1 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 1 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. 2 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 2 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). 3 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 3 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 4 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 4 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). 5 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 5 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 6 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 6 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). 7 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 7 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 8 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 8 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 9 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 9 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 10 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 10 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. 11 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 11 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 ISSN 2562-8429 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 ISSN 2562-8429 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) 14 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 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 ISSN 2562-8429 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 ISSN 2562-8429 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 ISSN 2562-8429 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). 18 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 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 19 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 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 20 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 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 21 Canadian Journal of European and Russian Studies, 16(1) 2023: 1-28 ISSN 2562-8429 21 REFERENCES Anbarci, Nejat, Monica Escaleras, and Charles A. 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