Tag: Ukraine
OECD DevTalks: The Transformation and Reconstruction of Ukraine
The war in Ukraine, caused by Russia’s invasion, remains a profound humanitarian crisis with far-reaching economic and social consequences worldwide. In response, the Organisation for Economic Co-operation and Development (OECD) has strongly condemned Russia’s actions. Moreover, it is now advancing a new strategy to strengthen Ukraine’s recovery and reconstruction efforts.
The OECD’s work builds on a Memorandum of Understanding first signed with Ukraine in 2014 and renewed in 2021. Since then, the organisation has deepened its collaboration with Ukrainian partners to rebuild the nation’s economy and institutions. In addition, the OECD Development Centre plays a crucial role by providing policy expertise and data-driven analysis. It supports multiple sectors, including governance, innovation, and sustainable growth. As a result, these coordinated efforts aim to help Ukraine achieve long-term stability and resilience.
Webinar on Ukraine’s Economic and Social Transformation
On Tuesday, 17 May 2022, the OECD DevTalks series hosted a high-level webinar focusing on Ukraine’s economic and social transformation, both before and after the full-scale invasion. The event gathered leading economists, policymakers, and development experts to discuss:
- The state of Ukraine’s economy prior to 2022
- The impact of the war on social and economic structures
- Priorities for reconstruction and recovery
- The role of international support and cooperation
This discussion contributed to shaping a shared vision for Ukraine’s future, highlighting the resilience of its people and institutions amid ongoing challenges.
Distinguished Speakers
- Mathias Cormann, Secretary-General, OECD
- Vadym Omelchenko, Extraordinary and Plenipotentiary Ambassador of Ukraine to France
- Yuriy Gorodnichenko, Quantedge Presidential Professor of Economics, University of California, Berkeley
- Nataliia Shapoval, Head of KSE Institute & Vice President for Policy Research, Kyiv School of Economics
- Tymofii Brik, Acting Wartime Vice-President of International Affairs & Head of Sociological Research, Kyiv School of Economics
- Torbjörn Becker, Director, Stockholm Institute of Transition Economics (SITE), Stockholm School of Economics
- William Tompson, Head of Eurasia, Global Relations and Co-operation, OECD
- Ragnheidur Elín Árnadóttir, Director, OECD Development Centre
About OECD DevTalks
OECD DevTalks is a continuing series of expert panel discussions and blogs organized by the OECD Development Centre. Each session brings together global thought leaders to exchange ideas on sustainable development, inclusive growth, and policy innovation. For more #DevTalks – a series of online panel discussions, along with Development Matters blogs, follow the OECD #DevTalk page.
Disclaimer: Opinions expressed during events and conferences are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
The Effects of Sanctions
Sanctions imposed on Russia after its invasion of Ukraine are argued to be the strongest and farthest-reaching imposed on a major power after WWII, more numerous and more comprehensive than all other measures currently in force against all other sanctioned countries. A question often asked, which is hard to answer, is whether sanctions are effective. In the present case, the effect most associate with success would be a swift end of the hostilities, perhaps accompanied by a regime change in Russia. But even when it seems these prizes are out of reach, sanctions certainly have effects, all too often glossed over by the debate but nonetheless of significance.
Why Are Sanctions Seen as Ineffective?
Sanctions are restrictions imposed on a country by one or more other countries with the intent to pressure in effect some desirable outcome, or conversely to condemn and punish some undesired action already taken. When evaluating sanctions, therefore, the focus is naturally on whether they succeed to discourage this particular course of action, or to remove the decision-makers responsible for it. And on this account, sanctions are overwhelmingly seen as unsuccessful. However, a few complications cloud this conclusion.
First of all, sanctions that are implemented already failed at the threat stage. If the threat of a well-specified and credible retribution did not deter the receiving part from pursuing the sanctioned course of action, it is because they reckoned that they can afford to ignore it. So, unless this punishment goes beyond what was expected, in scope or in time, its implementation will also fall flat. This implies that any effort to evaluate sanctions retrospectively suffers from the negative selection problem, when almost exclusively cases of failure, intended in this particular sense, are observed.
Second, sanctions are a rather blunt instrument, that often cannot be targeted with the precision one would desire. Even though sanctions have over time become “smarter”, in the sense that stronger efforts are made to target the regime, or elites that may have the clout to actually affect the regime (think the oligarchs in Russia), they often fail to reach or affect in a meaningful way those individuals that are the real objective, for various reasons. Instead, they can cause significant “collateral damage”, to groups of a population that often are quite far removed from any real decisional power, including those in the sending countries, and even third parties who are extraneous to the situation. The damage inflicted to those parties can only in very special circumstances be part of a causal link eventually impacting the intended outcome. For instance, citizens struggling in an impoverished economy could be led to a riot, or in some other way put pressure on their government – but this implies that the country is sufficiently free for riots to take place or for voters’ opinions to be taken into consideration.
To this, it should be added that, once a course of action has been taken, it might be not obvious how to change or undo it, notwithstanding the signaled displeasure from the sanctioning parties. Sanctions are therefore rarely working in isolation. When positive outcomes are achieved, it is often the case that diplomatic channels were kept open and clear incentives offered for a way out. But then it might be unclear whether it was the sanctions or something else that led to the success.
Other Effects of Sanctions
The pitfalls highlighted above, which make it tricky to answer whether sanctions are effective at reaching their aim, also apply when studying other effects that sanctions might have. There is of course a range of outcomes that might be affected: in this literature we find studies looking at inequality (Afesorgbor et al., 2016), exchange rates (Dreger et al., 2016), trade (Afesorgbor, 2019; Crozet et al. 2020), the informal sector (Early et al, 2019), military spending (Farzanegan, 2019), women’s rights (Drury, 2014), and many more. But as it often happens the most studied outcome is GDP, as this is a measure that efficiently summarizes the whole economy and correlates very nicely with many other outcomes we care about.
Suppose then that we would like to investigate what is the effect of sanctions on a target country’s GDP. One problem is identifying an appropriate counterfactual; to observe what would have happened in the target country in the absence of sanctions. It is also an issue that the incidence of international sanctions is often a product of a series of events in the target or sender country (e.g. the Iraqi invasion of Kuwait or the apartheid system in South Africa), which also have impacts on the economy that would need to be isolated from the impact of sanctions themselves.
A variety of econometric techniques can be of help in this situation. One first idea is to use, as a reference, cases where sanctions were almost implemented. Gutmann et al. (2021) compare countries under sanctions to countries under threat of sanctions, while Neuenkirch and Neumeier (2015) contrast implemented sanctions to vetoed sanctions, in the context of UN decisions. Both studies find a relatively sizeable negative impact on GDP, in a large group of countries over a long period of time. In the first study, the target country’s GDP per capita decreases on average by 4 percent over the two first years after sanctions imposition and shows no signs of recovery in the three years after sanctions are removed. The second study estimates a reduction in GDP growth that starts at between 2,3 and 3,5 percent after the imposition of UN sanctions and, although it decreases over time, only becomes insignificant after ten years. It should be considered that a lower growth rate compounds over time: experiencing a slower growth even by only 1 percent over ten years implies a total loss of almost 15 percent. As a comparison, the average GDP loss due to the Covid-19 pandemic is estimated to be 3,4 percent in 2020.
These studies have limitations. Countries under threat of sanctions are probably making efforts to avoid punishment, which might imply that these countries are precisely the ones who would be most negatively affected by the sanctions. If so, the impact found by Gutmann et al. (2021) is probably underestimated. Neuenkirch and Neumeier (2015) only look at UN sanctions, which on one hand might give a larger impact because of the multilateral coordination. But on the other hand, the issue of an appropriate counterfactual emerges again: countries whose sanctions are vetoed might be larger, more influential, and better connected within the international community or to some of the major powers, which may also affect their economic success in other ways.
Kwon et al. (2020) adopt a different technique and come to a different conclusion. They use an instrumental variable (IV) approach and find that standard OLS overestimates the negative effect of sanctions, in other words, that sanctions’ effects are less negative than we think. They find an instantaneous effect on per capita GDP that becomes insignificant in the long run, just as if sanctions never happened.
Our confidence in these estimates hinges upon the validity of the IV used. In this case, the actual imposition of sanctions is replaced by its estimated likelihood based on sender countries’ variation in institutions and diplomatic policies (which are exogenous to the target country’s economic developments) and pre-determined country-pair characteristics (trade and financial flows, travels, colonial ties). Therefore, episodes where sanctions are imposed because the sender country happens to be in a period of hawkish foreign policy and because the target does not have strong historical relations with them are contrasted to episodes in which the opposite is true, and sanctions are therefore not implemented, everything else being equal.
