Location: Global
The Case for Seizing Russian State Assets
This brief examines the legal and economic arguments in the ongoing debate over whether to confiscate Russian state assets frozen in Western democracies and redirect them toward supporting Ukraine’s resilience and reconstruction. It also outlines concrete proposals for how such a measure could be undertaken in compliance with international law and with manageable economic consequences.
At the outset of Russia’s full-scale invasion of Ukraine, substantial Russian state assets held in Western countries were frozen. While not all countries have disclosed precise figures, estimates place the total between $290–330 billion, most of it held within European jurisdictions. These numbers can be put in perspective to the total global support to Ukraine so far, €267 billion according to the Kiel Institute’s Ukraine Support Tracker. A lively discussion has emerged around the legal, economic, and political feasibility of seizing these assets to support Ukraine. As evident, this would constitute a very substantial addition to the support for the country. Thus far, agreement has only been reached on utilizing the returns on the assets to service a $50 billion loan to Ukraine under the Extraordinary Revenue Acceleration (ERA) mechanism. It has been argued that $50 billion should be enough, but Western contributions to the defence of Ukraine have been around €80 billion per year. The ERA is thus only a partial and very short-term financial solution for Ukraine, while a €300 billion fund based on the seizure of the assets would last perhaps 3-5 years. In short, the size of the fund matter and the principal amount is significantly larger than the fund that has been set up based solely on taxing the returns of the frozen assets.
This brief survey’s the main areas of contention and proposes viable pathways forward. It focuses on the legal and economic dimensions, setting aside moral arguments—which are broadly accepted given Russia’s unprovoked aggression and the destruction it has caused. Ultimately, the question is a political one: whether the legal justification and economic trade-offs favour asset seizure over other financing methods.
The Legal Arguments
Opposition to seizure often cites the principle of sovereign immunity. Yet, international law permits exceptions through countermeasures—acts that would otherwise be unlawful but are allowed in response to grave violations by another state. Additionally, asset confiscation may be lawful when enforcing international judgments (other possible legal avenues are for instance explored in Webb (2024), though in the end deemed as less likely to gain traction and legal approval). In both cases, the goal is to induce compliance with international obligations and secure reparations. A further legal basis lies in the doctrine of collective self-defense, which permits states not directly attacked to aid those that are, in response to unlawful aggression (Vlasyuk, 2024).
Critics often note that countermeasures should be temporary and reversible. However, as Vlasyuk (2024) points out, international law qualifies reversibility as being required only “as far as possible.” This implies that in cases of severe violations—where reversible countermeasures have failed—non-reversible actions may be justified. One proposed mechanism ties the frozen assets to future war reparations, allowing permanent transfers only if Russia refuses to comply with a future reparations ruling. Since reparation should go to the victim of Russia’s aggression, it also means that it is Ukraine that has the ultimate claim on the frozen Russian assets. This implies that any decision of confiscation and governance structure for transferring funds to Ukraine should be made with the consent of Ukraine. Put differently; even if the money is in Western financial institutions, there are good reasons to make sure the resources are used according to Ukrainian preferences.
The Economic Arguments
The principal economic concerns surrounding asset seizure are its potential impact on confidence in European capital markets, including risks of capital flight, increased interest rates, and diminished credibility of the euro. There are also fears of reciprocal actions by Russia against remaining Western investments.
These concerns, however, are increasingly overstated. The major shock to financial markets occurred when the assets were first frozen; any anticipated impact should now be fully priced in. Moreover, a viable reserve currency must be supported by convertibility, sound economic governance, and rule of law—features absent in countries like China, Gulf states, or most other emerging economies. The yen and Swiss franc lack either scale or stability. Despite previous sanctions and the 2022 asset freeze, the dollar and euro still account for around 80 percent of global foreign exchange reserves (The International Working Group on Russian Sanctions, 2023). Given the current crisis of confidence in U.S. fiscal governance, the euro remains especially robust.
The extraordinary nature of the situation also diminishes fears of setting a destabilizing precedent. Investors alarmed by this measure may not be long-term assets to Western markets but rather criminal states or individuals that should not be protected by the West’s financial and legal systems. More broadly, it signals to authoritarian regimes that aggressive actions will carry financial consequences. Western firms still operating in Russia have had ample time to disinvest, and those that remain should not constrain public policy.
Importantly, the costs of inaction must be considered. Financing Ukraine through increased public borrowing could raise interest rates across the eurozone and widen yield spreads between fiscally stronger and weaker member states. Seizing Russian assets, by contrast, may be economically safer, more equitable, and legally sound (International Working Group on Russian Sanctions, 2023).
Suggested Approaches
Several proposals aim to facilitate asset transfer in ways consistent with international law and economic stability.
Zelikow (2025) proposes the establishment of a trust fund to lawfully assume custody of frozen assets. This fund—grounded in the legal doctrine of countermeasures—would not represent outright confiscation but a conditional hold. Assets would remain Russia’s property until disbursed to victims of its aggression. A board of trustees would oversee disbursements—for example, servicing ERA loans or financing reconstruction. In this proposal, the fund would broadly define “victims” to include Ukraine and neighbouring states that have borne costs, such as accommodating refugees. This can perhaps help build political support among Western countries for the trust fund, but it has the obvious drawback that it may imply less support to Ukraine. Zelikow (2025) argues that institutions like the Bank of England or World Bank could manage the fund, given past experience with similar arrangements, potentially issuing bonds backed by the assets to accelerate support.
Vlasyuk (2024) proposes a multilateral treaty among coalition states recognizing Russia’s grave breaches of international law. This would provide a unified legal basis for transferring central bank assets to Ukraine via a compensation fund. National legislation would follow—similar to the U.S. REPO Act—tailored narrowly to address such violations. These laws should include safeguards, such as provisions to suspend asset seizure if hostilities end and reparations are paid.
Dixon et al. (2024) propose a “reparation loan” backed by Ukraine’s reparations claims. The EU or G7 would lend to Ukraine, using these claims as collateral. If Russia fails to pay after a ruling by a UN-backed claims commission, the frozen assets could be seized. This approach aligns well with the requirement for reversibility in countermeasures and may also reassure financial markets.
Conclusions
In summary, compelling legal arguments support the transfer or confiscation of Russian state assets under international law. Meanwhile, fears of damaging economic consequences appear increasingly unfounded. Any meaningful support for Ukraine—whether through asset seizure or public borrowing—will carry financial implications. However, using Russian rather than Western taxpayer resources is both morally and politically compelling.
What is now needed is coordinated political will and a practical, legally sound mechanism to operationalize asset transfers. With sound governance, such a step would not only finance Ukraine’s recovery but reinforce the international legal order and deter future aggression. An arrangement that makes sure all resources go to Ukraine—and not toward covering losses incurred by supporting Western countries—should be prioritized.
References
- Dixon, H., Buchheit, L. C., & Singh, D. (2024). Ukrainian reparation loan: How it would work. The International Working Group on Russian Sanctions.
- The International Working Group on Russian Sanctions. (2023). Working Group paper #15. Stanford University.
- Vlasyuk, A. (2024). Legal report on confiscation of Russian state assets for the reconstruction of Ukraine. KSE Institute.
- Webb, P. (2024). Legal options for confiscation of Russian state assets to support the reconstruction of Ukraine. European Parliament.
- Zelikow, P. (2025). A fresh look at the Russian assets: A proposal for international resolution of sanctioned accounts (Hoover Institution Essay). Hoover Institution Press.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Russia’s Car Fleet Dynamics – and Why They Matter
Russia’s car imports have evolved dramatically since its full-scale invasion of Ukraine in February 2022. The invasion and subsequent sanctions have led to a shift away from mainly Western car imports to domestically produced cars, and especially Chinese cars, both entailing quality concerns. Despite state sponsored loans reliefs, the heightened inflation pressures in Russia and increased financial burden on households is catching up to the car market – in the first quarter of 2025, the sales of new cars decreased by 25 percent compared to 2024. This policy brief uses the developments in the Russian primary car market as a lens to examine the spending power of Russian households and highlight the limitations of state interventions under sanctions and inflationary pressure.
From Western Dominance to Domestic Car Sales
Prior to February 2022, imports of American, European, South Korean and Japanese (hereafter called western) cars stood for about 60 percent of all new car sales in Russia. Domestic production took up most of the remaining 40 percent market share (SITE, 2024). In 2023, the number of western car sales was almost zero as most of these automotive firms exited the Russian market following the country’s war on Ukraine. Collaborations between European and Russian automotive companies, such as between Renault and Autovaz, as well as production of western cars in Russia, were also largely abolished. The mass exodus severely impacted the production levels in the Russian automotive industry; in 2021 around 1 350 000 cars were produced in Russia, dropping to around 450 000 in 2022, and increasing to only about 750 000 cars in 2024. However, the sales of new Russian cars fell in the immediate months following the invasion and subsequent sanctions but managed to bounce back to initial levels in 2023 (Figure 1).
Figure 1. New car sales in Russia
Source: Association of European Businesses. Note: Detailed data for 2024 and 2025 is unavailable.
The Chinese Import Surge
While the sale of Russian cars rebounded following the invasion, the key market player post-2022 is China. As illustrated in Figure 1, in 2023, the sales of newly produced Chinese cars in Russia were eight times the 2020 figures.
Although the imports of Chinese cars made up for a large part of the massive withdrawals of western cars post-invasion, new issues have arisen. Chinese cars are considered unfit for Russian weather conditions, and spare parts are also considered to be of low quality. Additionally, Chinese cars are reported to survive shorter total mileages (about half, compared to many western brands), and to have poor electronic and ergonomic systems. Still, prices for a Chinese car are generally higher than for a Russian car, mostly due to taxes and import tariffs. To dampen the recent Chinese expansion on the car market (in 2025 accounting for 63 percent of the market), Russia in March 2025, hiked the import tax on Chinese cars from nearly $6000 to $7500. Furthermore, the price of Chinese cars is expected to increase in 2025, following a depreciation of the ruble against the yuan.
High Prices, Large Loans
Not only Chinese cars have met criticism when it comes to quality and price. In summer 2022, Autovaz declared that the 22 model of the classic Lada Granta would be void of air bags, an ABS braking system and a brake assist system, due to a scarcity of imported components. A subset of the model has since been equipped with a driver-seat air bag. Despite such major shortcomings, prices for new Russian-made cars have increased by 67 percent since the onset of the war. These price increases are mirrored on the secondary market where the price for a used foreign car have increased by 60 percent since 2022.
Another feature of the Russia automotive market concerns the large increase in automobile loans granted to businesses and entrepreneurs over the last four years (Figure 2).
Figure 2. Volume of companies’ automobile loans
Source: Rosstat.
While the near doubling in the loan value for companies’ car loans seems large, its growth is small compared to that for individuals. Since the onset of the war, the volume of private car loans has grown more than fivefold. This increase is arguably spurred by the preferential loans scheme for the purchase of new cars, introduced mid-July 2022 and granted to Russians with at least one child under 18, new car owners, people employed within health and education, military personnel and their close relatives, and disabled people. The so-called loan (projected to be in place up until 2027) applies to car purchases in Russia of a maximum 2 million ruble and discounts the price by 20 percent (25 percent for cars sold in the Far East Region). Under this scheme, car loans constituted almost 6 percent of all consumer loans in mid-2024, a sixfold increase in just a year (see Figure 3). This trend has not waned off since 2024. In December 2023, 70 percent of all cars bought in Russia were financed by borrowed funds. The size of an average car loan also grew substantially, around 20 percent, between 2022 and 2023. At the same time, the share of risky borrowers increased. In October 2024, 60 percent of the borrowers had a Debt Service-To-Income (DSTI) Ratio of over 50 percent, indicating that a large segment of car buyers will potentially be unable to repay the debt (CBR, 2024).
Figure 3. Private Automobile Loans
Source: CBR (2024). Note: Figure based on approximation from CBR figure.
Household Strains and Financial Risks
Over the last five years gasoline prices have gone up by about 17 percent (standard petrol), alongside substantial price increases for nearly all major consumption goods in Russia – driven by the rampant inflation. In fact, the price of the Russian consumer basket nearly doubled between February 2022 and August 2024. Progressive income taxes have been introduced for about 3.2 percent of the working population – increasing taxation from 13 percent up to 22 percent. Furthermore, in July 2024, the subsidized mortgages for newly built apartments were scrapped such that all buyers now face a 16-20 percent rate (SITE, 2025). While real wages did increase by 8 to 9 percent in 2023 and 2024, real pensions did not. Furthermore, reported inflation figures are likely severely understated, with actual inflation being around 20, rather than the reported 9.5 percent. If so, the actual real wage growth would be about 0 percent (SITE, 2025).
This undermines the spending power of Russian households, which is now being reflected on the primary car market. There has been a sharp drop in car sales – 25 percent in the first quarter in 2025, and car prices are also on the decline. This, combined with the growing reliance on credit, signals that many consumers are no longer able to make large purchases despite the state driven support scheme – pointing to major affordability issues. Given that the preferential loans scheme will be in place only up until 2027 and that Chinese cars will likely become more expensive, demand may dwindle even further in the years to come. In such situation, the government could be forced to expand their preferential scheme to artificially keep up demand levels, taking on greater financial risks and associated costs. They may also increasingly close off the inflow of Chinese cars, which leave consumers with no options outside of domestically produced cars.
