection” 

stage when an authoritarian donor, such as Russia, makes a decision about which countries deserve 

its assistance, several factors consistent with the theoretical expectations turned out to be 

significant in shaping Russia’s decisions. These include political affinity (as measured by 

countries’ votes in the UN General Assembly), membership in alliances with Russia, and the 

nature of the political regime. Russia appears to reward countries supporting its foreign policies 

by resorting to the “checkbook" diplomacy of humanitarian assistance. Similarly, members of 

alliances are rewarded with aid, as are the fellow co-authoritarians, while countries with higher 

scores on the liberal democracy index are less likely to receive Russia’s aid. When it comes to the 



16                     Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 
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 16 

second stage of the decision-making process regarding the amount of total assistance allocated to 

the countries selected as the recipients of Russia’s COVID-19 aid, many of the determinants of aid 

from the selection model appear to be less relevant.   

 

 

Discussion and Conclusions 

 

In 2003, an informal donor forum and network of 17 donors endorsed the Principles of Good 

Humanitarian Donorship (2003). These principles include humanity (saving lives and alleviating 

suffering as a goal of humanitarian assistance), impartiality, neutrality, and independence. By 

adhering to these principles, donors foreswear using humanitarian aid for geopolitical and other 

non-humanitarian means. Like many other so-called “new” and “emerging” donors, Russia has 

not acceded to these principles, and its motives for humanitarian assistance have been called into 

question. By focusing on Russia’s decisions about COVID-19 assistance allocation, this article 

sought to contribute to the larger literature on foreign aid that deals with donors' determinants of 

aid flow. More directly relevant for this study is the literature on humanitarian aid suggesting that 

the level of humanitarian assistance is not just an expression of humanitarian concerns, but it is 

also influenced by donors’ domestic strategic considerations.  

 

The study theorized aid as a function of the donor government’s interest in political survival, which 

is more astute in authoritarian than democratic states. The key differences between democratic and 

autocratic regimes relevant for understanding their aid choices have to do with the nature of 

constraints placed on democratic versus authoritarian leaders. By destabilizing the affected 

countries, humanitarian disasters threaten autocratic states’ access to and their ability to distribute 

private gains and/or elevate the costs of maintaining the “winning coalition.” Autocratic states, 

therefore, are sensitive to the prospects of the disaster-induced instability and are more likely to 

assist countries that are vulnerable to political instability.  

 

Consistent with these expectations, the study found that Russia was more likely to allocate aid to 

countries that were non-democratic, supportive of Russia’s foreign policy orientation, and 

belonged to alliances with Moscow. Russia’s international response to the COVID-19 crisis is 

telling of its foreign policy priorities. Moscow has taken advantage of the humanitarian cause to 

advance its military cooperation, weaken the prospects for democracy around the world, and gain 

geopolitical approval.  

 

Moscow might have scored some points by presenting itself as a responsible global power 

delivering the much-needed medical supplies at a time of US retrenchment, its political gamble 

under the guise of humanitarian COVID-19 assistance was cut short by the pandemic troubles at 

home and the limitations of Russia’s own fiscal approach. The government of Vladimir Putin has 

been opposed to investments in public welfare in favour of a fiscal discipline to eliminate 

Moscow’s public debt. Russia’s unfinished healthcare reforms increased the public’s vulnerability 

to contracting COVID-19, which spiked during the summer of 2020 (Cook and Twigg 2020). As 

the Russian military jets delivered PPE and medical supplies around the world, Russia turned out 

to be utterly unprepared to manage the crisis at home. The Kremlin’s cavalier approach to the 

pandemic, which involved downplaying the threat of COVID-19 infections in Russia, had 

backfired with the population growing highly skeptical of personal health and safety measures and 



17                     Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 
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 17 

becoming distrustful of vaccines (Stronski 2021). Russia’s strong financial reserves have been 

viewed as a symbol of its “sovereignty.” Subsequently, Russia’s domestic COVID-19 relief 

measures were less than 3 percent of its GDP, compared to extensive pandemic relief packages in 

some European countries that stood in double digits (Trudolyubov 2020). Only a sliver of this 

amount was put toward foreign COVID-19 assistance, which dwindled toward the end of 2020.  

 

This does not mean, however, that Russia’s humanitarian contributions are irrelevant. 

Humanitarian aid is not the only tool in the Russian foreign policy toolkit. When used together 

with other instruments of the so-called “smart power,” combining “soft” and “hard” power 

techniques for generating a desired foreign policy effect, humanitarian aid can be a force multiplier 

helping Moscow to achieve its foreign policy objectives. Whether in Central Asia, Africa, Latin 

America, or South East Asia, Russia’s COVID-19 aid has been used in conjunction with expanding 

military cooperation, including through the use of Private Military Contractors (PMCs), deepening 

economic engagement (mostly through Moscow’s contracts in extractive industries), and 

informational influence. The efforts at cultivating and sustaining bi-lateral, mainly elite-to-elite, 

connections have been accompanied by an anti-Western narrative contrasting the principles of 

sovereignty and [regime] security with the value-based conditionality imposed by the West. 

 

The so-called “traditional donors” are not beyond the pale in deviating from the need-based 

principles of humanitarian aid allocations. Still, the ideas of impartiality, neutrality, and need have 

long been integral to the integrity of the humanitarian system and an important draw for private 

and public donations. Russia’s participation in humanitarian efforts for clear, pragmatic concerns 

and geopolitical motives reinforces the growing skepticism regarding the universality of 

international disaster relief aid that threatens to undermine global humanitarian efforts. The 

perceived politicization of aid and biases in states’ allocations of disaster relief assistance have 

been named among the chief reasons for the crisis in the international humanitarian system 

(Paulmann 2016).  