The results also show that there is heterogeneity across types of sanctions, with trade sanctions having both a short and long run negative impact, while smart sanctions (i.e. sanctions targeted on particular individuals or groups) have positive effects on the target country’s economy in the long run. This is quite an important point in itself. Often, sweeping statements about effectiveness of “sanctions” lump all the different measures together, and fail to appreciate that there may be substantial differences. However, the effect of one or another type of sanctions will vary depending on the structure of the economy that is hit.
A third approach is the synthetic control method. Here the researcher tries to replicate as closely as possible the path of economic development in the target country up to the point of sanctions’ implementation, using one or a weighted average of several other countries. In this way, evolution after sanctions’ inception can be compared between the actual country and its synthetic control. Gharehgozli (2017) builds a replica of Iran based on a weighted combination of eight OPEC member countries, two non-OPEC oil-producing countries and three neighboring countries, that match a set of standard economic indicators for Iran over the period 1980-1994. The study finds that over the course of three years the imposition of US sanctions led to a 17.3 percent decline in Iran’s GDP, with the strongest reduction occurring in 2012, one year after the intensification of sanctions (2011-2014) was initiated.
This is a stronger effect than those presented earlier. However, it only speaks to the special case of Iran, rather than estimating a broader global average effect. Another study focusing on Iran (Torbat, 2005) makes the important point that the effect of sanctions varies by type: financial sanctions are found to be more effective (in lowering Iran’s GDP) than trade sanctions – which contrasts with what is found to be true on average by Kwon et al. (2020).
Finally, the relation between economic damage and the effectiveness of sanctions in terms of reaching their goals is debatable. In a theoretical model, Kaempfer et al. (1988) suggest that this relation might even be negative and that the most effective sanctions are not necessarily the most damaging in economic terms. The sanctions most likely to facilitate political change in the target country are those designed to cause income losses on groups benefiting from the target country’s policies, according to the authors.
The Effect of Sanctions on Russia
Are these results from previous studies useful to form expectations about the effects of the current sanctions on Russia? The invasion of Ukraine which started at the end of February was a relatively unexpected event, at least in character and scale, in contrast to what can be said in the majority of situations involving sanctions. However, the context leading up to it was not one of normality either. Besides the global pandemic, Russia was already under sanctions following the Crimean Crisis in 2014. The impact of those economic sanctions, and of the counter-sanctions imposed by Russia as retaliation, is still unclear – and will be in all probability completely dwarfed by the current sanction wave as well as other exogenous shocks, such as significant changes in oil prices in this period. Kholodilin et al. (2016) estimated the immediate loss of GDP in Russia to be 1,97 percent quarter-on-quarter, while no impact on the aggregate Euro Area countries’ GDP could be observed. A Russian study (Gurvich and Prilepsky, 2016) forecasted for the medium term a loss of 2,4 percentage points by 2017 as compared to the hypothetical scenario without sanctions. This pales in comparison to the magnitude of consequences that are being contemplated now. Even the potentially optimistic, or at least conservative, assessment of the current situation by the Russian Federation’s own Accounts Chamber, in the words of its head Alexei Kudrin, suggests that: “For almost one and a half to two years we will live in a very difficult situation.” At the end of April, they published revised forecasts on the economic situation, among which the one for GDP is shown below. Russian Central Bank chief Elvira Nabiullina also sounded bleak, speaking in the State Duma: “The period when the economy can live on reserves is finite. And already in the second – the beginning of the third quarter, we will enter a period of structural transformation and the search for new business models.” The World Bank has forecasted that Russia’s 2022 GDP output will fall by 11.2% due to Western sanctions. These numbers do not yet factor in the announcement of the sixth EU sanction package, which famously includes an oil embargo (see an earlier FREE Policy Brief on the dependency of Russia on oil export).
Figure 1. Revised forecasts of growth rates for the Russian economy

Source: Macroeconomic survey of the Bank of Russia, April 2022.
Are these estimates realistic, and what would have been the counterfactual development without sanctions? If we believe the studies reviewed in the previous section, and also taking into account the unprecedented scale and reach of the current sanctions, at least the time horizon, if not the size, of the consequences forecast by Russian authorities is, though substantial, certainly underestimated. But there is too much uncertainty at the moment, hostilities are still ongoing and sanctions are not being lifted for quite some time in any foreseeable scenario. One reason why these sanctions are not likely to be relaxed, and why their impact is expected to be more severe than in most cases, is that a very broad coalition of countries is backing them. Not only this but the sanctioning countries see Russia’s conduct as a potential threat to the existing world order, so their motivation to contrast it is particularly strong relative to, say, the cases of Iran, North Korea, or Burma.
Moreover, these loss estimates do not yet factor in the announcement of the sixth EU sanction package, which famously includes an oil embargo. Oil is a fundamental driver of growth in Russia. An earlier FREE Policy Brief shows how two-thirds of Russia’s growth can be explained by changes in international oil prices. This is not because oil constitutes such a large share of GDP but because of the secondary effect oil money generates in terms of domestic consumption and investment. Reducing export revenues from the sale of oil and gas will therefore have significant effects on Russia’s GDP, well beyond what the first-round effect of restricting the oil sector would imply.
In short, it is too early to venture an assessment in detail, however, the scale of loss that can be expected is clear from these and many other indicators. In the longer run, it will only be augmented by the relative isolation in which Russia has ended up, implying lower investments and subpar capital inputs at inflated prices, and by the ongoing brain drain (3,8 million people have already left the country since the war began).
Conclusion
In conclusion, the debate about economic sanctions as a tool of foreign policy is often restricted to a binary question: do they work or not? There is ample support in the literature studying sanctions to say that this question is too simplistic. Even if we do not see immediate success in reaching the main aim of the sanction policy, they do cause damage, in many dimensions, and such damage is non-negligible. The political will and the regime behind it may be unaffected, but the resources they need to continue with their course of action will unavoidably shrink in the longer run.
References
- Afesorgbor, S. K. (2019). The impact of economic sanctions on international trade: How do threatened sanctions compare with imposed sanctions?. European Journal of Political Economy, 56, 11-26.
- Afesorgbor, S. K., & Mahadevan, R. (2016). The impact of economic sanctions on income inequality of target states. World Development, 83, 1-11.
- Crozet, M., & Hinz, J. (2020). Friendly fire: The trade impact of the Russia sanctions and counter-sanctions. Economic Policy, 35(101), 97-146.
- Dreger, C., Kholodilin, K. A., Ulbricht, D., & Fidrmuc, J. (2016). Between the hammer and the anvil: The impact of economic sanctions and oil prices on Russia’s ruble. Journal of Comparative Economics, 44(2), 295-308.
- Drury, A. Cooper and Dursun Peksen. “Women and economic statecraft: The negative impact international economic sanctions visit on women.” European Journal of International Relations 20 (2014): 463 – 490.
- Early, B., & Peksen, D. (2019). Searching in the shadows: The impact of economic sanctions on informal economies. Political Research Quarterly, 72(4), 821-834.
- Farzanegan, Mohammad Reza. (2019). “The Effects of International Sanctions on Military Spending of Iran: A Synthetic Control Analysis.” Organizations & Markets: Policies & Processes eJournal .
- Gharehgozli, O. (2017). An estimation of the economic cost of recent sanctions on Iran using the synthetic control method. Economics Letters, 157, 141-144.
- Gurvich E., Prilepskiy I. (2016). The impact of financial sanctions on the Russian economy. Voprosy Ekonomiki. ;(1):5-35. (In Russ.) https://doi.org/10.32609/0042-8736-2016-1-5-35
- Gutmann, J., Neuenkirch, M., and Neumeier, F., 2021. ”The Economic Effects of International Sanctions: An Event Study” CESifo Working Paper No. 9007
- Kaempfer, W. H., & Lowenberg, A. D. (1988). The theory of international economic sanctions: A public choice approach. The American Economic Review, 78(4), 786-793.
- Kholodilin, Konstantin A. and Netsunajev, Aleksei. (2016) Crimea and Punishment: The Impact of Sanctions on Russian and European Economies. DIW Berlin Discussion Paper No. 1569, SSRN: https://ssrn.com/abstract=2768622
- Kwon, O., Syropoulos, C., & Yotov, Y. V. (2020). Pain and Gain: The Short-and Long-run Effects of Economic Sanctions on Growth. Manuscript.
- Neuenkirch, M., & Neumeier, F. (2015). The impact of UN and US economic sanctions on GDP growth. European Journal of Political Economy, 40, 110-125.
- Torbat, A. E. (2005). Impacts of the US trade and financial sanctions on Iran. World Economy, 28(3), 407-434.