The falling demand for cars may also be considered an indicator of household’s beliefs about the economic conditions to come. That is, the demand for cars could be a signal of consumers understanding that the economy is, or will shortly be in a recession (Attanasio, Larkin, Ravn and Padula, 2022). While the Russian war time economy is not currently displaying recession signs, its persistent issues with rampant inflation, rapidly growing household mortgages and changes in the credit to GDP ratio signals its financial stability is at risk. As discussed in the report “Financing the Russian War Economy”, these are key indicators correlated with banking crises (SITE, 2025). If declining demand for cars is a sign of consumers perceiving the economy as increasingly fragile, this perception could amplify existing vulnerabilities.
Conclusion
The automotive sector offers comparatively timely data, making it a useful window for assessing the financial situation of Russian households. In the current automotive landscape in Russia, buying a new car is becoming increasingly expensive. This has forced not only private buyers but also businesses to increasingly take up loans to cover the payment of a new car – often despite reduced quality and limited choice. The demand for new cars is partly driven by state intervention, particularly the preferential loan scheme. This not only places a growing financial burden on the state but also carries rising risks of borrowers defaulting. At the same time, the current trends in the sector illustrate the growing limitations of both import substitution and state-backed credit schemes as tools for maintaining consumer demand. The recent drop in new car sales, despite state support, may reflect a growing reluctance among households to make large purchases, exposing how Russian households’ purchasing power are eroding in the Russian wartime economy. Importantly, this drop may point not only to affordability issues but also to a broader perception that the financial system is increasingly unstable.
Overall, the dynamics in the automotive sector suggest that the Russian economy is not doing as well as officially claimed, adding support for the effectiveness of sanctions and company withdrawals from the Russian market.
References
- Attanasio, O., Larkin, K., Ravn, M. O., & Padula, M. (2022). “(S)Cars and the Great Recession”. Econometrica, 90(5), 2319–2356.
- The Central Bank of Russia (CBR). (2024). “Financial Stability Review No. 2. Q2-Q3, 2024”.
- Stockholm Institute of Transition Economics (SITE). (2024). “The Russian Economy in the Fog of War”.
- Stockholm Institute of Transition Economics (SITE). (2025). “Financing the Russian War Economy”.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Complementary but Different: The Politics of Green Industrial Policy and Carbon Pricing
Sweden, historically a global leader in carbon pricing, has recently made a significant shift in its climate policy towards green industrial policy. It has moved away from environmental taxation – reflected in reduced transport fuel tax rates and increased emissions from the transport sector – towards a state-driven energy policy centered on nuclear power. To support the planned construction of ten new nuclear reactors, the Swedish government has proposed loan guarantees and state loans of up to $40 billion (Persson, 2022). By lowering transport fuel tax rates while simultaneously offering state support for nuclear energy, Sweden is treating carbon pricing and green industrial policy as substitutes rather than complements. This policy brief challenges that approach, arguing that carbon pricing and green industrial policy should be seen as complementary climate policy instruments. However, their political economies differ significantly, making industrial policy more politically feasible. Yet, the two key challenges with green industrial policy are how to finance it and how to “pick winners” – choosing which technologies and companies to support. We use the recent bankruptcy of Swedish battery manufacturer Northvolt as a case study to illustrate these challenges.
Two Climate Policy Instruments
Over the past decade, climate policymaking has undergone a significant shift. Governments in Europe, USA, and China have increasingly adopted green industrial policy as a central strategy to reduce carbon emissions, moving beyond the traditional focus on carbon pricing.
Green industrial policy serves both environmental and economic purposes. It mobilizes government efforts toward decarbonization while fostering the development of zero-carbon technologies and domestic firms to drive employment, innovation, and growth in green sectors (Rodrik, 2014). Common policy tools include subsidies, loan guarantees and state loans. Sweden’s emphasis on loan guarantees and state loans to support nuclear energy is not unusual, as these are among the most widely used industrial policy instruments in rich countries (Juhasz, Lane, and Rodrik, 2023). A historical parallel can be found in France, which relied on state loans and loan guarantees in the 1970s and 1980s to support its nuclear energy expansion (Andersson and Finnegan, 2024).
Carbon pricing, on the other hand, focuses solely on emissions by putting a price on the negative externality of carbon emissions. By equalizing the private and social cost of releasing carbon, carbon pricing leaves it to the market – firms and households – to decide how to most effectively reduce emissions. While carbon pricing is a form of government intervention to correct a market failure, it is technology neutral, and the state is not actively steering the economy toward specific pathways of decarbonization in the same way that green industrial policy does.
Complements Rather Than Substitutes
At its core, decarbonization requires shifting the cost advantage from fossil fuels to the zero-carbon alternatives of wind, solar, and nuclear. The key factor is the relative price between the two energy categories. This shift can be achieved by increasing the cost of fossil fuels through carbon pricing or by lowering the cost of zero-carbon energy via industrial policy.
The cost of energy production – particularly electricity – can be divided into three main components: capital investment costs, operation and maintenance, and fuel costs (IEA, 2005; 2020). While operation and maintenance costs are typically a minor share of the (levelized) cost of electricity for all energy sources, there are large differences in investment and fuel costs between fossil fuels and zero-carbon alternatives. The cost of fossil fuels is sensitive to fuel prices, which often account for more than half (coal), to around 80 percent (natural gas), of total costs in most regions. Wind and solar, on the other hand, have zero fuel costs but are highly dependent on capital investment costs, making them particularly sensitive to interest rates. Nuclear energy, though requiring some fuel costs, is also predominantly capital-intensive (IEA, 2015).
Because of these differences in cost structure between the energy categories, carbon pricing and industrial policy work as complements rather than substitutes. Carbon pricing raises the variable fuel costs of fossil fuel-based energy, making it less competitive, while industrial policy can reduce the fixed capital costs of zero-carbon technologies, improving their affordability. A well-balanced climate strategy may employ both approaches to achieve decarbonization. A revenue-neutral model could even use carbon pricing revenues to fund industrial policy, balancing cost burdens and investment incentives.
Figure 1 illustrates how the two policy instruments of carbon pricing and industrial policy are complements when it comes to climate policy as they both shift the relative price in favor of zero-carbon energy sources.
Figure 1. Decarbonization and relative prices
Source: Authors’ illustration.
Differences in Their Political Economy
Despite their complementarity, the political economy of carbon pricing and green industrial policy differs significantly, making the latter more politically feasible.
First, carbon pricing and green industrial policy differ in how they distribute costs and benefits across time and geography. Carbon taxes impose short-term, localized, and visible costs on consumers and producers while generating long-term, globally dispersed benefits. Because the costs and benefits are unevenly distributed over time and space, the households that bear the costs are likely not the same as those that receive the benefits. In contrast, green industrial policy can create immediate and visible local benefits for households and businesses, while spreading the costs more broadly. These costs can be distributed nationally using general taxation, internationally through global climate funds, or shifted into the future via deficit spending.
Second, carbon pricing generates a first-mover disadvantage, as the implementing country will incur higher energy prices for producers and consumers and thus potential deindustrialization and unemployment as firms relocate to countries with less stringent climate policy and lower energy costs. Green industrial policy inverts this narrative by incentivizing low-carbon firms to relocate to countries offering substantial state support. As a result, the first country that adopts generous green subsidies will put political pressure on other countries to do the same for fear of job loss and diminished competitiveness. This dynamic has been in play over the last couple of years with European leaders fearing the impact on European competitiveness of the Inflation Reduction Act in the U.S. – the largest climate bill ever implemented in that country – and green industrial policy in China. In this sense, subsidies offer a first-mover advantage, encouraging early adoption.
Combined, these two important political economy factors make green industrial policy more politically feasible by increasing public and political support compared to carbon pricing.
The first-mover advantage of green industrial policy also has important implications for global climate policy. The advantage, coupled with increasing opportunity costs of non-adoption (loss of competitiveness), can result in an equilibrium where the largest economies, such as the U.S., China, and the EU, all adopt similar green industrial policies. The first country that adopts green industrial policy pressures other nations to follow suit, fearing job losses, diminished competitiveness, and market-share erosion, creating a domino effect that results in a global implicit carbon price. This outcome is an equilibrium since none of the “players”, observing the choices made by others, have an incentive to withdraw their state support and subsidies for the green sector.
In contrast, a globally imposed carbon price using taxes, such as through international agreements like the Paris Agreement, does not constitute an equilibrium. Countries under such an agreement continuously face incentives to defect by repealing their carbon taxes to gain competitive advantages and free-ride on the ambitions of others. To transform such an agreement into a stable equilibrium, there must be credible punishment mechanisms – such as border carbon adjustments that penalize imports from defecting countries – to reduce incentives for free-riding (Nordhaus, 2015). Yet, such a global agreement with credible punishments has remained elusive, reflecting the complexities of international cooperation.
Two Key Challenges
While politically more feasible compared to carbon pricing, governments face two key challenges with industrial policy: how to finance it and how to select the right technologies and companies to support. These challenges are not just theoretical – they have real-world consequences. The recent failure of the Swedish battery manufacturer Northvolt highlights the potential risks governments face when using industrial policy.
Founded in 2015, Northvolt aimed to supply batteries for electric vehicles and energy storage, positioning itself as Europe’s main competitor to dominant Chinese manufacturers. With a rapid expansion of factories, the company struggled with production delays, mounting losses, and an inability to secure additional capital investments, ultimately leading to its bankruptcy. The Swedish government has provided some economic support but was unwilling to match the kind of large-scale state subsidies that China provides to its battery industry (Ekström och Mikaelsson, 2024). Likely, the level of financial support required for Northvolt to compete globally would need to come from the EU level, rather than national funding alone (Milne et al., 2025). The Northvolt case emphasizes one of the main challenges for green industrial policy: financing. Unlike carbon pricing, which generates revenue, industrial policy requires substantial government funding. High fiscal costs may limit its feasibility outside of large economies like China, the U.S., and the EU.
Furthermore, even if Sweden had provided stronger financial support – similar to its proposed subsidies for nuclear energy – Northvolt may still have failed due to technological competition. Experts suggest that Chinese competitors will be reluctant to acquire Northvolt’s Swedish factory, as Chinese investors believe it’s not correctly constructed for battery manufacturing (Nordensson, 2025). This underscores the second risk of industrial policy: governments may invest in technologies and companies that ultimately fail to compete.
Conclusion
Carbon pricing and green industrial policy are complementary tools for climate mitigation, but their distinct political economies make industrial policy more politically feasible. However, with green industrial policy, governments are faced with the risks of “picking winners” and how to finance the policy.
Sweden faces these two risks with its nuclear energy strategy. For instance, the levelized cost of nuclear energy has risen over time (Bilicic and Scroggins, 2023). Today, nuclear is the most expensive source for new grid capacity, while wind and solar are the cheapest. By 2045, when Sweden’s planned ten new reactors are expected to be operational, renewables may be so cheap that nuclear power struggles to compete, leading to financial losses and high electricity prices (SVT, 2024). In this sense, Sweden’s focus on a single zero-carbon technology may turn out to be a costly mistake.
Sweden should use green industrial policy as a complement to, rather than a substitute for, its previous carbon pricing strategy. Furthermore, to reduce the risks of not picking the “winners,” it should diversify its support across multiple zero-carbon technologies – including electric vehicles, battery manufacturing, solar, and wind – rather than focusing narrowly on nuclear power.
References
- Andersson, J. and Finnegan, J. (2024). Industrial Policy and Decarbonization: The Case of Nuclear Energy in France. Working Paper.
- Bilicic, G. and Scroggins, S. (2023). 2023 Levelized Cost of Energy+. Lazard.
- Ekström, J., and Mikaelsson, C. (2024). Därför nobbar regeringen Northvolt. Svenska Dagbladet. October 6, 2024.
- IEA. (2005). Projected Costs of Generating Electricity: 2005 Edition. International Energy Agency. Paris.
- IEA. (2015). Projected Costs of Generating Electricity: 2015 Edition. International Energy Agency. Paris.
- IEA. (2020). Projected Costs of Generating Electricity: 2020 Edition. International Energy Agency. Paris.
- Juhazc, R., Lane, N., and Rodrik, D. (2023) The New Economics of Industrial Policy. Working Paper 31538, National Bureau of Economic Research.
- Milne, R., Johnston, I. and Bounds, A. (2025). Boss of bankrupt Northvolt urges Europe to invest in homegrown battery sector. Financial Times. March 13, 2025.
- Nordensson, B. (2025). Expert: Inga utsikter driva Northvolt vidare. Svenska Dagbladet. March 12, 2025.
- Nordhaus, W. (2015). Climate Clubs: Overcoming Free-riding in International Climate Policy. American Economic Review, 105(4), 1339–1370
- Persson, I. (2022). Allt du behöver veta om ’Tidöavtalet. SVT Nyheter. 14 October, 2022.
- Rodrik, D. (2014). Green Industrial Policy. Oxford review of economic policy 30 (3):469-491.
- SVT Nyheter. (2024). Kärnkraften kan bli nära dubbelt så dyr som regeringen trott. SVT Nyheter. January 25, 2024.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Should the $60 Price Cap on Russian Oil Exports be Lowered?
Western governments have imposed a $60 price cap on Russian seaborne oil exports using Western services. To evade the policy, Russia has developed a “shadow fleet” which uses no such services. In this policy brief, we claim that the resulting segmentation of Russian oil exports dramatically modifies the conventional analysis of a price cap. Our research shows that lowering the cap would not hurt Russia as intended unless a robust expansion in non-Russian oil supply was to limit the induced increase in the world oil price. If this price increase is not limited, lowering the cap could even moderately increase Russian profits because shadow fleet sales would be more profitable. By contrast, policies that reduce some shadow fleet capacity would reduce Russian profits if undertaken while Russia still relies on some Western services.