 

  



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 18 

Appendix I 

 

Table 2: OLS and Logit Models of the Determinants of Russian COVID-19 Aid 

 (1)  

Logistic 

(Selection) 

Model 

(2) 

OLS 

(Outcome) 

Model 

(3)  

Logistic 

(Selection) 

Model 

(4) 

OLS 

(Outcome) 

Model 

COVID-19 Deaths 

Rate 

-0.013 

(0.071) 

-0.083 

(0.126) 

-0.052 

(0.083) 

0.023 

(0.131) 

UN Resolution 

A74/17 

 

2.482** 

(1.106) 

3.046** 

(1.333) 

2.793** 

(1.321) 

1.126* 

(0.661) 

Distance to capital -0.0001 

(0.0001) 

-0.0002** 

(0.0001) 

-0.0002* 

(0.0001) 

-0.0002 

(0.0001) 

Liberal democracy 

index 

-2.988** 

(1.477) 

-1.853 

(2.562) 

-1.227 

(1.676) 

-0.199 

(2.501) 

 

Membership in 

alliance 

2.045*** 

(0.647) 

3.644*** 

(1.222) 

1.776*** 

(0.581) 

3.518*** 

(1.185) 

States bordering 

Russia 

   2.924 

(1.780) 

Total trade in 2019 

(logged) 

-0.0113 

(0.040) 

0.019 

(0.062) 

0.0111 

(0.041) 

0.019 

(0.065) 

GDP per Capita in 

2019 (logged) 

1.828* 

(0.968) 

-0.907** 

(0.355) 

-0.809*** 

(0.231) 

-1.004*** 

(0.365) 

Total population 

(logged) 

-0.078 

(0.153) 

-0.296 

(0.281) 

-0.167 

(0.174) 

-0.452 

(0.310) 

Arms Sales 

 

-0.692 

(0.632) 

1.559 

(0.974) 

0.411 

(0.586) 

1.728 

(1.252) 

Constant 0.40 

(2.57) 

15.98*** 

(5.718) 

7.357* 

(3.910) 

15.86*** 

(5.582) 

N 

(pseudo) R-squared 

153 

0.23 

153 

0.24 

153 

0.29 

153 

Wald chi2(8)/ F(8, 

145) 

Prob > chi2 / Prob 

> F 

16.55 

0.035 

4.57 

0.000 

21.25 

0.019 

4.66 

0.00 

Robust error terms in parentheses 
* p < 0.1, **p < 0.05, ***p < 0.01 

 

 

 

 

 

 

 



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 19 

Table 3: Russia’s COVID-19 Donations (2020) 

 
Country 

 

Minimum Estimate 

 

Maximum Estimate 

 

Afghanistan 65,629.8 757,500 

Algeria 771,600 14,600,000 

Angola 440,000 11,400,000 

Armenia 1,299,600 15,000,000 

Azerbaijan 1,457,112 16,800,000 

Belarus 2,234,041 22,100,000 

Bosnia and Herzegovina 3,086,400 58,300,000 

Cape Verde 440,000 11,400,000 

Central African Republic 4600 4600 

China 1,840,000 34,800,000 

Comoros 46 4600 

Congo 4600 4600 

Costa Rica 26000 300,000 

Democratic Republic of the 

Congo 103,920 125,440 

Djibouti 778,378 1,020,270 

Ethiopia 4600 46,000 

Guinea 154,200 606,400 

Iran 649,800 7,500,000 

Italy 33,500,000 380,000,000 

Kazakhstan 1,299,600 15,000,000 

Kyrgyzstan 2,737,900 3,953,000 

Moldova 1,494,540 17,300,000 

Mongolia 1,299,600 15,000,000 

Nicaragua 11569 528,630 

North Korea 84,474 975,000 

Serbia 8,526,570 160,000,000 

Sierra Leone 130,000 1,500,000 

Somalia 46 4600 

South Africa 1299.6 15,000 

Syria 172,124.5 1,595,445 

Tajikistan 1,299,600 15,000,000 

Ukraine 80,000 1,512,000 

Uzbekistan 1,299,600 15,000,000 

Venezuela 1,189,880 19,600,000 

Zambia 65,000 750000 

Zimbabwe 4600 46,000 

 

 

 



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 20 

Table 4: Examples of the PPE and Market Prices (in US$) 
PPE Price per 

item (low) 

Price per 

item (high) 

Price per 

box 

Amount in 

the box 

Weight per 

item 

3ply face 

mask 

0.2 0.6 30 50 0.011 

KN95 mask 1.89 2.6 18.9 50 0.005 

Nitrile 

gloves 

0.2907 3.5 29.07 100 0.012 

Protective 

coveralls 

20.99 45   0.124 

Rapid test 

kit 

12.996 150 324.9 25  

Test reagent 66 370    

Face shield 4.9 5.1   0.078 

 

 

 

Table 5: Variance Inflation Factor 

 

Variable      VIF        1/VIF   

 

Liberal democracy index 2.55      0.392290 

UNRES74_19        2.48     0.402521 

GDP per capita (logged)    1.85     0.539595 

Arms sales from Russia 1.48      0.677441 

Distance to capital       1.35      0.738348 

Population (logged)      1.33      0.749909 

Membership in alliances       1.26      0.792916 

COVID Deaths rate      1.2     0.831789 

Trade (logged)   1.18  0.845555 

 

 

Mean VIF         1.66 

  



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