- World Bank. (2022). “War in the Region” Europe and Central Asia Economic Update (Spring), Washington, DC: World Bank.
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.
Financial Aid to Ukrainian Reconstruction: Loans Versus Grants
This brief provides an overview of the discussion on the relative merits of grants and loans in the literature on foreign aid, including a short section on debt relief initiatives. These claims are then tested against the context of Ukrainian post-war reconstruction, and it is argued that the case for providing grants is very strong. This argument is based on the magnitude of the investments needed, the need to create a long-run sustainable economy, the road towards a future EU membership, and the global value of a democratic and prosperous Ukraine as a bulwark against autocratic forces.
Introduction
One topic in the discussion on the post-war reconstruction of Ukraine is to what extent foreign support should come as loans or grants. The case at hand regards reconstruction in the aftermath of a military invasion by an aggressive neighbor. Therefore, Ukrainian reconstruction is sometimes compared to the Marshall Plan, the US package to help rebuild Europe after World War II. But this choice is also part of the more general discussion on foreign aid, comparing concessional loans (loans with lower interest rates than the market rate) with grants (financial transfers with no expectation of repayment), not least since many aid receiving countries have been highly indebted. What are then the arguments in favor of one or the other in the foreign aid literature? And how should we think about this in the context of the Ukraine crisis?
The Case for Loans
From a donor perspective, loans could be preferred from a purely financial viewpoint, as long as they are repaid. This must be put into the perspective of the purpose of foreign aid, though. If the purpose is to increase the welfare of the poor, and if loans cause macroeconomic imbalances that eventually lead to a debt crisis, using loans for aid will defeat its purpose. It is thus important, even from a donor perspective, to differentiate between the pure financial costs and the effectiveness and efficiency of foreign aid in relation to the stated goals. Yet, the paradigm on which development banks such as the World Bank motivate their strategy is that, even from an effectiveness perspective, loans may outperform grants. In their model, the bank has a broad portfolio of investments across multiple countries prioritized in order of the social rate of return. By lending out money, the bank can invest the returns from the most prioritized project into the second-most prioritized project, most likely in a different country. If the money instead had been given as a grant, the best possible outcome is that the receiving country can now invest the returns in the next best project within that country. This argument thus relies on the assumption that development banks can continually identify the most promising recipients among their wide portfolio of alternatives.
It has also been argued that grants may reduce incentives to raise tax revenues, and encourage government consumption over investments, as there is no need to generate net revenues to repay the debt (e.g., Clements et al. 2004; Djankov et al. 2004). From a donor perspective, it can also be argued that the monitoring of grants may be weaker because donors have no direct financial interest in the success of a project if it is financed by a grant. The disciplining effect of loans, though, relies on the absence of moral hazard problems. If receiving governments expect debt to be forgiven anyway when it is perceived as unsustainable and counterproductive to the country’s development, loans may be no better.
Based on arguments such as those above, part of the literature suggests that concessional loans are more likely than grants to promote growth in recipient countries, at least in good institutional environments. Cordella and Ulku (2007) look into this in detail and develop a model linking the degree of concessionality, for a given level of foreign aid (i.e. the extent to which finances are on preferential terms compared to market rates), to the receiving country’s economic growth rate, in a world where default is possible. Concessionality varies from 100 percent grants to 100 percent loans on market terms. The model suggests that a country with better policies and stronger institutions has a higher absorptive capacity for investments, meaning it can handle a lower level of concessionality (i.e., more loans, fewer grants) without going into default. They also argue that the immediate incentives for default on a loan are higher for a poorer and more indebted country as the cost of servicing the loan is higher. This would motivate relatively more grants and fewer loans to countries that are poor and highly indebted. Taking this to the data, they find in consistence with their theory that for any given level of total assistance, the impact on growth is increasing with the degree of concessionality for poor countries with weak policy and institutional environments, whereas this matters less for richer countries with better policies and stronger institutions. Looking at the level of indebtedness, the results are inconclusive.
The Case for Grants
The arguments above generally favor loans over grants, but it is of course crucial to also consider the risks and consequences of excessive debt burdens and sovereign default. Perhaps the most dramatic example of the potential consequences of shouldering a country with an excessive debt burden comes from Germany after the end of World War I. The economic struggles and sense of humiliation that followed have been argued to have contributed to German grievances leading up to World War II. Less dramatic but still with significant implications is the “lost decade” affecting Latin American middle-income countries in the 1980s. The combination of cheap credit from oil-exporting countries and the sudden dramatic increase of international interest rates following US policies in the early 1980s resulted in unsustainable levels of commercial loans. This crisis led to a US initiative, the Brady Plan, by which bank loans were consolidated and partially backed by the US government.
Excessive lending is often the result of distorted incentives. Within development banks, there are widely recognized internal incentives to get projects “through the door” (e.g., Briggs 2021). This “aid pushing” happens for both grants and loans, but the consequences can be more detrimental for loans if this leads to unsustainable debt levels. Similarly, there is evidence of defensive lending, where countries receive loans simply to be able to repay previous loans. Birdsall et al. (2003) find that donors lent more to African countries with bad policies if they had a large existing debt. On the other side, recipient country governments with short-term horizons and in environments with weak institutional checks and balances do not necessarily internalize the full costs of excessive lending. Due to these incentives on both sides, loans too often reach unsustainable levels, with debt to GDP ratios and debt to net export revenues becoming increasingly alarming.
With increased recognition of the costs of development of unsustainable levels of official lending, debt negotiations targeting highly indebted low-income countries have become common. These negotiations have often taken place through the Paris Club (a group of 22 high or upper-middle income creditor nations, including Russia) or through the HIPC (Highly Indebted Poor Countries) initiative (e.g. Birdsall et al. 2002). These debt reduction agreements have been continuously renegotiated, offering more and more generous conditions including debt forgiveness, rescheduling of existing loan terms, and more focus on grants in the portfolios of official financing.
Of particular relevance for this note, though, are the discussions around these initiatives that illustrate the different arguments made in favor of, or against, debt relief. As brought up in Birdsall et al. (2002), critique against the HIPC initiatives came from both sides. On the one hand, some argued that debt forgiveness was just more aid “down the rathole”, encouraging irresponsible policies by receiving governments (e.g. Easterly 2001), and fuelled by commercially motivated bilateral donors and multilateral institutions with misguided bureaucratic incentives. In order for aid to be effective, much more stringent conditionality was needed, and if that didn’t work, stricter selectivity in terms of which governments to partner with. On the other hand, others argued that the initiatives did not go far enough (e.g. Sachs, 2002). The economic arguments largely relied on concepts of a poverty trap, impossible to escape under conditions of a heavy debt burden requiring scarce foreign exchange to be used for debt service and discouraging investments. These countries were perceived as particularly vulnerable to adverse economic shocks, and as such, in need of insurance mechanisms that wouldn’t burden them with claims hampering their ability to prosper looking forward. But there was also a moral dimension, with blame focused on the creditor side, arguing that citizens of poor nations could not be burdened by debt issued for political reasons by creditors looking the other way when receiving rulers used proceeds for personal purposes.
Financing Post-war Recovery
The discussion above relates to foreign aid in general. The situation of financing post-war recovery is more specific, but past examples may give some points of reference. It should be noted, however, that every situation is unique in terms of the level of destruction, preconditions for a quick recovery, the political ramifications, and the risk of a resurgence of violence. And all these factors matter for the ability and willingness of foreign actors to step in and help.
An often-made reference in conjunction with Ukrainian recovery plans is the Marshall Plan, also known as the European Recovery Plan following World War II. Through this plan, financed by the US, initially 16 countries in Europe were getting “help to self-help” at an amount corresponding to roughly 10,5 percent of the countries’ GDP at the time (roughly about $13 billion, or $138 billion in 2019 dollars). The resources were spent differently across receiving countries, depending on the level of physical destruction. Importantly, grants accounted for as much as 90% of the total resources (Becker et al. 2022). More generally, grants usually account for a more significant share of aid flows when it comes to post-war reconstruction. This is natural, as a large share of the funding typically goes to humanitarian relief, and war-torn countries tend to be saddled with debt and a low capacity to raise domestic revenues in the short to medium term given the destruction of the war.
The common reference to the Marshall Plan in the context of Ukraine is probably partly geographically motivated: it is another war in Europe. But there are also other reasons, such as the direct unprovoked aggression by one of the world’s leading military powers, and the potential ramifications for world peace and the existing world order. The Marshall plan was motivated by the desire to avoid the mistakes from the peace agreements after WWI, and to help create a unified western Europe as a bulwark against further communist expansion from the Soviet Union. There are similar arguments to be made for the case of Russia’s war on Ukraine.