In response to Russia’s invasion of Ukraine in February 2022, the EU, the U.S., and other G7 countries (hereafter the West) ceased their imports of Russian oil, leading Russia to export more to India, Turkey, and China instead. In addition, the West imposed sanctions on oil exports from Russia, whose profits are instrumental in supporting its war.
Since more than 80 percent of Russia’s seaborne oil exports relied on the provision of Western services (CREA, 2023) (financial, operational, and commercial) the EU suggested banning the use of these Western services for all Russian seaborne exports. However, governments feared that this would cause a spike in the world oil price. As an alternative, the U.S. suggested a price cap, which the West ultimately imposed in December 2022, limiting Russian revenues from oil shipped using Western services to $60 per barrel.
Oil transported without Western services is exempt from the cap. Therefore, Russia has gradually assembled a “shadow fleet” that uses non-Western services in order to sell oil at prices above the cap.
The price cap on Russian oil is a new, insofar untested economic sanction, currently a subject of active public discussion, with experts recommending potential adjustments and application to more countries, and policymakers currently considering to tighten the price cap – see for example the January 2025 call by Sweden, Denmark, Finland, Latvia, Lithuania and Estonia to lower the price cap below $60. The policy quickly piqued the interest of economists – see for example Spiro, Wachtmeister, and Gars’ (2024) comprehensive review of policy options to limit Russia’s ability to finance the war.
In their pioneering contribution to the literature, Johnson, Rachel, and Wolfram (2025) provide a rich analysis of the effects of the price cap, albeit under the assumption that the shadow fleet has a fixed capacity. In a recent working paper (Cardoso, Salant, and Daubanes, 2025), we present a new dynamic economic model that accounts for the expansion of the Russian shadow fleet. The model is calibrated to reproduce observed facts and used to simulate the effects of (1) various levels of the price cap, including the extreme case of a complete ban, (2) enforcement stringency, and (3) policies targeting the shadow fleet.
Perhaps surprisingly, our analysis shows that, in the absence of any increase in non-Russian oil supply, lowering the level of the price cap below $60 would benefit Russia. This includes lowering the cap to levels so low (below $34) that the policy amounts to a ban as Russia would prefer not to use Western services at all at these cap levels. More generally, the model reveals that a lower cap would have two opposite effects on Russia: On the one hand, it would reduce Russia’s profit (i.e., revenues net of production costs) from sales at the cap. On the other hand, since a lower cap would reduce Russia’s oil exports, it would increase the oil price and, therefore, Russia’s profit from sales through its shadow fleet. Our analysis yields a testable and intuitive condition under which the latter effect dominates the former, making a lower cap counterproductive. This condition depends on the shadow fleet capacity relative to Russian sales at the ceiling price.
Application of this condition shows that when sanctions were imposed, Russia’s shadow fleet capacity was already sufficiently high for Russia to benefit from a reduction in the price ceiling. Russia would even have benefited from a reduction in the cap if the West had prevented any expansion in Russia’s shadow fleet beyond its initial level. With no such limitation, Russia would continue to expand its fleet size regardless of the size of the cap reduction. This leads us to conclude that Russia would also benefit if an unanticipated reduction in the cap (or a complete ban) occurred subsequently.
It should be noted that in the absence of a non-Russian supply response, caps at different levels quantitatively impact Russian total profits in a similar way. For example, the $60 cap reduces Russian profits by about 25 percent compared to a scenario without sanctions, and a complete ban would have impacted Russia only slightly less.
The following figure shows a comparison of prices, shadow fleet capacity, and profits under a price cap sanction (solid lines), a service ban (dotted lines), and the absence of sanctions (grey dashed lines). The simulations assume no supply response from non-Russian producers (none occurred when the cap was first implemented). A lower cap cuts Russian exports and raises the global oil price, increasing Russian profits from its fleet sales. A non-Russian supply response would dampen this oil price spike and would, therefore, diminish the resulting revenue increase from Russian fleet sales.
Figure 1. Outcomes under different sanction scenarios
Source: Authors’ calculations.
Russia sometimes uses Western services to ship oil at a price above the cap, taking the risk that its shipments get sanctioned. Increasing the probability that cheating is punished lowers the price Russia expects to receive, with consequences identical to a reduction in the cap level.
By contrast, policies that reduce some capacity of the shadow fleet (“sidelining” some of its tankers) may harm Russia, even though they prompt Russia to rebuild its fleet rapidly. This happens, for example, if sidelining part of the fleet occurs while oil is also being sold at the ceiling, so that ceiling sales replace the lost fleet sales and there is no increase in the world oil price.
Conclusion
To conclude, we consider a variety of oil-market sanctions that have been have imposed on Russia to reduce the total export profits it uses to finance the war in Ukraine. As seen, tightening these sanctions is more effective if the induced increase in the world price can be significantly mitigated (if not entirely eliminated); otherwise, increased revenues from shadow fleet sales will weaken or undermine the intended effect of the tighter sanctions.
In one case we considered, no supplementary intervention is required for the sanction to be effective. Reducing Russia’s shadow fleet capacity when Russia is still selling at the ceiling price will induce an equal and offsetting increase in Russian sales at the ceiling, resulting in no increase in the world price.
However, other sanctions – lowering the ceiling, increasing its enforcement, or even reducing the shadow fleet capacity after Russian sales at the ceiling have ceased – will induce an increase in the world price sufficient to undermine the sanctions’ intended effect unless accompanied by a simultaneous expansion of non-Russian supply (presumably from the U.S. or OPEC) to dampen the increase in the world price. Supplemented in this way, the potency of each of these sanctions would be restored.
Overall, our results call attention to the need for complementary energy policies that would facilitate the response of non-Russian oil production to higher global prices.
References
- Cardoso, D. S., S. W. Salant, and J. Daubanes. (2025). The Dynamics of Evasion: The Price Cap on Russian Oil Exports and the Amassing of the Shadow Fleet. MIT CEEPR Working Paper 2025-05.
- Centre for Research on Energy and Clean Air. (2023). December 2023 Monthly Analysis on Russian Fossil Fuel Exports and Sanctions.
- Johnson, S., L. Rachel, and C. Wolfram. (2025). A Theory of Price Caps on Non-Renewable Resources. NBER Working Paper No. 31347.
- Spiro, D., H. Wachtmeister, and J. Gars. (2024). Assessing the Impact of Oil Sanctions on Russia. SSRN Working Paper.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Exposure to Violence and Prosocial Attitudes
This policy brief examines the academic literature on the impact of conflict exposure on pro-social behavior, a crucial component of resilience and societal cohesion. It also explores potential implications for public opinion, particularly in relation to Ukraine’s prospective EU accession and foreign relations.
Introduction
Since the full-scale invasion of Ukraine began on February 24, 2022, Russian forces have launched daily attacks with varying intensity. Living in a conflict zone profoundly affects individuals in multiple dimensions, including physical and mental health, as well as economic and social conditions. While reports often focus on the destruction of physical and human capital, social capital is equally affected by violence, influencing community resilience, cohesion, and cooperation. In conflict settings, identity can become more pronounced, particularly in distinguishing allies from adversaries.
This policy brief overviews the academic literature on this topic; the impact of conflict exposure on pro-social behavior broadly defined. This literature primarily examines post-conflict settings within the broader discourse on sustaining peace. It focuses on individuals directly engaged in combat or civilians directly affected by violence, particularly regarding the reintegration of former combatants and the rehabilitation of affected populations. As discussed below, results vary, depending on indicators used and the specific context. There is more consistent support for an impact on cooperation than on trust for instance. Another key finding in the literature is the differential behavior towards in-group members – those with whom individuals identify – versus out-group members, raising important questions about national identity and attitudes towards foreign allies. Based on this literature, the brief proceeds to discuss potential implications for public attitudes in Ukraine, focusing on Ukraine’s prospective EU accession.
Literature Overview
This review focuses on the empirical literature, though the theoretical basis spans psychology and the social sciences. Post-traumatic growth theory posits that adversity can foster positive change, whereas post-traumatic withdrawal theory suggests that violence exposure leads to distrust and social withdrawal. Economic arguments emphasize the need for rebuilding, enhanced safety concerns, or reduced time constraints for civic participation due to economic disruptions. Other perspectives highlight the detrimental effects of fragmented communities, given that trust and cooperation take time to develop, or suggest that individuals directly involved in violence may face social ostracization (see Fiedler 2023 for a detailed discussion).
Empirical studies on pro-social behavior employ diverse methodologies and data, including survey responses, indicators of political engagement, and controlled experiments measuring cooperation and trust. Methodology and research design vary, but most studies compare those with direct exposure to violence (treatment group) to those indirectly exposed (control group) within a post-conflict context. It is thus important to note that even the control group experiences some degree of conflict-related impact, meaning that studies specifically capture the effects of direct exposure.
Fiedler (2023), in a recent overview, categorizes the impact of violence into three main domains: personalized and political trust, cooperation, and political engagement. Most studies suggest a negative effect on trust, as seen in Kosovo (Kijewski & Freitag, 2018) and across Europe, the South Caucasus, and Central Asia post-World War II (Grosjean, 2014). Bauer et al. (2016) conducted a meta-analysis of 16 early studies measuring the effects of war violence on social participation, cooperation, and trust. When it came to trust, no significant impact of exposure to violence was found. Cassar et al. (2013) found that Tajik civil war survivors exhibited lower trust in close neighbors but not distant villagers, suggesting that intra-community political divisions played a role. However, a small number of studies report positive effects, such as Hall & Werner (2022), who found that victimized Syrian and Iraqi refugees in Turkey exhibited higher generalized trust.
In terms of cooperation, early studies overwhelmingly support a positive effect, including the meta-analysis of Bauer et al. (2016). For example, Bauer et al. (2014) held experimental games in Sierra Leone and Georgia, demonstrating that those directly exposed to violence exhibited greater altruism and inequality aversion. More recent work has come to different conclusions, however. Hager et al. (2019) found that Uzbek victims of violence in Kyrgyzstan were less cooperative in experimental games with both in-group and out-group members. Similarly, Cecchi & Duchoslav (2018) found that violence-exposed caregivers in Uganda contributed less in public goods games.
When it comes to political engagement, most studies find a positive effect, including the meta-analysis by Bauer et al. (2016) looking at participation in social groups and political engagement. Early and influential studies by Bellows & Miguel (2006, 2009), found that individuals in Sierra Leone with direct war exposure were more likely to participate in community meetings, elections, and social or political groups. Interestingly, while Kijewski & Freitag (2018) found that violence reduced trust in Kosovo, Freitag et al. (2019) found increased political participation in the same setting. Grosjean (2014) also reported a negative effect on trust but found that conflict victims were more likely to engage in civic organizations and collective action. These findings suggest that broad measures of prosocial behavior may be overly simplistic.
A common, and important, finding in much of the literature is with regards to differential behavior towards in-groups and out-groups. Bauer et al. (2014) found that exposure to violence increased altruism and inequality aversion only when interactions occurred within the in-group. Similar findings emerge in studies on soccer players in Sierra Leone (Cecchi et al., 2016) and trust experiments in Colombia (Francesco et al., 2023). Calvo et al. (2019) found that in conflict-affected areas of Mali, participation increased in kinship-based groups while it decreased in more inclusive organizations. Similarly, Mironova & Whitt (2016) found that Kosovars exhibited greater altruism and cooperation when interacting with in-group members. These findings align with research on parochial altruism in general, where cooperation and altruistic behavior are evolutionarily linked to in-group solidarity in response to external threats (e.g. Bernhard et al., 2006, Tajfel et al., 1979). There is thus a risk that social identity becomes more based on a narrow in-group (defined by ethnicity, religion, or language) potentially exacerbating societal divisions.
Implications for Ukraine
What do these insights imply for Ukraine? Given the context-dependent nature of the literature, definitive conclusions are challenging. Two studies on conflict exposure in eastern Ukraine offer preliminary insights. Mironova & Whitt (2021) examined fairness preferences among young Ukrainian men in Donbas, finding that, while no bias against ethnic Russians existed at the onset of violence in 2014, such bias increased after a year of conflict – particularly among non-combatants, contradicting typical patterns in the literature. Coupe & Obrizan (2016) used survey data from November 2014, showing that direct exposure to violence affected political behavior: physical damage reduced voter turnout, while property damage increased support for Western-leaning parties and stronger opposition to Russian aggression.
The strong effect on non-combatants in Mironova & Whitt (2021) highlights a key limitation in the literature – findings on direct exposure may not generalize to entire populations under invasion. Comparing directly and indirectly exposed individuals does not capture the broader societal impact, potentially leading to an overly optimistic view of conflict-induced prosocial behavior. If everyone is negatively affected, those with direct exposure to violence may simply be impacted a little less.
Of particular interest is how the war shapes national identity, in-group perceptions, and political preferences. These dynamics matter for domestic cohesion, interethnic relations, and Ukraine’s foreign policy trajectory. Focusing on the latter, the EU and the U.S. have provided substantial support during the full-scale invasion but delays and insufficiencies in aid may influence perceptions of these allies. EU accession presents economic benefits but entails lengthy and costly reforms with uncertain outcomes. Additionally, shifting U.S. policies and emerging geopolitical alignments may alter Ukrainian attitudes toward Western institutions.
Terror management theory (Landau et al., 2004) suggests that fear strengthens support for charismatic leadership, which, in fragile democratic settings, may favor more authoritarian tendencies. If Western democratic institutions lose appeal, this could negatively impact Ukraine’s political engagement, trust in allies, and willingness to align with European values, which are crucial for successful EU integration.
Conclusions
This review examined the literature on exposure to violence and prosocial behavior, discussing implications for Ukraine’s societal resilience and international alignment. The findings suggest no universal relationship between conflict exposure and prosociality; instead, effects vary depending on the recipient of trust, cooperation, and engagement. Generally, prosocial behavior increases within in-groups, while attitudes toward out-groups may remain unchanged or worsen. In the Ukrainian context, this has ramifications for internal cohesion and external diplomatic relations, particularly regarding the country’s path toward EU membership.
References
- Bauer, M., Blattman, C., Chytilová, J., Henrich, J., Miguel, E., & Mitts, T. (2016). Can War Foster Cooperation? Journal of Economic Perspectives, 30(3), 249–274.
- Bauer, M., Cassar, A., Chytilová, J., & Henrich, J. (2014). War’s Enduring Effects on the Development of Egalitarian Motivations and In-Group Biases. Psychological Science, 25(1), 47–57.
- Bellows, J., & Miguel, E. (2006). War and Institutions: New Evidence from Sierra Leone. American Economic Review, 96(2), 394–99.
- Bellows, J., & Miguel, E. (2009). War and Local Collective Action in Sierra Leone. Journal of Public Economics, 93(11–12), 1144–57.
- Bernhard, H., Fehr, E., & Fischbacher, U. (2006). Group Affiliation and Altruistic Norm Enforcement. American Economic Review, 96(2), 217–221.
- Calvo, T., Lavallée, E., Razafindrakoto, M., & Roubaud, F. (2019). Fear Not for Man? Armed Conflict and Social Capital in Mali. Journal of Comparative Economics, 48(2), 251–76.
- Cassar, A., Grosjean, P. A., Khan, F. J., & Lambert, M. (2022). Mothers, Fathers and Others: Competition and Cooperation in the Aftermath of Conflict. UNSW Business School Research Paper.
- Cecchi, F., Duchoslav, J. (2018). The Effect of Prenatal Stress on Cooperation: Evidence from Violent Conflict in Uganda. European Economic Review, 101, 35–56.
- Cecchi, F., Leuveld, K., & Voors, M. (2016). Conflict Exposure and Competitiveness: Experimental Evidence from the Football Field in Sierra Leone. Economic Development and Cultural Change, 64(3), 405-435.
- Coupé, T., & Obrizan, M. (2016). Violence and political outcomes in Ukraine—Evidence from Sloviansk and Kramatorsk. Journal of Comparative Economics, 44(1), 201-212.
- Fiedler, C. (2023). What Do We Know about How Armed Conflict Affects Social Cohesion? A Review of the Empirical Literature. International Studies Review.
- Francesco, B., Gómez, C., & Grimalda, G. (2023). Crime-related exposure to violence and prosocial behavior: Experimental evidence from Colombia. Journal of Behavioral and Experimental Economics, 104.
- Freitag, M., Kijewski, S., & Oppold, M. (2019). War Experiences, Economic Grievances, and Political Participation in Postwar Societies: an Empirical Analysis of Kosovo. Conflict Management and Peace Science, 36(4), 405–24.
- Grosjean, P. (2014). Conflict and Social and Political Preferences: Evidence from World War II and Civil Conflict in 35 European Countries. Comparative Economic Studies, 56(3), 424–51.
- Hager, A., Krakowski, K., & Schaub, M. A. X. (2019). Ethnic Riots and Prosocial Behavior: Evidence from Kyrgyzstan. American Political Science Review, 113(4), 1029–44.
- Hall, J., & Werner, K. (2022). Trauma and Trust: How War Exposure Shapes Social and Institutional Trust among Refugees. Frontiers in Psychology, 13, 786838.
- Kijewski, S., & Freitag, M. (2018). Civil War and the Formation of Social Trust in Kosovo: Post-traumatic Growth or War-Related Distress? Journal of Conflict Resolution, 62(4), 717–42.
- Landau, M. J., Solomon, S., Greenberg, J., Cohen, F., Pyszczynski, T., Arndt, J., Miller, C. H., Ogilvie, D. M., & Cook, A. (2004). Deliver us from Evil: The Effects of Mortality Salience and Reminders of 9/11 on Support for President George W. Bush. Personality and Social Psychology Bulletin, 30(9), 1136–1150.
- Mironova, V., & Whitt, S. (2016). Social Norms after Conflict Exposure and Victimization by Violence: Experimental Evidence from Kosovo. British Journal of Political Science, 48(3), 749–65.
- Mironova, V., & Whitt, S. (2021). Conflict and parochialism among combatants and civilians: Evidence from Ukraine. Journal of Economic Psychology, 86.
- Tajfel, H., Turner, J. C., Austin, W. G., & Worchel, S. (1979). An integrative theory of intergroup conflict. Organizational Identity: A Reader, 56-65.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Ukraine’s Fight Is Our Fight: The Need for Sustained International Commitment
We are at a critical juncture in the defense of Ukraine and the liberal world order. The war against Ukraine is not only a test of Europe’s resilience but also a critical moment for democratic nations to reaffirm their values through concrete action. This brief examines Western support to Ukraine in the broader context of international efforts, putting the order of magnitudes in perspective, and emphasizing the west’s superior capacity if the political will is there. Supporting Ukraine to victory is not just the morally right thing to do, but economically rational from a European perspective.
As the U.S. support to the long-term survival of Ukraine is becoming increasingly uncertain, European countries need to step up. This is a moral obligation, to help save lives in a democratic neighbor under attack from an autocratic regime. But it is also in the self-interest of European countries as the Russian regime is threatening the whole European security order. A Russian victory will embolden the Russian regime to push further, forcing European countries to dramatically increase defense spending, cause disruptions to global trade flows, and generate another wave of mass-migration. This brief builds on a recent report (Becker et al., 2025) in which we analyze current spending to support Ukraine, put that support in perspective to other recent political initiatives, and discuss alternative scenarios for the war outcome and their fiscal consequences. We argue that making sure that Ukraine wins the war is not only the morally right thing to do, but also the economically rational alternative.
The International Support to Ukraine
The total support provided to Ukraine by its coalition of Western democratic allies since the start of the full-scale invasion exceeded by October 2024 €200 billion. This assistance, that includes both financial, humanitarian and military support, can be categorized in various ways, and its development over time can be analyzed using data compiled by the Kiel Institute for the World Economy. A summary table of their estimates of aggregate support is provided below.
A particularly relevant aspect in light of recent news is that approximately one-third of total disbursed aid has come from the United States. The U.S. has primarily contributed military assistance, accounting for roughly half of all military aid provided to Ukraine. In contrast, the European Union—comprising both EU institutions and bilateral contributions from member states—stands as the largest provider of financial support. This financial assistance is crucial for sustaining Ukraine’s societal functions and maintaining the state budget.
Table 1. International support to Ukraine, Feb 2022 – Oct 2024
Source: Trebesch et al. (2024).
Moreover, the EU has signaled a long-term commitment to provide, in the coming years, an amount comparable to what has already been given. This EU strategy ensures greater long-term stability and predictability, guaranteeing that Ukraine has reliable financial resources to sustain state operations in the years ahead. Consequently, while a potential shift in U.S. policy regarding future support could pose challenges, it would not necessarily be insurmountable.
What is crucial is that Ukraine’s allies remain adaptable, and that the broader coalition demonstrates the ability to adjust its commitments, as this will be essential for sustaining the necessary level of assistance moving forward.
Putting the Support in Perspective
To assess whether the support provided to Ukraine is truly substantial, it is essential to place it in context through meaningful comparisons. One approach is to examine it in historical terms, particularly in relation to past instances of large-scale military and financial assistance. A key historical benchmark is the Second World War, when military aid among the Allied powers played a decisive role in shaping the outcome of the conflict. Extensive resources were allocated to major military operations spanning multiple continents, with the United States and the United Kingdom, in particular, dedicating a significant share of their GDP to support their allies, including the Soviet Union, France, and other nations. As seen in Figure 1, by comparison, the current level of aid to Ukraine, while substantial and essential to its defense, remains considerably smaller in relation to GDP.
Figure 1. Historical comparisons
Source: Trebesch et al. (2024).
Another way to assess the scale of support to Ukraine is by comparing it to other major financial commitments made by governments in response to crises. While the aid allocated to Ukraine is significant in absolute terms, it remains relatively modest when measured against the scale of other programs, see Figure 2.
A recent example is the extensive subsidies provided to households and businesses to mitigate the impact of surging energy prices since 2022. Sgaravatti et al. (2021) concludes that most European countries implemented energy support measures amounting to between 3 and 6 percent of GDP. Specifically, Germany allocated €157 billion, France and Italy each committed €92 billion, the UK spent approximately €103 billion. These figures represent 5 to 10 times the amount of aid given to Ukraine so far, with some countries, such as Italy, allocating even greater relative sums. On average, EU countries have spent about five times more on energy subsidies than on Ukraine aid. Only the Nordic countries and Estonia have directed more resources toward Ukraine than toward energy-related support. Although not all allocated funds have been fully disbursed, the scale of these commitments underscores a clear political and financial willingness to address crises perceived as directly impacting domestic economies.
Figure 2. EU response to other shocks (billions of €)
Source: Trebesch et al. (2024).
Another relevant comparison is the Pandemic Recovery Fund, also known as Next Generation EU. With a commitment of over €800 billion, this fund represents the EU’s comprehensive response to the economic consequences of the Covid-19 pandemic. Again, the support to Ukraine appears comparatively small, about one seventh of the Pandemic Recovery Fund.
The support to Ukraine is also much smaller in comparison to the so-called “Eurozone bailout”, the financial assistance programs provided to several Eurozone member states (Greece, Ireland, Spain and Portugal) during the sovereign debt crisis between 2010 and 2012. The programs were designed to stabilize the economies hit hard by the crisis and to prevent the potential spread of instability throughout the Eurozone.
Overall, the scale of these commitments underscores a clear political and financial willingness and ability to address crises perceived as directly impacting domestic citizens. This raises the question of whether the relatively modest support for Ukraine reflects a lack of concern among European voters. However, this does not appear to be the case. In survey data from six countries – Belgium, Germany, Hungary, Italy, the Netherlands, and Poland – fielded in June 2024, most respondents express satisfaction with current aid levels, and a narrow majority in most countries even supports increasing aid (Eck and Michel, 2024).
A further illustration comes from the Eurobarometer survey conducted in the spring of 2024 which asked: “Which of the following [crises] has had the greatest influence on how you see the future?”. Respondents could choose between different crises, including those mentioned above, and the full-scale invasion of Ukraine.
Figure 3 illustrates the total commitments made by EU countries for Ukraine up until October 31, 2024, compared to other previously discussed support measures, represented by the blue bars. The yellow bars, on the other hand, show a counterfactual allocation of these funds, based on public priorities as indicated in the Eurobarometer survey. Longer yellow bars indicate that a higher proportion of respondents perceived this crisis as having a greater negative impact on their outlook for the future. By comparing the actual commitments (blue bars) with this hypothetical allocation (yellow bars)—which reflects how resources might have been distributed if they aligned with the population’s stated priorities—it becomes evident that there is substantial public backing for maintaining a high level of support for Ukraine. The results show that the population prioritizes the situation in Ukraine above several other economic issues, including those that directly affect their own personal finances.
Figure 3. Support to Ukraine compared to other EU initiatives – what do voters think?
Source: Trebesch et al. (2024); Niinistö (2024); authors’ calculations.
The Costs of Not Supporting Ukraine
When discussing the costs of support to Ukraine it is important to understand what the correct counterfactual is. The Russian aggression causes costs for Europe irrespective of what actions we take. Those costs are most immediately felt in Ukraine, with devastating human suffering, the loss of lives, and a dramatic deterioration in all areas of human wellbeing. Also in the rest of Europe, though, the aggression has immediate costs, in the economic sphere primarily in the form of dramatically increased needs for defense spending, migration flows, and disruptions to global trade relationships. These costs are difficult to determine exactly, but they are likely to be substantially higher in the case of a Russian victory. Binder and Schularik (2024) estimate increased costs for defense, increased refugee reception and lost investment opportunities for the German industry at between 1-2 percent of GDP in the coming years. As they put it, the costs of ending aid to Ukraine are 10-20 times greater than continuing aid at Germany’s current level.
Any scenario involving continued Russian aggression would demand substantial and sustained economic investments in defense and deterrence across Europe. Clear historical parallels can be drawn looking at the difference in countries’ military spending during different periods of threat intensity. Average military spending in a number of Western countries during the Cold War (1949-1990) was about 4.1 percent of GDP, much higher in the U.S. but also in Germany, France and the UK. In the period after 1989-1991 (the fall of the Berlin Wall, the dissolution of the Soviet Union), the amounts fell significantly. The average for the same group of countries in this period is about 2 percent of GDP and only 1.75 percent if the U.S. is excluded.