Implications for Ukraine Reconstruction
According to World Bank statistics, the total external debt stock of Ukraine in 2020 was $130 billion in current values, or 81,4 % of Gross National Income (GNI). This is already quite high, but the war has of course completely upended the situation and the IMF argued that Ukraine was facing debt sustainability issues already by the beginning of March 2022. Public finances are in the short run facing double pressure from a steep fall in revenues as economic activity drops and the ability to raise taxes is eroded, and an increase in expenditures on defence and humanitarian relief. Looking ahead, estimates of the Ukrainian costs of the war range between $440 and $1 000 billion by end of March 2022, but there is of course high uncertainty, and the bill is increasing for each day that the war goes on (Becker et al. 2022). This could be compared to the 2021 estimate of Ukraine’s GDP at around $165 billion. Even in the most optimistic scenarios, the rebuilding effort will be very costly, and will require massive amounts of foreign capital.
The sheer amount of effort needed in itself speaks to the need for grant financing. Rebuilding will require both public and private capital, and attracting new investments will necessitate an economic environment that is perceived as stable, dynamic, and conducive to long-term growth. As in the discussion on debt forgiveness for low-income countries above, such new investments are unlikely to materialize if the debt situation is deemed unsustainable. Furthermore, arguments in favor of loans over grants on grounds of fostering domestic macroeconomic responsibility and reducing moral hazard problems, fall flat when a country is invaded by an aggressive neighbor. Ukraine has had its share of bad politics, but the current situation is not caused by poor policies, lack of reform, or irresponsible lending under the assumption of future bailouts.
It should also be noted that both the Ukrainian government and representatives of the European Union (EU) have emphasized the long-term ambition that Ukraine should join the EU. This will not be possible, however, unless the country’s economy is in order, including a sustainable debt level, according to EU requirements for all joining members. Were Ukraine to shoulder excessive levels of debt at this moment it would thus jeopardize this ambition. And not least, Ukraine is fighting for its survival, but the war is also part of a wider emerging struggle between democratic and authoritarian forces over the future world order. The result of the war is of great significance for all democratic countries, though it’s the people of Ukraine that are facing the immediate horrific consequences. It is thus in our common interest to rebuild a prosperous and democratic Ukraine also as a bulwark against further authoritarian ambitions to change the existing world order. A Ukraine saddled with an unsustainable debt burden runs completely counter to the interests of the democratic world.
The Marshall Plan was successful in its goal “to permit the emergence of political and social conditions in which free institutions can exist”. This allowed for economic and political cooperation to take roots in western Europe, also contributing to political stability and prosperity. This cooperation expanded further east after 1989 with the inclusion of new member states into the European Union, largely solidifying a move towards market-based democracy in the region (despite some recent setbacks, primarily in Hungary). Let us build on these successful examples. The current situation offers an opportunity to bring an additional 44 million people into the European umbrella of peaceful cooperation in the near future. This ambition would become much more difficult, though, if Ukraine was saddled with an excessive debt burden.
References
- Becker, Torbjörn, Barry Eichengreen, Yuriy Gorodnichenko, Sergei Guriev, Simon Johnson, Tymofiy Mylovanov, Kenneth Rogoff, and Beatrice Weder di Mauro. (2022). “A Blueprint for the Reconstruction of Ukraine” Rapid Response Economics 1, CEPR Press.
- Birdsall, Nancy, John Williams, and Brian Deese. (2002). “Delivering on Debt Relief: From IMF Gold to a New Aid Architecture”, Peterson Institute for International Economics, Washington DC.
- Birdsall, Nancy, Stijn Claessens, and Ishac Diwan. (2003). “Policy Selectivity Forgone: Debt and Donor Behavior in Africa” World Bank Economic Review 17 (3): 409–35.
- Briggs, R. C. (2021). “Why does aid not target the poorest” International Studies Quarterly 65 (3), 739-752.
- Benedict Clements, Sanjeev Gupta, Alexander Pivovarsky, and Erwin R. Tiongson. (2004). “Foreign Aid: Grants versus Loans” Finance and Development, September, pp. 46–49.
- Cordella, Tito and Hulya Ulku. (2007). “Grants vs. Loans” IMF Staff Papers, 54(1), 139-162.
- Djankov, Simeon, Jose G. Montalvo, and Marta Reynal- Querol. (2004). “Helping the Poor with Foreign Aid: The Grants vs. Loans Debate” World Bank, Washington, D.C.
- Easterly, William. (2001). “Debt Relief”, Foreign Policy 126, 20-26.
- Sachs, Jeffrey. (2002). “Resolving the Debt Crisis of Low-Income Countries” Brookings Papers on Economic Activity 1, Brookings 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.
Land Market and a Pre-emptive Right in Farmland Sales
After more than 20 years of a land sales ban, Ukraine finally opened its farmland market on July 1st, 2021. A design of the land market contains a pre-emptive right to buy the land for the farmland tenants. In this study, we model the effect of this pre-emptive right. Following the approach of Walker (1999), we use a theoretical model with three players – landowner, potential buyer, and the tenant – to model outcomes of the land transactions with and without the pre-emptive right. To empirically estimate the effect of the pre-emptive right, we use farm-level data to derive farmers’ maximum willingness to pay and the minimum price that landowners are willing to accept. The introduction of the pre-emptive right decreases the land price and increases the tenant’s chances of winning as well as his surplus, at the cost of a potential buyer and the landowner. The introduction of the pre-emptive right also leads to inefficient distribution and deadweight losses to the economy.
Introduction
After more than 20 years of a land sales ban, Ukraine finally opened its farmland market on July 1st, 2021. The moratorium on the sales of agricultural land in Ukraine covered of 96% of the country’s farmland market (or 66% of its entire territory).
The critical element of the newly opened Ukrainian farmland market design is the pre-emption right (right of the first refusal, RoFR) that is granted to the current tenant of land plots. By applying their pre-emptive right, tenants can purchase the land at the highest price the landowner could get on the market. On top of that, this right is transferable, meaning that the tenant could sell the right to the interested party. In this brief, we model the consequences of the pre-emptive right introduction in Ukraine.
Farmland Market in Ukraine
The moratorium on farmland sales that was in place for the last 20 years created a substantial distortion on the farmland market. It led to the situation where large companies predominantly cultivate the rented land, with the average share of leased land in the land bank for corporate farms in Ukraine approaching 99% (Graubner et al., 2021). Another noticeable trait of the farmland market in Ukraine is significant inequality in Ukrainian farms’ land banks. Based on the statistical forms 50AG, 29AG, and 2farm, our calculations show that the GINI index for the allocation of cultivated land across farms in Ukraine is 86%, indicating an extreme degree of inequality. As we can see from Table 1 – the top 10% of farms operate on 75% of all cultivated farmland in Ukraine. On the other side of the spectrum, 49% of the smallest farms in Ukraine operate on only 2% of the cultivated farmland and rent only 0,3% of all rented farmland.
Table 1. Ukrainian farmland market structure

Source – own calculations based on the statistical forms 50AG, 29AG, 2farm for the year 2016.
Therefore, in our analysis, we break a sample of Ukrainian farms into five categories with respect to their size.
Framework
To model the effect of the pre-emptive right, we will use the approach proposed by Walker (1999) using farm-level data. Thus, this study compares two scenarios – with the pre-emptive right (right of the first refusal, RoFR) and without the pre-emptive right in place. We assume that there are only three sides to each transaction – the seller (landowner), the prospective buyer, and the tenant, to whom the pre-emptive right is granted. Throughout this brief, we assume that there are no transaction costs involved.
Scenario 1. No Pre-emptive Right
In the no-RoFR scenario, the prospective buyer offers the landowner a price that the seller is willing to accept. The seller now has two options: either accept and get the offered price or reach the tenant and propose to outbid this offer. The option of reaching a tenant is more attractive since, in a worst-case scenario, if the tenant’s valuation – i.e., the maximum price the tenant is willing to pay for the land plot – is lower than the offered price, the tenant would simply not respond to this offer, and the landlord still gets the offered price.
On the other hand, if the tenant’s valuation is higher than the offered price, he has a strong incentive to make the counteroffer and start a bidding process. Both the tenant and the prospective buyer are incentivized to make a counteroffer up until the point where the offer’s value reaches their respective valuation. Thus, the smallest valuation between those of the tenant and prospective buyer would be the final transaction price.