Also after 1991 there is evidence of how perceived threats affect military spending. Figure 4 plots the change in military spending over GDP between 2014-2024 against the distance between capital cities and Moscow. The change varies between 0 (Cyprus) and around 2.25 (Poland) and shows a very clear positive correlation between increases in spending and proximity to Moscow. There has also in general been a substantial increase in military spending after 2022 in several European countries, but in a scenario where Russia wins the war, these will certainly have to be increased further and maintained at a high level for longer. An increase in annual military expenditure in relation to GDP in the order of one to two percentage points would mean EUR 200-400 billion per year for the EU, while the total EU support to Ukraine from 2022 to today is just over €100 billion.
Figure 4. Increase in military expenditures in relation to distance to Moscow
Source: SIPRI data, authors’ calculations.
A Russian victory would also have profound consequences for migration flows, with the most severe effects likely in the event of Ukraine’s surrender. The Kiel Institute estimates the cost of hosting Ukrainian refugees at €26.5 billion (4.2 percent of GDP) for Poland, one of the countries that received the largest flows. Beyond migration, a Russian victory would also reshape the global geopolitical order. Putin has framed the war as a broader conflict with the U.S. and its democratic allies, while an emerging alliance of Russia, Iran, North Korea, and China is positioning itself as an alternative to the Western-led system. A Ukrainian defeat would weaken the authority of the U.S., NATO, and the rules-based international order, potentially driving more nations in the Global South toward authoritarian powers for military and economic support. This shift could disrupt global trade, affect access to food, metals, and energy. Estimating the full economic impact of such a shift is difficult, but comparisons can be drawn with other global shocks. The European Union’s GDP experienced a significant contraction due to the Covid-19 pandemic, 5.9 percent contraction in real GDP according to Eurostat, 6.6 percent according to the European Central Bank. While the economy rebounded relatively quickly from the pandemic, a permanent geopolitical realignment caused by a Russian victory would likely have far more severe and lasting economic consequences.
Given that Ukraine is at the forefront of Russia’s aggression, its resilience serves as a critical test of Europe’s ability to withstand potential future threats. Thus, strengthening our own security and economic stability in the long term is inseparable from strengthening Ukraine’s resilience now. The fundamental difference lies in the long-term trajectory of these investments. In a scenario where Ukraine is victorious, military and financial aid during the war would eventually transition into reconstruction efforts and preparations for the country’s integration into the EU. This outcome is undeniably more favorable—both economically and in humanitarian terms—not only for Ukraine but for Europe as a whole. Therefore, an even more relevant question is whether the level of support is enough for Ukraine to win the war.
Is Sufficient Support Feasible?
Is it even reasonable to think that we in the West could be able to support Ukraine in such a way that they can militarily defeat Russia? Russia is spending more on its war industry than it has since the Cold War. In 2023, it spent about $110 billion (about 6 percent of GDP). By 2024, this figure is expected to have increased to about $140 billion (about 7 percent of GDP). These amounts are huge and represent a significant part of Russia’s state budget, but they are not sustainable as long as sanctions against Russia remain in place (SITE, 2024). For the EU, on the other hand, the sacrifices needed to match this expenditure would not be as great. The EU’s GDP is about ten times larger than Russia’s, which means that in absolute terms the equivalent amount is only 0.6-0.7 percent of the EU’s GDP. If the U.S. continues to contribute, the share falls to below 0.3 percent of GDP.
Despite the economic advantage of Ukraine’s allies over Russia, several factors could still shift the balance of power in Russia’s favor. One key issue is military production capacity—Russia has consistently outproduced Ukraine’s allies in ammunition and equipment. While Western economies have the resources to manufacture superior weaponry, actual production remains insufficient, requiring both increased capacity and political will. Another challenge is cost efficiency. Military purchasing power parity estimates suggest that Russia can produce approximately 2.5 times more military equipment per dollar than the EU, giving it a cost advantage in volume production. However, this does not fully compensate for its overall economic disadvantage, particularly when factoring in quality differences.
Manpower is also a critical factor. Russia’s larger population allows for sustained mobilization, but at a steep financial cost. Soldiers are recruited at a minimum monthly salary of $2,500, with additional bonuses bringing the first-year cost per recruit to three times the average Russian annual salary. Compensation for injured and fallen soldiers further strains state finances, with estimated payouts reaching 1.5 percent of Russia’s GDP between mid-2023 and mid-2024. Over time, these costs limit Russia’s ability to fund its war effort, making mass mobilization financially unsustainable.
Overall, advanced Western weaponry and superior economic capacity can match Russia’s advantage in manpower if the political will is there. Additionally, Russia’s already fragile demographic situation is deteriorating due to battlefield losses and wartime emigration. Any measure that weakens Russia’s economic capacity—particularly through sanctions and embargoes—diminishes the strategic advantage of its larger population and serves as a crucial complement to military and financial support for Ukraine.
Conclusion
Ukraine’s western allies have provided the country with substantial military and financial support since the onset of the full-scale invasion. Yet, relative to the gravity of the risks involved, previous responses to economic shocks, and citizens’ concerns about the situation, the support is insufficient. The costs of a Russian victory will be higher for Europe, even disregarding the human suffering involved. With U.S. support potentially waning, EU needs to pick up leadership.
References
- Becker, Torbjörn; and Anders Olofsgård; and Maria Perrotta Berlin; and Jesper Roine. (2025). “Svenskt Ukrainastöd i en internationell kontext: Offentligfinansiella effekter och framtidsscenarier”, Commissioned by the Swedish Fiscal Policy Council.
- Binder, J. & Schularick, M. (2024). “Was kostet es, die Ukraine nicht zu unterstützen?” Kiel Policy Brief No. 179.
- Eck, B & Michel, E. (2024). “Breaking the Stalemate: Europeans’ Preferences to Expand, Cut, or Sustain Support to Ukraine”, OSF Preprints, Center for Open Science.
- Niinistö, S. (2024) .“Safer Together – Strengthening Europe’s Civilian and Military Preparedness and Readiness” European Commission Report.
- Sgaravatti, G., S. Tagliapietra, C. Trasi and Zachmann, G. (2021). “National policies to shield consumers from rising energy prices”, Bruegel Datasets, first published 4 November 2021.
- SITE. (2024). “The Russian Economy in the Fog of War”. Commissioned by the Swedish Government.
- Trebesch, C., Antezza, A., Bushnell, K., Bomprezzi, P., Dyussimbinov, Y., Chambino, C., Ferrari, C., Frank, A., Frank, P., Franz, L., Gerland, C., Irto, G., Kharitonov, I., Kumar, B., Nishikawa, T., Rebinskaya, E., Schade, C., Schramm, S., & Weiser, L. (2024). “The Ukraine Support Tracker: Which countries help Ukraine and how?” Kiel Working Paper No. 2218. Kiel Institute for the World Economy.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Road Congestion Pricing with A Public Transport Cashback Mechanism
Traffic jams are a major problem in cities leading to wasted time, air pollution, reduced accessibility, and, in turn, lower economic activity. Transport economists widely agree that charging drivers fees for using busy roads during rush hours (congestion pricing) is the best answer to road congestion problems. However, such a policy is rarely used, mostly because people see it as unfair in how it affects different income groups. We propose an innovative personalized public transport cashback mechanism to make congestion pricing more acceptable. Recent surveys in Riga and Vienna show that people are more willing to support the introduction of congestion pricing when it includes a cashback component.
Road Congestion Pricing and Its Discontent
Road Congestion Pricing
Traffic jams happen when too many cars at the same place and at the same time use a road of a limited capacity. Building new roads or lanes is expensive, especially in cities, and it only provides short- and medium-term traffic improvements, with little impact on congestion in the long term (Ossokina et al., 2023; Hymel, 2019). Duranton and Turner (2011) show that when major roads are expanded, more people start using them and, over time, congestion returns to the same level as before. Meanwhile, a travel mode shift from cars to public transport and bicycles also requires investments and is difficult to implement in practice.
Dynamic congestion pricing, when road tolls vary based on the time of day, is designed to spread out traffic flow over time without the need to expand road infrastructure (Small and Verhoef, 2007). Notably, this approach does not aim to reduce the total number of cars on the road. Instead, it encourages them to spread their travel times more evenly, ensuring that the road capacity can handle the traffic without congestion.
Dynamic congestion pricing typically works as follows: there is no charge at night, the toll is small in the early morning, then it gradually increases during the morning until it reaches its peak. The toll then decreases in the afternoon before rising again during the evening. This system works in a congestion zone, which is usually the busiest areas of a city. When a car enters the zone, video cameras automatically identify it without stopping the car. There are no toll booths on the streets – an electronic system calculates the toll based on the time of day and charges the driver automatically through a linked account. Cities can tailor the system to fit their specific geography and infrastructure, offering exemptions for certain vehicles and pass-through traffic (for practical examples, visit the Swedish Transport Agency’s website to learn more about congestion pricing in Stockholm and Gothenburg).
By reducing the number of cars during congested hours, such dynamic pricing benefits both the city and its residents:
- (i) Drivers enjoy faster travel times as road toll allows them to gain time in exchange for money. For example, in the morning, drivers can leave for work later as they no longer need to account for time spent in traffic jams.
- (ii) Non-drivers enjoy congestion-free neighborhoods with improved air quality and overall higher quality of life.
- (iii) The city can tackle congestion without making large investments in new roads. The funds collected from drivers not only cover the toll system maintenance, but also contribute to the cost of the infrastructure they use. The funds may also be used to improve public transportation.
Low Public Acceptability
In light of the benefits of congestion pricing, it seems surprising that very few cities actually use it. Notable examples include London, Singapore, Stockholm and Gothenburg. New York City introduced its congestion charge on the 5th of January 2025, the first in the US. This stands in stark contrast to paid on-street parking, another transport policy measure that has been successfully implemented in almost every large city across Europe. The disparity arises because the general public often sees congestion pricing as an additional tax, believing it unfairly affects lower-income individuals. Presumably, low-income individuals have less flexible work schedules and fewer travel choices, making it harder for them to avoid traveling during high-toll periods (Selmoune et al., 2020). Moreover, they would spend a larger share of their income on road tolls compared to wealthier drivers, which makes congestion pricing a regressive policy.
Even though congestion pricing is not a tax and is not meant to redistribute funds, it may still appear as such to the public. This perception leads to vocal public resistance to road pricing which, in turn, discourages politicians from implementing the policy. Another reason for public skepticism is a lack of trust in politicians and municipal officials to manage the collected funds effectively, with concerns that the money may not be spent in ways that benefit the city.
Public Transport Cashback
Cashback Mechanism
To address the perceived unfairness of congestion pricing and fears about the misuse of collected funds, we propose a personalized public transport cashback mechanism – a novel approach that has not yet been implemented anywhere. Instead of collecting the tolls, we suggest immediately transferring the money back to drivers in the form of public transport vouchers or cashback. That is, when a driver pays road toll, almost the entire amount is credited directly to their personal public transport account/card as cashback, while a small portion of the toll is retained to cover maintenance costs of the road pricing system. The cashback can only be used to pay for public transport. Since the road toll is returned to drivers in the form of public transport cashback, there is no need for money redistribution by public authorities.
Our pricing mechanism retains the core feature of conventional dynamic road pricing: the road toll motivates drivers to adjust their travel times, helping to prevent traffic jams. The toll values are likely to be different though, as the toll now has additional value to drivers who might use the cashback for public transport. While this feature reduces the efficiency of the toll compared to conventional congestion pricing, the cashback mechanism also introduces a new beneficial property. By motivating some drivers to occasionally switch to public transport, it further reduces car use and helps ease congestion. The interplay between these two factors ultimately determines the required congestion toll values.
The cashback can be accumulated over several years and is non-transferable to prevent drivers from using their cars more often. The cashback mechanism would likely work for private cars only, though exceptions and specific features can be adjusted to local circumstances. Public transport companies are likely to benefit from additional revenue through increased ticket sales and unused, expired cashback. However, since public transport ticket prices do not always cover the full cost of providing the service, it is important to balance the additional costs of implementing the cashback mechanism with the expected revenue gains. This could potentially be done by reducing the cashback portion relative to the toll share retained for system maintenance.
However, congestion pricing with a cashback mechanism is not a standalone solution or a silver bullet. It works best when combined with improvements of the public transport network, as this encourages drivers to make regular use of their cashback.
Transport Survey Data
The key idea behind the cashback mechanism is that it gives drivers direct and transparent control of their money, which is expected to make road pricing policy more acceptable. Whether this holds true or not is an empirical matter. This was tested by considering the means of a representative survey conducted in Riga (Latvia) and Vienna (Austria) in the summer of 2024. The survey includes 1,000 residents in both capitals and their respective surrounding municipalities. It features questions about respondents’ socio-demographic characteristics, current travel options, commute patterns (including accompanying trips with children), and their political and social attitudes. It also includes two stated-choice experiments exploring the acceptability of congestion pricing and potential changes in travel behaviour if such pricing is introduced. While detailed data analysis is still ongoing, this policy brief highlights some intriguing preliminary insights.
In the survey, we ask the respondents whether they would vote in a referendum in favor of congestion pricing under four different scenarios for using the collected toll funds: (i) transferring them as a public transport cashback, (ii) sharing them equally among all city inhabitants, (iii) leaving the allocation decisions to local politicians, or (iv) using them to support eco-friendly transport. Respondents were familiarized with the topic before answering the question by participating in a stated-choice experiment about congestion pricing acceptability. The experiment included a detailed explanation of how congestion pricing works, along with a potential congestion zone map. Figure 1 shows responses from Riga, and Figure 2 from Vienna.
Figure 1. Responses from Riga. “Would you support congestion pricing in a referendum if the collected toll funds were used this way?”
Source: Representative survey in Riga in summer 2024.
Figure 2. Responses from Vienna. “Would you support congestion pricing in a referendum if the collected toll funds were used this way?”
Source: Representative survey in Vienna in summer 2024.
In Riga, the cashback option is the most popular, with more participants supporting than opposing it. The overall positive attitude towards congestion pricing with the cashback option suggests that Riga might already be ready to implement it. In Vienna, the cashback ranks a close second after the green transport option. This result shows that cashback might be a viable option also in Vienna.
Conclusion
To overcome public skepticism towards road congestion pricing, we propose a cashback mechanism. It involves returning toll money back to drivers as public transport cashback. The cashback mechanism has several benefits: drivers retain some control of their money, there is no need to redistribute collected toll funds, and it helps reduce congestion without major investments in road infrastructure. Surveys in Riga and Vienna in 2024 show support for the cashback option. While the specifics of such a solution should be tailored to each city’s needs, many cities struggling with congestion could benefit from implementing road congestion pricing with a public transport cashback mechanism.
Acknowledgment
This policy brief is based on a collaborative research effort by economists Sergejs Gubins from Riga (BICEPS) and Stefanie Peer and Martina Reggerova from Vienna (WU) as part of the “Tolls That Work” project, supported by the ERA-NET research grant. Agreement No ES RTD/2023/11. See project updates on the webpage:
https://www.wu.ac.at/en/spatialeconomics/projects/city-tolls-that-work
References
- Duranton, G., & Turner, M. A. (2011). The fundamental law of road congestion: Evidence from US cities. American Economic Review, 101(6), 2616–2652. https://doi.org/10.1257/aer.101.6.2616
- Hymel, K. (2019). If you build it, they will drive: Measuring induced demand for vehicle travel in urban areas. Transport Policy, 76, 57–66. https://doi.org/10.1016/j.tranpol.2018.12.006
- Ossokina, I. V., van Ommeren, J., & van Mourik, H. (2023). Do highway widenings reduce congestion? Journal of Economic Geography, 23(4), 871–900. https://doi.org/10.1093/jeg/lbad025
- Selmoune, A., Cheng, Q., Wang, L., & Liu, Z. (2020). Influencing factors in congestion pricing acceptability: A literature review. Journal of Advanced Transportation, 2020, 4242964, 11 pages. https://doi.org/10.1155/2020/4242964
- Small, K. A., & Verhoef, E. T. (2007). The economics of urban transportation. London: Routledge.
- The Swedish Transport Agency. https://www.transportstyrelsen.se/en/road/vehicles/taxes-and-fees/road-tolls/congestion-taxes-in-stockholm-and-gothenburg
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
How Social Assistance Shapes Election Outcomes: The Case of Georgia
This policy brief investigates the relationship between social assistance programs and election outcomes in Georgia, focusing on the 2024 parliamentary elections. Our regression analysis establishes a statistically significant link between an increase in social assistance beneficiaries and the vote share obtained by the incumbent Georgian Dream party. The results raise critical questions about the potential use of social assistance programs as a strategic political tool. Specifically, a 1 percentage point increase in targeted social assistance beneficiaries as a share of the population lead to an, on average, 0.5 percentage point increase in the Georgian Dream’s vote share, even after controlling for poverty-related factors. The findings recognize the dual impact of social assistance programs – alleviating poverty while shaping political behavior. They also underscore the need for ensuring that social assistance remains focused on addressing the needs of vulnerable populations without exerting undue political influence.
Introduction
The relationship between social assistance programs and electoral outcomes has gathered significant attention in both academic and policy circles, especially in the last decade. Social assistance programs, designed to support vulnerable populations, often carry political implications, particularly in developing democracies where incumbent governments may leverage these programs to secure voter loyalty. In Georgia, one of the largest components of social assistance is the targeted living allowance program, which, unlike other types of social assistance – such as those for individuals with special needs, internally displaced people, or elderly population –relies on assessing the beneficiaries’ poverty levels through proxy means testing (PMT). This makes subsistence allowance benefits vulnerable to biased, favorable selection by those in power. Allegations exist that the government may have strategically used this program, including increasing the number of beneficiaries in the lead-up to elections to secure votes or threatening existing beneficiaries with the withdrawal of their assistance based on their disclosed political preferences (Shubladze (2024); Japaridze (2023); Social Justice Center (2024)).
This policy brief explores the impact of social assistance on electoral outcomes in Georgia, specifically assessing whether increases in the number of targeted subsistence allowance beneficiaries during the 2020-2024 period influenced the votes received by the incumbent party in the 2024 parliamentary elections.
This analysis is especially important given the recent developments in Georgia’s political landscape. The 2024 parliamentary elections marked a critical juncture, with the Georgian Dream claiming to have secured 53.93 percent of the votes. Concerns over the fairness and transparency of the elections have been widespread. The Organization for Security and Co-operation in Europe’s (OSCE) Office for Democratic Institutions and Human Rights (ODIHR) reported systematic election irregularities, including pressure on voters, media bias, unequal campaign conditions, and election-day practices that compromised the ability of some voters – including public sector employees and recipients of social assistance – to cast their ballots without fear of retribution.
Our regression analysis documents a positive relationship between the number of living allowance beneficiaries and the votes garnered by the incumbent party across Georgian municipalities, raising further concerns about the integrity of the electoral process, and the allocation of state funds.
Political Implications of Social Assistance Programs: A Global Perspective
The link between social assistance and electoral outcomes has been widely studied. Social assistance programs often serve a dual purpose: they alleviate poverty and provide tangible support to vulnerable citizens while also shaping political behavior, particularly voting patterns. These programs can enhance incumbents’ electoral support by fostering gratitude among recipients, signaling government competence in addressing social needs, or creating concerns among beneficiaries that their political preferences if exposed, may influence the government’s decisions when choosing the beneficiaries of social assistance.
Research by De La O (2013) provides a compelling case in the context of Mexico. Examining the Progresa/Oportunidades conditional cash transfer program, De La O find that the program led to an increase in both voter turnout and incumbent vote share.
Zucco (2013) contributes further evidence from Brazil, where the Bolsa Família conditional cash transfer program emerged as a cornerstone of electoral strategy. Zucco demonstrates that municipalities with higher proportions of cash transfer beneficiaries tended to favor incumbent candidates in three different presidential elections, establishing a clear link between social assistance and voting behavior.
Adding to this body of work, Layton & Smith (2015) provide further insight into the nuanced ways in which targeted social assistance programs influence voter behavior in Latin America. The authors theorize that such programs simultaneously mobilize non-voters and convert opposition supporters, with variations based on country-level political and programmatic differences.
In addition, recent research from Indonesia further illustrates the impact of social assistance budgets on electoral outcomes. A study by Dharma, Syakhroza, and Martani (2022) examines 212 regencies and cities in Indonesia where incumbents participated in local elections. The findings reveal a direct positive effect of social assistance spending on incumbent votes. The authors further claim that high political competition counteracts incumbents’ advantages and mitigates the effectiveness of such spending.
The international literature thus provides valuable motivation for exploring the Georgian case, where social assistance may play a similar role in shaping voting behavior.
The Georgian Context
The deeply controversial October 26, 2024, parliamentary elections in Georgia mark a pivotal moment in the country’s political history. According to the Central Election Committee of Georgia, the ruling party, the Georgian Dream, secured 53.93 percent of the votes, maintaining its dominant position in Georgian politics. However, the elections were accompanied by widespread allegations of electoral malpractice, casting a shadow over their legitimacy and raising concerns about the future of democratic governance in the country.
OSCE’s ODIHR provided a comprehensive observation of the electoral process, noting both positive aspects and critical shortcomings. While the elections were generally well-administered, ODIHR’s final report emphasized significant concerns related to the broader political environment. Key issues included the adoption of legislation that undermined fundamental freedoms, restrictions on civil society, and a pervasive atmosphere of voter intimidation. Specific election-day practices, such as pressuring voters and leveraging administrative resources, were highlighted as undermining the integrity of the process. The report also mentions instances in 16 municipalities where public sector employees and economically vulnerable groups, particularly those reliant on social assistance, faced pressure to support the ruling party. Such fear of losing social benefits or facing retribution at work creates an atmosphere where voters struggle to form independent opinions and vote independently.
Further scrutiny from independent analysts has shed light on systematic irregularities that suggest the elections may not have reflected the genuine will of the Georgian electorate. Gutbrod (2024) suggests that tactics such as vote buying, mass intimidation, and direct manipulation of electoral outcomes were employed, leading to statistical anomalies. Specifically, the Georgian Dream’s support increased disproportionately in precincts linked to reported violence and irregularities. Additionally, social assistance beneficiaries were identified as a target group for snowball mobilization, organized by individuals affiliated with the Georgian Dream – a method where participants are encouraged to mobilize or identify a certain number of additional people to expand voter outreach and engagement.
Social Assistance in Georgia
The Law of Georgia on Social Assistance outlines several types of social welfare programs aimed at addressing the needs of vulnerable populations. These include living allowance, reintegration assistance, foster care allowance, adult family member care allowance, non-monetary social assistance, and social package. Among these, the targeted social assistance program, commonly referred to as the “living allowance“, holds particular significance. This program is designed to provide financial support to families living in extreme poverty. Eligibility for the living allowance is determined through a proxy means test that evaluates the socioeconomic conditions of applicants, ensuring that the assistance reaches those most in need. For this policy brief, the focus will be on beneficiaries of the living allowance (hereafter social assistance beneficiaries), as their numbers and electoral behavior present a unique opportunity to analyze the intersection of social assistance and voting patterns in Georgia.
As of October 2020 (previous parliamentary elections’ date) 142,870 families in Georgia received social assistance, benefiting a total of 510,343 individuals. The total amount of social assistance transfers during this period amounted to 28,825,259 GEL. Over the next four years, leading up to the 2024 Parliamentary Elections, social assistance grew significantly. By October 2024, the number of families receiving assistance had increased by 25 percent to 178,107 families, while the number of individual beneficiaries rose by 34 percent, reaching 684,432. The most notable expansion occurred in the total amount of social assistance transfers, which surged by 143 percent to 69,936,512 GEL. This corresponds to a cumulative annual growth rate of 25 percent (Social Service Agency of Georgia, 2024).
In 2022, an important modification was introduced for social assistance beneficiaries aged 18 years to retirement age and without disabilities or serious health conditions, offering employment opportunities mainly, in the public sector with a salary of up to 300 GEL per month. These wages did not affect recipients’ existing social assistance benefits. Participants had the option to take suitable public sector jobs, formalize any informal employment, or, if formally employed in the private sector, provide necessary documentation. The modification covered also new beneficiaries who were not already formally employed. Notably, families or individuals enrolled in the program were guaranteed eligibility for a living allowance for four years, as their social assistance status would not be reassessed during this period.
As of October 2024, 50,962 families were enrolled in the program with guaranteed social assistance, accounting for 28.6 percent of all families receiving social assistance. The monthly spending of social assistance transferred to these families amounted to 22,766,706 GEL, representing 33 percent of the total social assistance transfers.
The significant increase in social assistance beneficiaries and the introduction of the 2022 program for employing social assistance recipients, guaranteeing them four years of social assistance transfers, highlight the growing scope and influence of targeted social welfare initiatives in Georgia. While these developments may have addressed pressing socioeconomic challenges, they also raise important questions about the potential political motivations. Specifically, the substantial increase in the number of beneficiaries and the guaranteed eligibility linked to employment programs could be interpreted as mechanisms to foster voter loyalty and mobilization in favor of the ruling party.
Methodology and Results
To examine the relationship between the increase in social assistance beneficiaries and electoral outcomes, particularly the votes garnered by the incumbent Georgian Dream party, we employ a regression analysis framework. This statistical method allows us to explore whether and to what extent the growth in social assistance recipients is associated with the changes in the vote share of the incumbent party. Since social assistance depends on the varying levels of poverty across municipalities, we incorporate control variables that isolate the effect of economic well-being, minimizing potential confounders.
The study utilizes data from two primary sources: information on social assistance recipients, including families, and individuals, and the total amount of transfers across municipalities, was retrieved from the Social Service Agency of Georgia. This dataset covers 64 municipalities (and self-governing cities) in Georgia. From 2022, data includes families and individuals guaranteed to retain their socially vulnerable status for four years under the State Program for Promoting Public Employment. Second, election data was sourced from the Central Election Commission of Georgia, covering both the 2024 and 2020 parliamentary elections. The 2024 data covers the results from both electronic and non-electronic voting. Key variables include the number of registered voters, total votes cast, and votes obtained by the Georgian Dream and opposition parties. This election data is also aggregated at the level of the 64 municipalities (and self-governing cities).
Information on poverty levels in Georgian municipalities is not publicly available; therefore, we utilize control variables for employment and economic activity with the latter proxied by either the municipalities’ tax revenues or the value added generated in the private sector. Information on employment and value added are gathered from the National Statistics Office of Georgia, while data on tax revenues is retrieved from the Ministry of Finance.
The following table describes the results of the regression analysis.
Table 1. Regression analysis results
Source: Author’s calculations.
Note: The values in parentheses indicate the p-value. *Significant at the 10 percent level; **Significant at the 5 percent level; ***Significant at the 1 percent level.