Scenario 2. A Tenant Has the Pre-emptive Right
In this scenario, the tenant does not need to increase the price in his counteroffer if the third-party buyer’s offer is lower than the tenant’s valuation. The tenant could execute his pre-emptive right and buy the plot at the third-party buyer’s proposed price. Therefore, the outside buyer will change his approach to the initial offer. If the offer he makes is “too low”, he loses the chance of buying this plot since the tenant would exercise his pre-emptive right. If the offer is “too high,” he misses the profit he would make by making a lower offer.
In such circumstances, the transaction price will be given by the third-party buyer’s offer that maximizes his expected profit. The latter, in turn, depends on the probability of the tenant exercising his preemptive right, the third-party buyer’s own valuation, and the price he offers to the landlord. The probability of the tenant exercising the offer is the probability that the tenant’s valuation exceeds the offered price. It depends on the tenant’s farm size category and on the offer itself and can be calculated based on the distribution of valuations.
Empirical Approach
Our empirical analysis considers a (hypothetical) situation of a third-party buyer coming to the landowner, whose land is rented to another farmer, with the offer to buy a one-hectare plot. We assume that the offer exceeds the landowner’s minimum price that a landowner is willing to accept (WTA). The landowner’s WTA is proxied by the current rental price the landlord gets multiplied by the capitalization rate, set to 20 for all three sides of the transaction. The farmers’ valuations are estimated based on their net profit per hectare. We use the farm-level data to compute the average net profit per hectare needed for valuations estimation and the average rental price per hectare for the WTA estimation. This data was collected by the State Statistics Service of Ukraine through statistical questionnaires called 50AG, 29AG, and 2farm for the year 2016 and covers 39,297 farms. The descriptive statistics of the data are presented in table 2.
Table 2. Descriptive statistics

Source: own calculations based on the statistical forms 50AG, 29AG, 2farm for the year 2016.
We construct a set of potential buyers for each farm that operates on rented land based on the 10-km threshold distance between the tenant and third-party buyer. We end up with a sample of 764760 pairs of tenants and potential third-party buyers. We drop all pairs where third-party buyers cannot make an offer landlord is willing to accept. Therefore, only a sample of 291506 observations of tenant – prospective buyer pairs is used for the analysis. Importantly, for large and ultra-large farms, the share of observations that would attempt a transaction is 70% and 69% correspondingly. On the lower side of the size spectrum, this share is noticeably lower. For the group of small third-party buyers, the buyer would attempt the transaction only in 42% of cases. The most excluded from the farmland sales market category are ultra-small farms as they would only attempt the transaction in 25% of all cases.
Results
Our findings suggest that the effect of the pre-emptive right on the land price is twofold. On the one hand, in 55% of cases – the RoFR price is higher than the (modelled auction) price in the absence of a preemptive right. However, the median price differences in these cases are just 0,7% of the auction price. At the same time, for the cases where the auction price is higher than the price with the RoFR, it exceeds the RoFR price, on average, by 83%, with a median value of 66%. As a result, if we compare the expected prices, the expected prices under the RoFR are significantly lower than the auction prices. There are also differences between different farm size categories of the third-party buyer – the larger the buyer is, the higher the transaction price would be regardless of the RoFR. In the scenario without the RoFR, the average transaction price for ultra-small farms would be $1259 per hectare. While for the ultra-large farm as the third-party buyer, the transaction price would be $1647. With the pre-emptive right granted to the tenant, the transaction prices would be $977 and $1313 correspondingly.
The pre-emptive right also increases the probability of the tenant acquiring the land. The most noticeable effect is for ultra-small and small farms – if an outside buyer attempts the transaction, their chances of purchasing the land increase from 12% to 28% and from 23% to 45%, respectively. The probability increase for the larger tenants persists, but percentage-wise it is smaller – their probability of purchasing the land due to the granted pre-emptive right increases from 42-45% to 65-66%.
The pre-emptive right also redistributes the surplus from the transaction. Measuring the surplus as the difference between the valuation and the buyer’s actual purchase price, we can conclude that the third party’s surplus decreased due to the RoFR introduction. The tenant’s surplus, on the other hand, increases. In the case of RoFR introduction, the percentage increase in the tenant’s surplus is larger for the ultra-small and small farmers, from 5% to 13% and from 10% to 23% of the tenant’s valuation, respectively. For larger farms, albeit the surplus’ increase is larger in absolute terms, percentage-wise, it is smaller than for their smaller counterparts. Their average surplus increased from 18-20% to 37-38% of the tenant’s valuation. For the third-party buyers, the percentage-wise decrease is more or less the same, regardless of their farm size. Their surpluses, on average, shrink by 23-27% depending on the size of the farm.
We also estimated the effect of the pre-emptive right on the joint surplus of the landlord and the tenant. The effect of the pre-emptive right on their joint surplus is positive regardless of the size category of the tenant. The largest increase of the joint surplus, percentage-wise, is observed for the small-sized farms as a tenant. In this case, the average joint surplus increased by 5%, translating into an $87 increase in the joint surplus. In absolute terms, the highest increase is for medium-sized farms as a tenant – $108 increase in the surplus or 4.5% of their original joint surplus.
The pre-emptive right also leads to inefficient allocations when the land is acquired by a lower valuation party, resulting in deadweight losses. Inefficient allocation is observed in 19% of all observations. The deadweight losses generated by the introduction of the ROFR are statistically significant (with the t-value equal to 195) and average 233 USD per hectare.
Conclusions
In this brief, we suggest a theoretical and analytical approach to calculate the impact of the pre-emptive right in farmland sales. Our analysis offers a range of important findings. First, small and medium-sized farms are almost entirely excluded from the farmland market. While more than two-thirds of the medium, large or ultra-large farms can afford to buy a nearby parcel, based on their profitability – for ultra-small farms, which have a land bank of under 50 hectares – this share is equal to just 25%. The introduction of the pre-emptive right granted to the current tenant may exaggerate this problem. The reason is that most of the rented land is already controlled by large and ultra-large companies. At the same time, the pre-emptive right increases the tenant’s probability of winning and its surplus at the expense of the landowner and outside buyer.
On the other hand, the pre-emptive right increases the joint surplus of the tenant and the landowner. Therefore, if the pre-emptive right would be a voluntaristic clause in the contract, rather than a right granted to all tenants by the government, it creates an incentive to include the pre-emptive right in the rental agreement with the price of this right negotiated between the landlord and the tenant.
Summing up, the pre-emptive right, as a policy instrument, has its costs. It leads to inefficient distribution and deadweight losses. In view of this, as much as the recent farm market reform in Ukraine is a clear step towards a market economy, the design of the land market should be taken with a grain of salt.
References
- Graubner, Marten, Igor Ostapchuk and Taras Gagalyuk, 2021. “Agroholdings and land rental markets: a spatial competition perspective”, European Review of Agricultural Economics, 48(1), 158-206
- Walker, David, 1999. “Rethinking rights of first refusal“, Stanford Journal of Law, Business & Finance, 5, 1-58.
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.
Do Condominiums Pay Less for Heating?
In Ukraine, a widely shared perception is that housing utility costs are too high. In this policy brief, we study if these costs can be alleviated by introducing a modern form of housing management practice, condominiums. We find that condominiums in old houses (built before 1991) pay 22% less for heating compared to old non-condominiums. Among new houses (built after 1991), we find that condominiums pay 29% less for heating. Considering the dynamics of condominium formation in 2018-2020, old houses do not show any significant immediate effect of condominium formation on heating costs relative to that of non-condominiums. However, condominium formation among new houses leads to a relative 18% decrease in heating costs. In addition, among condominiums in old houses, participation in an overhaul co-financing program is associated with a 15% lower heating bill. The immediate effect of the program in 2018-2020 is a 16% relative decrease in heating costs for old condominiums and 37% – for new ones.
Heating Costs and Condominiums
In recent years, the cost of housing utilities has been a common concern among Ukrainians. According to a recent survey, 80% of Ukrainians believe that tariffs on utilities are too high.
The form of housing management is a factor that could affect utility costs. Experiences from Slovakia, Hungary, Poland, and Romania in the 1990s suggest that state-owned housing maintenance companies are often associated with inefficient management. Residential buildings that are owned and managed collectively by its dwellers (hereafter referred to as condominiums) are more likely to choose a more efficient private housing maintenance company (Banks, O’Leary et. al., 1996). For instance, in Slovakia’s second-largest city, Kosice, one-third of houses that were privatized in the 1990s chose private maintenance companies with competitive prices. Residents perceived the services as “far more effective” (ibid).
This brief summarizes our analysis of the relationship between heating costs and the form of housing management in Ukraine. Analyzing a large sample of houses in Kyiv, we show that condominiums are associated with lower heating costs, both among the older houses, built before Ukrainian independence in 1991, and among newer houses.