The first regression (column 1) investigates the relationship between the change in social assistance beneficiaries as a share of the population and the change in the Georgian Dream party’s vote shares, displaying a significant relationship between the two. Specifically, the coefficient (0.49) is significant at the 5 percent level, suggesting that a 1 percentage point increase in social assistance beneficiaries as a share of the population, increases the vote share for the Georgian Dream by approximately 0.49 percentage points.
To control for the effect of poverty, we first use employment rates in 2023 (the latest available data) as a proxy for poverty. Column 2 presents these results. In this specification, the coefficient for the change in social assistance beneficiaries remains significant at 5 percent level and its value (0.47) remains consistent with the previous specification. The model further suggests that poverty is also positively and significantly (at the 1 percent level) associated with incumbent votes – the higher the poverty (lower employment) in municipalities, the higher the Georgian Dream vote share.
In the next step (column 3), we model the relationship between the change in the Georgian Dream’s vote share, change in employment as a share of the population, mobilized local tax revenues per capita, and the change in number of social assistance beneficiaries as a share of the population. Change in employment, calculated as the difference between 2019 and 2023 employment levels (as a share of the population), is used as a proxy for change in poverty. Tax revenues per capita for 2023 reflect economic activity across municipalities and self-governing cities, serving as a proxy for well-being. As seen in the table, change in employment is not statistically significant, however, the amount of tax revenues mobilized across municipalities is modestly significant. The coefficient for change in social assistance beneficiaries is once again statistically significant and consistent with the other specifications in terms of magnitude (at 0.51).
As a robustness test (column 4) we replace the previously used proxy for economic well-being (tax revenues), with the private sector value added per capita for 2023, which significantly (at the 1 percent level) correlates with an increase in the vote share for the incumbent party. Changes in employment remain insignificant. Importantly, the coefficient for change in social assistance beneficiaries remains positive (0.53) and statistically significant at the 1 percent level.
The discussed regression models were tested for a different dependent variable as well. In addition to observing the impact of change in vote shares, we also analyzed the impact on the number of votes cast for the Georgian Dream party between the 2020 and 2024 Parliamentary Elections. Changes in social assistance beneficiaries remain a significant explanatory variable in this specification as well.
The estimated impact of social assistance is consistent across all models, both in magnitude and significance, reinforcing the finding that increases in living allowance beneficiaries are strongly associated with higher vote shares for the Georgian Dream party, underscoring the critical role of social assistance in shaping electoral outcomes.
Conclusion
The analysis demonstrates a strong and statistically significant relationship between the increase in social assistance beneficiaries and the vote share obtained by the incumbent Georgian Dream party in the 2024 parliamentary elections. Even after controlling for poverty and economic well-being, the results highlight the impact of social assistance in shaping electoral outcomes. The findings suggest that a 1 percentage point rise in social assistance beneficiaries as a share of the population translates into a 0.47–0.53 percentage point increase in the Georgian Dream’s vote share. When contextualized within the overall election results, these estimates suggest that the expansion of the targeted social assistance program may have garnered the Georgian Dream an additional 45 ,500 to 50,000 votes, representing 2.2–2.5 percent of the total votes.
The results raise critical questions about the potential use of social assistance programs as a strategic political tool. The robustness of the relationship across multiple models suggests that the observed trends are not merely byproducts of economic conditions but reflect a deliberate link between social assistance expansion and electoral outcomes. The implications are significant for democratic governance in Georgia. The strategic use of social welfare programs risks undermining public trust in the electoral process and highlights the need for greater transparency and accountability in the implementation of social assistance policies. Recognizing the dual impact of these programs – alleviating poverty while potentially shaping political behavior – will be critical in fostering fairer electoral conditions and ensuring that social assistance remains focused on addressing the needs of vulnerable populations without undue political influence.
References
- De La O, A. L. (2013). “Do conditional cash transfers affect electoral behavior? Evidence from a randomized experiment in Mexico”, American Journal of Political Science, 57(1), 1-14.
- Dharma, F., Syakhroza, A., & Martani, D. (2022). “Does the social assistance budget realization affect incumbents’ votes? (Study in Indonesia Local Election)”, International Journal of Professional Business Review, 7(6), e0636-e0636.
- Gutbrod, H. (2024). “A dozen daggers: How Georgia’s 2024 elections were rigged”.
- Japaridze, T. (2023). “Social Policy in contemporary Georgia: liberal narratives, intervention and welfare state”, King’s College London.
- Law of Georgia on Social Assistance (2024). Parliament of Georgia.
- Layton, M. L., & Smith, A. E. (2015). “Incorporating marginal citizens and voters: the conditional electoral effects of targeted social assistance in Latin America”, Comparative Political Studies, 48(7), 854-881
- OSCE Office for Democratic Institutions and Human Rights (ODIHR), (2024). “Georgia Parliamentary Elections: Final Report”, Organization for Security and Co-operation in Europe.
- Shubladze, R. (2024). “Targeted Social Assistance Program in Georgia and Its Link to Electoral Outcomes”, Social Justice Center.
- Social Justice Center (2024). “What are the challenges in the livelihood support system, and what changes are necessary for its improvement?”.
- Social Service Agency of Georgia. (2024). https://ssa.moh.gov.ge/index.php?lang=1&v=0
- Zucco Jr, C. (2013). “When payouts pay off: Conditional cash transfers and voting behavior in Brazil 2002–10”, American journal of political science, 57(4), 810-822.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Behavior and Information: Does Media Promote Consumerism?
Consumer behavior is well recognized as a vital component in dealing with climate change. In this regard, it is important to understand both which mechanisms promote pro-environmental behavior, and which instruments stimulate unsustainable consumer activities. This policy brief summarizes the results from a study on how media use can promote consumerism. Based on a 2022 online-survey of Belarus’s urban population, the study empirically assesses how exposure to information promoting overconsumption can impact unsustainable actions. The findings show that consumerism media use has a positive effect on unsustainable consumption behavior. To mitigate the impact and promote sustainable behavior, media could be obligated to provide information about the negative footprint of unsustainable consumption.
Introduction
Consumer behavior holds large potential when it comes to climate change and other environmental problems. According to Moran et al. (2020), changes in consumer behavior could lead to a European Union (EU) carbon footprint reduction by approximately 25 percent.
There are two conflicting streams of literature on the effects of media use on consumer behavior. The first strand states that media use exerts a positive effect on pro-environmental attitudes and behavior (Holbert et al. 2003; Wang & Hao, 2018) while the second declares that media use (in particular, the Internet) promotes consumerism (Simeone & Scarpato, 2020).
The objective of the study underlying this policy brief is to contribute to this debate by exploring whether media use positively affects unsustainable consumption behavior, drawing on data from a nationally representative online survey in Belarus.
Behavior and its Determinants
The study’s conceptual approach rests on the Attitude-Behavior-Context (ABC) theory (Guagnano et al., 1995; Gardner and Stern, 1996; Stern, 2000) which states that behavior is a product of attitudinal variables (norms, beliefs, values), contextual factors (e.g., interpersonal influences, media, community expectations, monetary incentives and costs) and personal capabilities (e.g., knowledge and skills).
With the ABC theory in mind, and also driven by prior empirical studies (e.g., Huang, 2016), the study explores how unsustainable consumption behavior can be affected by materialistic values, environmental self-efficacy (in the study perceived as a combination of values and personal knowledge), and consumerism media use.
We define unsustainable consumption behavior as conspicuous buying, which describes acquiring expensive, and luxury goods or services in order to impress others and gather prestige through objects (Rook, 1987; Pellegrino & Shannon, 2021).
Media use in general means exposure or attention to both traditional media, such as newspapers, TV, and radio, and the Internet (Huang, 2016). Consumerism media use in our study refers to the exposure on these media channels to information promoting a luxurious lifestyle and the idea that buying more leads to happiness.
According to Hurst et al. (2013), materialism can be more easily targeted and changed than personality traits, which are more stable. Besides, theoretical and empirical evidence suggests that materialistic values are negatively associated with pro-environmental behavior. To measure materialism as a value we employ the short version of the Materialistic Values Scale (Richins, 2004), which assesses beliefs about the importance of material possession.
Environmental self-efficacy, also known as perceived consumer effectiveness, refers to an individual’s belief in their ability to make a meaningful impact through their efforts (Ellen et al., 1991). We hypothesize that environmental self-efficacy should be negatively associated with unsustainable consumption behavior.
To operationalize the above constructs (see Table 1), the study uses data from a nationally representative online survey among the urban Belarusian population aged 18-75, conducted in April 2022 by MIA Research on behalf of BEROC. The sample size includes 1029 participants.
Table 1. Descriptive statistics of each construct’s indicators
Note: a Four-point Likert scale (1=never, 4=always). b Four-point Likert scale (1=never; 4=very often; 0=I do not use this type of media). c Five-point Likert scale (1=strongly disagree, 5=strongly agree).
As seen in Table 1, consumers in Belarus are mostly exposed to the information promoting luxurious lifestyle and buying more goods to be happy on the Internet, relative to other media channels. Another interesting outcome is that Belarusian consumers are more likely to perceive material possessions as a source of happiness compared to the other domains of the classical material value triad; success, centrality, and happiness (Richins and Dawson (1992) and Richins (2004), where success refers to using possessions to evaluate the success of oneself and others centrality refers to the central role of possessions in a person’s life, and happiness reflects the belief that happiness and life satisfaction are achieved through possessions and their acquisition.
Assessment of the Unsustainable Consumption Behavior Model
The study estimates the structural equation model for unsustainable consumption behavior. The main hypothesis of the study is that consumerism media use might exert a positive influence on unsustainable consumption behavior. Materialistic values as well as environmental self-efficacy can also affect unsustainable consumption behavior. As both our values and beliefs may to some extent determine the context in which we live, we assume that materialistic values and environmental self-efficacy might impact consumerism media use. Additionally, we assume that materialistic values can have a negative influence on environmental self-efficacy. Figure 1 details the path diagram with maximum-likelihood estimates of fully standardized coefficients.
Figure 1. Path diagram of the structural equation model explaining unsustainable consumption behavior
Note: standardized coefficients; solid line denotes significant path; dashed line denotes insignificant relationships. *** p<0.001, ** p<0.01; *p<0.05
The results show that consumerism media use has a positive, and significant effect on unsustainable consumption behavior (0.124; standard deviation change). The possible channel leading to these findings is the emotions at play. Advertisements promoting a luxurious lifestyle and buying more things to be happy can elicit quite strong emotions in consumers related to happiness and success in life. Around two decades ago a large body of literature in consumer research emerged on the role of emotions in decision-making (for an overview see Laros & Steenkamp, 2005). Recent experimental studies about adoption of sustainable innovations (e.g. Contzen et al., 2021 (a); Contzen et al., 2021 (b)) also prove the role emotions play in consumer behavior.
Materialistic values are another significant contributor to unsustainable actions (0.249 standard deviation change). As expected, materialistic values also exert a positive and statistically significant effect on consumerism media use (0.165 standard deviation change). However, contrary to our expectations, environmental self-efficacy does not exert a direct negative impact on unsustainable behavior (dashed line in Figure 1).
Conclusion
The results from the structural equation model show that consumerism media use exerts a positive moderate effect on unsustainable consumption behavior of the urban population in Belarus. This effect is statistically significant.
To reduce the negative environmental impact of unsustainable behavior, policymakers should, thus, target regulation that downplays the emotional appeal of ads promoting excessive consumption and stresses the adverse environmental effects of consumerism. This could include, for example, policies requiring ads to contain information about the environmental footprint of the product, from production to its full lifecycle.
References
- Contzen, N., Handreke, A. V., Perlaviciute, G., & Steg, L., 2021 (a). ‘’Emotions towards a mandatory adoption of renewable energy innovations: the role of psychological reactance and egoistic and biospheric values’’. Energy Research & Social Science, 80, 102232. https://doi.org/10.1016/j.erss.2021.102232.
- Contzen, N., Perlaviciute, G., Sadat-Razavi, P., & Steg, L., 2021 (b). ‘’Emotions toward sustainable innovations: A matter of value congruence’’. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.661314
- Ellen, P. S., Wiener, J. L., & Cobb-Walgren, C., 1991. ‘’The role of perceived consumer effectiveness in motivating environmentally conscious behaviors”. Journal of public policy & marketing, 10(2), 102-117. https://doi.org/10.1177/074391569101000206.
- Gardner, G. T., & Stern, P. C., 1996. “Environmental problems and human behavior”. Allyn & Bacon.
- Guagnano, G. A., Stern, P. C., & Dietz, T., 1995. “Influences on attitude-behavior relationships: A natural experiment with curbside recycling”. Environment and behavior, 27(5), 699-718. https://doi.org/10.1177/0013916595275005
- Holbert, R. L., Kwak, N., & Shah, D. V. (2003). Environmental concern, patterns of television viewing, and pro-environmental behaviors: Integrating models of media consumption and effects. Journal of Broadcasting & Electronic Media, 47(2), 177-196. https://doi.org/10.1207/s15506878jobem4702_2.
- Huang, H., 2016. “Media use, environmental beliefs, self-efficacy, and pro-environmental behavior”. Journal of Business Research, 69(6), 2206-2212. https://doi.org/10.1016/j.jbusres.2015.12.031.
- Hurst, M., Dittmar, H., Bond, R., & Kasser, T., 2013. “The relationship between materialistic values and environmental attitudes and behaviors: A meta-analysis”. Journal of Environmental Psychology, 36, 257-269. https://doi.org/10.1016/j.jenvp.2013.09.003.