Types of Housing Management Practices in Ukraine
The different housing management practices in Ukraine can be roughly divided into three types. The most commonly used practice is when housing maintenance is carried out by a municipally owned company (commonly referred to as ZhEK – “zhilischno-eksplotazionnaja kontora”, housing maintenance office). Usually, houses that have the ZhEK-type management were built before Ukrainian independence and have kept this practice since Soviet times. The second practice is when housing maintenance is done by a private company affiliated with the building developer. This management type is usually used by houses built after Ukrainian independence that did not form condominiums. These two practices are similar in the sense that dwellers are not directly involved in the decision-making, all decisions are made by the municipal or private company, respectively.
The third type of housing management practice, relatively new for Ukraine, is condominium ownership (the Ukrainian term for it is ОСББ, translated as “Association of Co-owners of Multi-Apartment House”). In a condominium, unlike in the previous two types, the house is managed collectively by the dwellers; in particular, they have the freedom to choose and/or change utility providers, invest in major overhaul, and participate in co-financing programs.
Houses with Condominiums Pay Less for Heating
In our analysis, we use monthly data on housing costs between 2018 and 2020 collected from the Ukrainian municipal enterprise Kyivteploenergo. The data covers more than 70% of residential buildings in Kyiv and includes information on heating costs per square meter, whether or not the house is a condominium, and other house-characteristics (including the source of heating production; the presence of the meter; type of the meter number of service days per month; and share of heat consumption by legal entities).
In addition, we have information on the year of building construction retrieved from the real estate portal LUN, and condominium formation date between 2018-2020, as well as data on house participation in overhaul co-financing programs obtained from the Kyiv state administration.
Our final sample contains 7957 houses. Since we only are interested in apartment housing, we exclude residential buildings with an area below 500 m2, which would normally correspond to a small private house (these constitute only a small part of our sample). The share of condominiums in the sample is 11%, the share of houses with ZhEK is 81% and the share of houses managed by private companies is 8%.
Figure 1. Median costs for heating per m2 across housing management types and house age.

Source: Authors’ calculations. Old houses are those built before 1991, the year of Ukrainian independence, and new houses are built after 1991.
Figure 1 provides preliminary evidence towards our hypothesis, showing that the median heating costs are lower in condominiums, independent of the year of construction.
In our first econometric model, we use an OLS-approach to compare utility costs across different types of housing and management models, while controlling for a number of observable characteristics. We find that condominiums in old houses pay 22% less for heating than old non-condominium. Similarly, we find that condominiums in new houses pay 29% less for heating compared to new non-condominiums.
The lower heating costs observed in condominiums may have several explanations:
- First, condominium-type management could be more flexible in its response to weather conditions. Considering that they are profit-maximizing, heating providers in Ukraine tend to overheat houses during the heating season; it could be that condominiums reduce consumption of heating on the warmer days to a greater extent than other houses. In other words, condominiums could increase the efficiency of heating use.
- Second, it could be that condominiums have lower heating costs because they improve energy efficiency, for example, by installing individual heating points (an automatized unit transferring heat energy from external heat networks to the house heating, hot water supply, ventilation, etc.), new windows, or even insulating the house.
Is There an Immediate Effect?
The next step in our econometric analysis is to study the effect of condominium formation during 2018-2020. Here, we investigate whether non-condominium houses that became condominiums experienced changes in heating costs by utilizing a fixed-effects regression model. This approach not only allows us to assess the immediate effect of condominium formation but also controls for unobservable house-specific characteristics that are constant over time, such as differences in building materials.
For new houses, we find that condominium formation decreases heating costs by 18% compared to other new houses. For old houses, we find that the corresponding effect is statistically insignificant.
This estimation only evaluates the effect of condominium formation in a relatively short timeframe, between 2018 and 2020. While the data coverage does not allow us to give a precise quantitative assessment for a long-run effect, we argue that the positive impact of condominium formation on heating costs could potentially be higher in the longer-run. Indeed, our previous OLS estimation assesses the average utility costs for all condominiums in the sample (including those formed prior to 2018). It shows that the gap in heating costs between all condominiums and non-condominiums is higher than the corresponding gap derived from our fixed-effects estimation (22% for the old houses and 29% – for the new ones). While this difference in results can be driven by several reasons (e.g., fixed effect estimation taking into account unobservable house-specific characteristics), a stronger long-term effect could be among them.
Concerning the results for new vs. old houses, it might be the case that new houses are technically equipped to be more flexible when it comes to adjusting costs (e.g., are able to switch the heating on/off), while old houses might be inferior in this regard. If this is the case, old houses would only experience lower costs after some thermo-modernization, such as installing individual heating points.
Heating Costs and the Co-financing Program
Since 2015, the Kyiv city council offers a program that helps condominiums to finance major overhauls with the intent to improve the energy efficiency of the residential sector. Applicants compete in planning thermo-modernization projects where winning condominiums are awarded financing covering 70% of the overhaul cost.
Our results show that for old houses with condominiums, those who at some point participated in the co-financing program pay on average 15% less for heating compared to non-participants. The corresponding effect for new houses with condominiums is not significantly different from zero.
However, the immediate effect of program participation is present in both new and old houses with condominiums. Old and new condominiums that took part in the program in 2018-2019 experienced an immediate reduction in heating costs by 16% and 37% respectively.
Figure 2. The number of houses participating in the 70/30 co-financing program across the years.

Authors’ calculations.
There are several potential explanations as to why we observe an immediate effect but no effect of ever participating in the program for the new houses with condominiums.
First, it could be that new houses with condominiums that are not participating in the program are investing in overhaul anyway, although somewhat delayed compared to investments made by participating new condominiums. The average difference in heating costs between participants and non-participants would then be visible in the short-run and fade away after a few years. If this is the case, the program is financing houses that would have invested in overhaul anyway, even without co-financing. This explanation is partly supported by the fact that the share of the new houses condominiums among participants is 32%, while the corresponding share is 15% among all houses. In other words, old houses with condominiums, that are usually in a worse condition, are underrepresented in the program.
If this is the case, the share of old houses with condominiums among participants should be increased. Given that the purpose of the program is to improve the energy efficiency of residential buildings, its efficient implementation implies encouraging overhauls in houses that are otherwise unable to fund it. In other words, the program should incentivize people living in energy-inefficient housing to form condominiums and undertake overhauls to improve their energy efficiency, rather than finance houses who are already doing well in that regard. To improve on such selection issues, the program could change the co-financing proportions, making participation more beneficial to old houses with condominiums, e.g. 80/20 – for old and 60/40 – for new condominiums.
Second, the new houses with condominiums that participate in the program might be in a much worse state before participation than those that do not. Program part-taking could make participants catch up to the average level of energy-efficiency (or perhaps do slightly better). If this is the case, the program fulfills its function in the sense that it targets the most energy-inefficient houses.
Government Policies That Should Be Changed
Above, we argue that the formation of condominiums leads to efficiency gains in energy use and cuts utility costs for dwellers. Given the design of the overhaul co-financing program, the Kyiv city council seems to recognize these benefits as well. However, there is a range of government policies currently in place that discourage people from condominium formation.
For example, there are cases when the government finances 100% of overhaul costs using a subvention (“subvention for socio-economic development”). In 2020, 17 houses in Kyiv got overhaul expenses funded by this type of subvention. At the same time, 85 houses that participated in the co-financing competition did not receive any state funding (there were 100 winners among 185 participants).
Considering that this type of subvention predominantly finances non-condominiums, we argue that this policy creates the wrong incentives. Dwellers will likely refrain from forming condominiums in the hope of eventually being selected for an overhaul fully financed by the state, instead of forming condominium and getting only part of overhauls expenses covered (70% of the overhaul funding if winning co-finance program competition, and no funding otherwise).
In addition, this subvention typically has a “pork-barrel” nature since it is often allocated to the constituencies of the ruling party’s MPs. State financed overhauls are often used as an advertisement tool to get popular support. This creates an additional problem in the sense that subvention is targeted to politically loyal regions and not necessarily to regions in need of support.
Along this line of reasoning, we suggest that this pork-barrel subvention should be cancelled and housing-overhauls should instead be funded through co-financing programs. The government should implement programs similar to the “70/30” and further encourage people to adopt condominium ownership.
Conclusion
Motivated by the common perception that utility costs are excessively high, we study one possible way of reducing the utility bill – condominium housing management.
Our analysis shows that old houses with condominiums pay 22% less for heating compared to old non-condominiums. For new houses, we find that condominiums pay 29% less in heating costs than non-condominiums. In addition, old houses with condominiums that participate in Kyiv’s co-financing program pay 15% less than other old condominiums. That is, condominium formation combined with the co-financing program could save more than one-third of a resident’s heating costs.
Our analysis suggests the following policy implications:
- Condominiums have a positive effect on energy efficiency, and utility cost savings, and should thus be promoted to the population as a preferable form of house management practice.
- State and municipal governments should provide incentives for condominium formation through, e.g., overhaul co-financing programs. Other state-provided forms of overhaul financing, such as pork-barrel subvention, should be cancelled.
- Co-financing programs should combine better targeting (e.g., to those houses that are in greater need of overhaul) with sufficient incentives for condominium formation.
References
- Hamaniuk, Oleksii; and Andrii Doschyn, 2020. “Let’s reduce the cost of heating by a third!” – ACMH and co-financing program for buildings”, https://voxukraine.org/en/let-s-reduce-the-cost-of-heating-by-a-third-acmh-and-co-financing-program-for-buildings/
- Banks, Christopher, Sheila O’Leary, and Carol Rabenhorst, 1996. Review of urban & regional development studies, vol. 8, issue 2. https://doi.org/10.1111/j.1467-940X.1996.tb00114.x
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 to Liberalise EU-Ukraine Trade under DCFTA: Tariff Rate Quotas
This policy brief focuses on trade relations between Ukraine and the EU amid preparations for the review of the Deep and Comprehensive Free Trade Agreement (DCFTA) due in 2021. In particular, it analyses Ukraine’s utilization of the DCFTA tariff rate quotas (TRQs) over 2016-2019. According to the results, Ukraine has been steadily increasing the level of TRQs usage – in terms of the number of utilized TRQs and export volumes within and beyond TRQs. For some DCFTA TRQs, total exports to the EU far outweigh quota volumes, while for other TRQs supply is limited by quota volume. The brief provides arguments and recommendations for the DCFTA TRQs update to increase Ukraine’s duty-free access to the EU market.
Why Update DCFTA TRQs for Ukraine?
EU-Ukraine trade under the Deep and Comprehensive Free Trade Agreement (DCFTA, in effect since January 1, 2016) progressed considerably. Ukraine’s exports of goods to the EU reached $20.8 billion in 2019 – a 54% increase compared to 2016 and a 24% increase compared to pre-crisis 2013.
According to the EU-Ukraine Association Agreement/DCFTA, the parties may initiate a review of its provisions in five years from its implementation – in 2021. So far, both governments confirmed their readiness to start such negotiations next year.
Ukraine advocates for further trade liberalisation with the EU through reducing the existing tariff and, most importantly, non-tariff barriers. This is an imperative for maintaining positive trade dynamics and providing new impetus to deepening bilateral economic integration.
Updating duty-free tariff rate quotas (TRQs) under the DCFTA is at the top of the EU-Ukraine 2021 negotiations agenda. Current quota volumes are based on outdated statistics, as it has been 10 years since the DCFTA negotiations (2008-2011).
Many TRQs are too low in terms of Ukraine’s current export and production capacities. For example, Ukraine’s total exports of grains (annual averages) increased from 19 million tons in 2008-2010 to 42.3 million tons in 2016-2018. Honey exports increased from 5.9 thousand tons in 2008-2010 to 58 thousand tons in 2016-2018. As a result, some TRQs are fully exhausted in the first days or months of the year.
High competition for access to duty-free quota volumes is a barrier first of all for SMEs that cannot compete effectively for it with large companies, while out-off-quota tariffs may be too restrictive for them.
Ukraine’s TRQs Utilisation During 2016-2019
DCFTA TRQs grant partial liberalisation of market access to the EU. Zero tariff rates are only applied to a specified quantity of imported goods inside a TRQ, while beyond TRQ imports to the EU are dutiable on a regular basis (subject to third-country tariff rates).
The EU applies TRQs for 36 groups of agro-food products originated in Ukraine plus 4 additional TRQs for certain product groups (in total 40 TRQs under DCFTA) – see Table 1. Ukraine applies TRQs for 3 groups of products plus 2 additional TRQs.
By the level of utilisation, TRQs fall into three groups: 1) fully utilised. They, in turn, can be divided into TRQs with and without over-quota supply; 2) partially utilised; and 3) not utilised.
The data indicate a general upward trend in Ukraine’s utilisation of TRQs under the DCFTA. In general, Ukrainian exporters utilised 32 TRQs in 2019 (80%) comparing to 26 TRQs in 2016 (65%).
Figure 1. Number of DCFTA TRQs utilized by Ukraine during 2016-2019.

Table 1 shows Ukraine’s utilization of 40 DCFTA TRQs over 2016-2019 – in tons and %. The main findings include:
The number of fully exhausted TRQs has been increasing. In 2019, Ukraine filled up 12 TRQs including honey; processed tomatoes; wheat; maize; poultry meat; barley groats and flour, other cereal grains; sugars; grape and apple juice; butter and dairy spreads starches; starch processed; as well as malt-starch processed products. For 9 of them, Ukraine’s supplies exceeded TRQs volumes.
The number of partially utilized TRQs increased from 16 in 2016 to 20 in 2019. In 2018-2019, Ukraine began using new TRQs such as fermented-milk processed products; malt-starch processed products; sugar syrups. High TRQs utilization rates (over 80%) in 2019 were observed for malt and wheat gluten; cereal processed products; eggs (main); barley, barley flour and pellets.
Moreover, Ukraine increased utilisation of TRQs for processed products. For example, utilisation of a TRQ for cereal processed products increased from 2.7% in 2016 to 99.5% in 2019. This signifies the growing ability of Ukrainian producers to comply with the EU food safety requirements and standards for processed products. Exports of processed starch increased significantly in 2019 and exceeded TRQ volume by a lot.
Ukraine’s utilisation of some TRQs has decreased. For example, a TRQ for oats gradually decreased from 100% in 2016 to 31% in 2019 due to a decrease in total exports and domestic production of oats in Ukraine during this period. Low utilisation of other TRQs may also be attributed to high price competition and quality requirements in the EU, complex quota allocation procedure, etc.
The number of not utilized TRQs decreased from 14 in 2016 to 8 in 2019. For instance, no exports within TRQs were observed for beef, pork, sheep meat, as Ukraine has not yet been authorized to export these meat products to the EU.
Moreover, since October 2017, Ukraine has been able to use provisional TRQs that were granted by the EU as autonomous trade measures (ATM) for 3 years. They increased duty-free access for 8 groups of Ukrainian products – in addition to the relevant DCFTA TRQs. So far, Ukraine fully utilises 5 ATM TRQs including honey; processed tomatoes; barley groats and meal, cereal grains otherwise worked; wheat, flour and pellets; maize, flour and pellets.
Total Exports to the EU vs Duty-Free Exports Within TRQs
For most fully utilized DCFTA TRQs, Ukraine’s total exports of the covered products exceeded TRQ volumes during 2016-2019. Considerable over-quota supply occurred for: honey; processed tomatoes; barley groats and meal, cereal grains; apple and grape juice; maize, flour and pellets; poultry meat; wheat, flour and pellets; sugars; butter and dairy spreads; starch processed.
For instance, over-quota exports of processed tomatoes from Ukraine to the EU in 2019 (31.2 thousand t) more than doubled the quota volumes (10,000 t of the DCFTA TRQ and 3,000 t of the provisional ATM TRQ). See Figure 2 for more examples.
Figure 2. Ukraine’s exports to the EU within and beyond certain TRQs, 2016-2019.

Increasing exports beyond TRQs indicate significant demand for these Ukrainian products in the EU, and their competitiveness in terms of price and quality on the EU market.
It also signifies that volumes of these fully utilised DCFTA TRQs with increasing over quota exports are rather low in terms of Ukraine’s export and production potential. Therefore, these TRQs are the primary candidates for updating.
At the same time, for certain DCFTA TRQs (malt-starch processed products; starch, malt and wheat gluten), exports to the EU were about 100% of TRQ volume but did not go far beyond. This may indicate a significant restrictive impact of those TRQs and out-of-quota tariffs for Ukrainian exports. These TRQs also need to be further analysed and revised.