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Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Gender Gap in Life Expectancy and Its Socio-Economic Implications
Today women live longer than men virtually in every country of the world. Although scientists still struggle to fully explain this disparity, the most prominent sources of this gender inequality are biological and behavioral. From an evolutionary point of view, female longevity was more advantageous for offspring survival. This resulted in a higher frequency of non-fatal diseases among women and in a later onset of fatal conditions. The observed high variation in the longevity gap across countries, however, points towards an important role of social and behavioral arguments. These include higher consumption of alcohol, tobacco, and fats among men as well as a generally riskier behavior. The gender gap in life expectancy often reaches 6-12 percent of the average human lifespan and has remained stubbornly stable in many countries. Lower life expectancy among men is an important social concern on its own and has significant consequences for the well-being of their surviving partners and the economy as a whole. It is an important, yet under-discussed type of gender inequality.
Country Reports
| Belarus Country Report | FROGEE POLICY BRIEF |
| Georgia Country Report | FROGEE POLICY BRIEF |
| Latvia Country Report | FROGEE POLICY BRIEF |
| Poland Country Report | FROGEE POLICY BRIEF |
Gender Gap in Life Expectancy and Its Socio-Economic Implications
Today, women on average live longer than men across the globe. Despite the universality of this basic qualitative fact, the gender gap in life expectancy (GGLE) varies a lot across countries (as well as over time) and scientists have only a limited understanding of the causes of this variation (Rochelle et al., 2015). Regardless of the reasons for this discrepancy, it has sizable economic and financial implications. Abnormal male mortality makes a dent in the labour force in nations where GGLE happens to be the highest, while at the same time, large GGLE might contribute to a divergence in male and female discount factors with implications for employment and pension savings. Large discrepancies in life expectancy translate into a higher incidence of widowhood and a longer time in which women live as widows. The gender gap in life expectancy is one of the less frequently discussed dimensions of gender inequality, and while it clearly has negative implications for men, lower male longevity has also substantial negative consequences for women and society as a whole.
Figure A. Gender gap in life expectancy across selected countries
Source: World Bank.
The earliest available reliable data on the relative longevity of men and women shows that the gender gap in life expectancy is not a new phenomenon. In the middle of the 19th century, women in Scandinavian countries outlived men by 3-5 years (Rochelle et al., 2015), and Bavarian nuns enjoyed an additional 1.1 years of life, relative to the monks (Luy, 2003). At the beginning of the 20th century, relative higher female longevity became universal as women started to live longer than men in almost every country (Barford et al., 2006). GGLE appears to be a complex phenomenon with no single factor able to fully explain it. Scientists from various fields such as anthropology, evolutionary biology, genetics, medical science, and economics have made numerous attempts to study the mechanisms behind this gender disparity. Their discoveries typically fall into one of two groups: biological and behavioural. Noteworthy, GGLE seems to be fairly unrelated to the basic economic fundamentals such as GDP per capita which in turn has a strong association with the level of healthcare, overall life expectancy, and human development index (Rochelle et al., 2015). Figure B presents the (lack of) association between GDP per capita and GGLE in a cross-section of countries. The data shows large heterogeneity, especially at low-income levels, and virtually no association from middle-level GDP per capita onwards.
Figure B. Association between gender gap in life expectancy and GDP per capita
Source: World Bank.
Biological Factors
The main intuition behind female superior longevity provided by evolutionary biologists is based on the idea that the offspring’s survival rates disproportionally benefited from the presence of their mothers and grandmothers. The female hormone estrogen is known to lower the risks of cardiovascular disease. Women also have a better immune system which helps them avoid a number of life-threatening diseases, while also making them more likely to suffer from (non-fatal) autoimmune diseases (Schünemann et al., 2017). The basic genetic advantage of females comes from the mere fact of them having two X chromosomes and thus avoiding a number of diseases stemming from Y chromosome defects (Holden, 1987; Austad, 2006; Oksuzyan et al., 2008).
Despite a number of biological factors contributing to female longevity, it is well known that, on average, women have poorer health than men at the same age. This counterintuitive phenomenon is called the morbidity-mortality paradox (Kulminski et al., 2008). Figure C shows the estimated cumulative health deficits for both genders and their average life expectancies in the Canadian population, based on a study by Schünemann et al. (2017). It shows that at any age, women tend to have poorer health yet lower mortality rates than men. This paradox can be explained by two factors: women tend to suffer more from non-fatal diseases, and the onset of fatal diseases occurs later in life for women compared to men.
Figure C. Health deficits and life expectancy for Canadian men and women
Source: Schünemann et al. (2017). Note: Men: solid line; Women: dashed line; Circles: life expectancy at age 20.
Behavioural Factors
Given the large variation in GGLE, biological factors clearly cannot be the only driving force. Worldwide, men are three times more likely to die from road traffic injuries and two times more likely to drown than women (WHO, 2002). According to the World Health Organization (WHO), the average ratio of male-to-female completed suicides among the 183 surveyed countries is 3.78 (WHO, 2024). Schünemann et al. (2017) find that differences in behaviour can explain 3.2 out of 4.6 years of GGLE observed on average in developed countries. Statistics clearly show that men engage in unhealthy behaviours such as smoking and alcohol consumption much more often than women (Rochelle et al., 2015). Men are also more likely to be obese. Alcohol consumption plays a special role among behavioural contributors to the GGLE. A study based on data from 30 European countries found that alcohol consumption accounted for 10 to 20 percent of GGLE in Western Europe and for 20 to 30 percent in Eastern Europe (McCartney et al., 2011). Another group of authors has focused their research on Central and Eastern European countries between 1965 and 2012. They have estimated that throughout that time period between 15 and 19 percent of the GGLE can be attributed to alcohol (Trias-Llimós & Janssen, 2018). On the other hand, tobacco is estimated to be responsible for up to 30 percent and 20 percent of the gender gap in mortality in Eastern Europe and the rest of Europe, respectively (McCartney et al., 2011).
Another factor potentially decreasing male longevity is participation in risk-taking activities stemming from extreme events such as wars and military activities, high-risk jobs, and seemingly unnecessary health-hazardous actions. However, to the best of our knowledge, there is no rigorous research quantifying the contribution of these factors to the reduced male longevity. It is also plausible that the relative importance of these factors varies substantially by country and historical period.
Gender inequality and social gender norms also negatively affect men. Although women suffer from depression more frequently than men (Albert, 2015; Kuehner, 2017), it is men who commit most suicides. One study finds that men with lower masculinity (measured with a range of questions on social norms and gender role orientation) are less likely to suffer from coronary heart disease (Hunt et al., 2007). Finally, evidence shows that men are less likely to utilize medical care when facing the same health conditions as women and that they are also less likely to conduct regular medical check-ups (Trias-Llimós & Janssen, 2018).
It is possible to hypothesize that behavioural factors of premature male deaths may also be seen as biological ones with, for example, risky behaviour being somehow coded in male DNA. But this hypothesis may have only very limited truth to it as we observe how male longevity and GGLE vary between countries and even within countries over relatively short periods of time.
Economic Implications
Premature male mortality decreases the total labour force of one of the world leaders in GGLE, Belarus, by at least 4 percent (author’s own calculation, based on WHO data). Similar numbers for other developed nations range from 1 to 3 percent. Premature mortality, on average, costs European countries 1.2 percent of GDP, with 70 percent of these losses attributable to male excess mortality. If male premature mortality could be avoided, Sweden would gain 0.3 percent of GDP, Poland would gain 1.7 percent of GDP, while Latvia and Lithuania – countries with the highest GGLE in the EU – would each gain around 2.3 percent of GDP (Łyszczarz, 2019). Large disparities in the expected longevity also mean that women should anticipate longer post-retirement lives. Combined with the gender employment and pay gap, this implies that either women need to devote a larger percentage of their earnings to retirement savings or retirement systems need to include provisions to secure material support for surviving spouses. Since in most of the retirement systems the value of pensions is calculated using average, not gender-specific, life expectancy, the ensuing differences may result in a perception that men are not getting their fair share from accumulated contributions.
Policy Recommendations
To successfully limit the extent of the GGLE and to effectively address its consequences, more research is needed in the area of differential gender mortality. In the medical research dimension, it is noteworthy that, historically, women have been under-represented in recruitment into clinical trials, reporting of gender-disaggregated data in research has been low, and a larger amount of research funding has been allocated to “male diseases” (Holdcroft, 2007; Mirin, 2021). At the same time, the missing link research-wise is the peculiar discrepancy between a likely better understanding of male body and health and the poorer utilization of this knowledge.
The existing literature suggests several possible interventions that may substantially reduce premature male mortality. Among the top preventable behavioural factors are smoking and excessive alcohol consumption. Many studies point out substantial country differences in the contribution of these two factors to GGLE (McCartney, 2011), which might indicate that gender differences in alcohol and nicotine abuse may be amplified by the prevailing gender roles in a given society (Wilsnack et al., 2000). Since the other key factors impairing male longevity are stress and risky behaviour, it seems that a broader societal change away from the traditional gender norms is needed. As country differences in GGLE suggest, higher male mortality is mainly driven by behaviours often influenced by societies and policies. This gives hope that higher male mortality could be reduced as we move towards greater gender equality, and give more support to risk-reducing policies.
While the fundamental biological differences contributing to the GGLE cannot be changed, special attention should be devoted to improving healthcare utilization among men and to increasingly including the effects of sex and gender in medical research on health and disease (Holdcoft, 2007; Mirin, 2021; McGregor et al., 2016, Regitz-Zagrosek & Seeland, 2012).
References
- Albert, P. R. (2015). “Why is depression more prevalent in women?“. Journal of Psychiatry & Neuroscience, 40(4), 219.
- Austad, S. N. (2006). “Why women live longer than men: sex differences in longevity“. Gender Medicine, 3(2), 79-92.
- Barford, A., Dorling, D., Smith, G. D., & Shaw, M. (2006). “Life expectancy: women now on top everywhere“. BMJ, 332, 808. doi:10.1136/bmj.332.7545.808
- Holden, C. (1987). “Why do women live longer than men?“. Science, 238(4824), 158-160.
- Hunt, K., Lewars, H., Emslie, C., & Batty, G. D. (2007). “Decreased risk of death from coronary heart disease amongst men with higher ‘femininity’ scores: A general population cohort study“. International Journal of Epidemiology, 36, 612-620.
- Kulminski, A. M., Culminskaya, I. V., Ukraintseva, S. V., Arbeev, K. G., Land, K. C., & Yashin, A. I. (2008). “Sex-specific health deterioration and mortality: The morbidity-mortality paradox over age and time“. Experimental Gerontology, 43(12), 1052-1057.
- Luy, M. (2003). “Causes of Male Excess Mortality: Insights from Cloistered Populations“. Population and Development Review, 29(4), 647-676.
- McCartney, G., Mahmood, L., Leyland, A. H., Batty, G. D., & Hunt, K. (2011). “Contribution of smoking-related and alcohol-related deaths to the gender gap in mortality: Evidence from 30 European countries“. Tobacco Control, 20, 166-168.
- McGregor, A. J., Hasnain, M., Sandberg, K., Morrison, M. F., Berlin, M., & Trott, J. (2016). “How to study the impact of sex and gender in medical research: A review of resources“. Biology of Sex Differences, 7, 61-72.
- Mirin, A. A. (2021). “Gender disparity in the funding of diseases by the US National Institutes of Health“. Journal of Women’s Health, 30(7), 956-963.
- Oksuzyan, A., Juel, K., Vaupel, J. W., & Christensen, K. (2008). “Men: good health and high mortality. Sex differences in health and aging“. Aging Clinical and Experimental Research, 20(2), 91-102.
- Regitz-Zagrosek, V., & Seeland, U. (2012). “Sex and gender differences in clinical medicine“. Sex and Gender Differences in Pharmacology, 3-22.
- Rochelle, T. R., Yeung, D. K. Y., Harris Bond, M., & Li, L. M. W. (2015). “Predictors of the gender gap in life expectancy across 54 nations“. Psychology, Health & Medicine, 20(2), 129-138. doi:10.1080/13548506.2014.936884
- Schünemann, J., Strulik, H., & Trimborn, T. (2017). “The gender gap in mortality: How much is explained by behavior?“. Journal of Health Economics, 54, 79-90.
- Trias-Llimós, S., & Janssen, F. (2018). “Alcohol and gender gaps in life expectancy in eight Central and Eastern European countries“. European Journal of Public Health, 28(4), 687-692.
- WHO. (2002). “Gender and road traffic injuries“. World Health Organization.
- WHO. (2024). “Global health estimates: Leading causes of death“. World Health Organization.
- Łyszczarz, B. (2019). “Production losses associated with premature mortality in 28 European Union countries“. Journal of Global Health.
About FROGEE Policy Briefs
FROGEE Policy Briefs is a special series aimed at providing overviews and the popularization of economic research related to gender equality issues. Debates around policies related to gender equality are often highly politicized. We believe that using arguments derived from the most up to date research-based knowledge would help us build a more fruitful discussion of policy proposals and in the end achieve better outcomes.
The aim of the briefs is to improve the understanding of research-based arguments and their implications, by covering the key theories and the most important findings in areas of special interest to the current debate. The briefs start with short general overviews of a given theme, which are followed by a presentation of country-specific contexts, specific policy challenges, implemented reforms and a discussion of other policy options.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.