Тable 1. Utilisation of DCFTA tariff rate quotas by Ukraine, 2016-2019.
| 2016 | 2019 | |||||
| Quota name | Quota volume | Utilised | Quota volume | Utilised | ||
| t | t | % | t | t | % | |
| “First-come, first-served” method for TRQ allocation | ||||||
| Sheep meat | 1500 | 0 | 0,0% | 1950 | 0 | 0,0% |
| Honey | 5000 | 5000 | 100% | 5600 | 5600 | 100% |
| Garlic | 500 | 49 | 9,8% | 500 | 393 | 78,6% |
| Oats | 4000 | 4000 | 100% | 4000 | 1239 | 31,0% |
| Sugars | 20070 | 20070 | 100% | 20070 | 20070 | 100% |
| Other sugars | 10000 | 5929 | 59,3% | 16000 | 1006 | 6,3% |
| Sugar syrups | 2000 | 0 | 0,0% | 2000 | 7 | 0,4% |
| Barley groats and meal, cereal grains otherwise worked | 6300 | 6300 | 100% | 7200 | 7200 | 100% |
| Malt and wheat gluten | 7000 | 7000 | 100% | 7000 | 6319 | 90,3% |
| Starches | 10000 | 1898 | 19,0% | 10000 | 10000 | 100% |
| Starch processed | 1000 | 0 | 0,0% | 1600 | 1600 | 100% |
| Bran, wastes and residues | 17000 | 7286 | 42,9% | 20000 | 14467 | 72,3% |
| Mushrooms main | 500 | 0 | 0,1% | 500 | 0 | 0,0% |
| Mushrooms additional | 500 | 0 | 0,0% | 500 | 0 | 0,0% |
| Processed tomatoes | 10000 | 10000 | 100% | 10000 | 10000 | 100% |
| Grape and apple juice | 10000 | 10000 | 100% | 16000 | 16000 | 100% |
| Fermented-milk processed products | 2000 | 0 | 0,0% | 2000 | 866 | 43,3% |
| Processed butter products | 250 | 0 | 0,0% | 250 | 0 | 0,0% |
| Sweetcorn | 1500 | 13 | 0,9% | 1500 | 23 | 1,5% |
| Sugar processed products | 2000 | 340 | 17,0% | 2600 | 417 | 16,0% |
| Cereal processed products | 2000 | 55 | 2,7% | 2000 | 1989 | 99,5% |
| Milk-cream processed products | 300 | 73 | 24,4% | 420 | 9 | 2,2% |
| Food preparations | 2000 | 5 | 0,3% | 2000 | 65 | 3,2% |
| Ethanol | 27000 | 1889 | 7,0% | 70800 | 6083 | 8,6% |
| Cigars and cigarettes | 2500 | 0 | 0,0% | 2500 | 0 | 0,002% |
| Mannitol-sorbitol | 100 | 0 | 0,0% | 100 | 0 | 0,0% |
| Malt-starch processed products | 2000 | 0 | 0,0% | 2000 | 1998 | 99,9%* |
| Import licensing method for TRQ allocation | ||||||
| Beef meat | 12000 | 0 | 0,0% | 12000 | 0 | 0,0% |
| Pork meat main | 20000 | 0 | 0,0% | 20000 | 0 | 0,0% |
| Pork meat additional | 20000 | 0 | 0,0% | 20000 | 0 | 0,0% |
| Poultry meat and preparations main | 16000 | 16000 | 100% | 18400 | 18400 | 100% |
| Poultry meat and preparations additional | 20000 | 8552 | 42,8% | 20000 | 9174 | 45,9% |
| Eggs and albumins main | 1500 | 232 | 15,5% | 2400 | 2027 | 84,5% |
| Eggs and albumins additional | 3000 | 0 | 0,0% | 3000 | 1891 | 63,0% |
| Wheat, flours, and pellets | 950000 | 950000 | 100% | 980000 | 980000 | 100% |
| Barley, flour and pellets | 250000 | 249460 | 99,8% | 310000 | 249250 | 80,4% |
| Maize, flour and pellets | 400000 | 400000 | 100% | 550000 | 550000 | 100% |
| Milk, cream, condensed milk and yogurts | 8000 | 0 | 0,0% | 9200 | 250 | 2,7% |
| Milk powder | 1500 | 450 | 30,0% | 3600 | 560 | 15,6% |
| Butter and dairy spreads | 1500 | 690 | 46,0% | 2400 | 2400 | 100% |
Source: European Commission, own calculations * Note: We consider 99.9% usage rate as fully utilized TRQ.
Conclusion
The EU and Ukraine confirmed their readiness to initiate the update of the DCFTA due in 2021. Ukraine is interested in increasing duty-free trade under DCFTA with the EU in line with the current state of Ukraine’s production and export capacities, as well as EU-Ukraine bilateral trade developments.
Although many DCFTA TRQs did not limit over-quota exports, Ukraine wants to revise DCFTA TRQs to secure permanent broader duty-free access to the EU market and reduce access barriers for SMEs (as SMEs are more affected by TRQs and other non-tariff barriers). So far, the EU temporarily increased certain TRQs in 2017 for three years as autonomous trade preferences for Ukraine. The primary candidates for the update should include DCFTA TRQs demonstrating high utilization rates, with or without over-quota supply (honey; processed tomatoes; barley groats and meal, cereal grains; apple juice; sugars; butter and dairy spreads; starch processed, etc.).
Amid future DCFTA update negotiations, Ukraine should conduct a detailed analysis for each DCFTA TRQ (taking into account temporary ATM quotas) to prepare its suggestions how and to what extent to liberalise them. It is worth considering different options of such liberalisation – by either increasing TRQs’ volumes or setting up preferential tariff rates for Ukraine instead, etc.
In the framework of the future negotiations with the EU, a special emphasis should be placed on increasing duty-free access for Ukrainian processed goods to promote their exports to the EU – as stipulated in the Export Strategy of Ukraine. For this purpose, Ukraine may explore possibilities for modifying the structure of certain TRQs (such as wheat, flour and pellets; maize, flour and pellets; barley, flour and pellets) to separate primary and processed products and to ensure more duty-free volumes for processed products.
References
- European Commission, 21.04.2020. DG Agriculture and Rural Development. “AGRI TRQs – Allocation Coefficients and Decisions”.
- European Commission, 12.02.2020. Remarks by Commissioner Várhelyi at a press conference with Prime Minister of Ukraine, Oleksiy Honcharuk.
- European Commission, DG Taxation and Customs Union, 21.04.2020. Tariff quota consultation.
- European Commission, 21.04.2020. “Trade Helpdesk Statistics.”
- OECD, 2018. “Fostering greater SME participation in a globally integrated economy”.
- Official Journal of the European Union, 2014. “EU-Ukraine Association Agreement”.
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.
A Decade of Russian Cross-Domain Coercion Towards Ukraine: Letting the Data Speak
Russia’s coercion towards Ukraine has been a topic of major international events, meetings and conferences. It regularly makes the headlines of influential news outlets. But the question remains open – do we really understand it? We diligently collect and analyze data to make informed decisions in practically all domestic issues but is the same done for international relations? This research paper introduces a number of tools and methods that could be used to study Russia’s coercion towards Ukraine beyond its most visible manifestation, looking into latent trends and relations that could reveal more.
Introduction
For the past decade, Ukraine has been in the headlines of the major world news outlets more frequently than ever before. Ukrainian-Russian relations have been and still remain the topic of international summits and other events. The occupation of a part of Ukraine’s territory has been denounced and Russia’s coercion towards Ukraine is now generally accepted as a fact. But what do we really know about the underlying empirics and dynamics and how can this multi-domain assertiveness be measured and tracked? This paper presents a number of data-driven approaches that allow looking beyond the headline stories to identify and track various dimensions of Russia’s coercion towards Ukraine and the dynamics of its development.
Academic Interest
Mapping the landscape of scholarly literature reveals a number of interesting results. First, the body of works studying Russia’s coercion towards Ukraine remains relatively modest. It quintupled in 2014 but afterwards the interest started tapering off. A search for papers on this topic in Scopus and Web of Science with a very precise query (to increase the accuracy of search) and publication time of 2009-2019 returned 155 papers most of which were published in or after 2014.
Figure 1. Scholarly publications on Russian-Ukrainian relations.

Source: WoS and Scopus, 2009-2019
A closer look at the content of these works with the use of a bibliometric software called CiteSpace shows that the majority of papers focus on Putin, once again emphasizing the significant role of his personality in Russia’s coercion towards Ukraine. The second largest cluster has the “great power identity” as its main theme and presumably looks beyond actions of Russia to identify its ideological grounds. Another group of publications is devoted to sanctions, pointing to their important role in studying Russian-Ukrainian relations.
Figure 2. The landscape of topics in scholarly publications on Russian-Ukrainian Relations.

Expressions of Coercion
The “practical” side of Russia’s coercion towards Ukraine is also frequently associated with the personality of Vladimir Putin and his attitudes towards Ukraine. To analyze this perception further, we created a corpus of speeches of Russian presidents published on the Kremlin website, filtered them to keep only those that mention Ukraine, divided them into pre-2014 and 2014 and after, and then analyzed them using an LDA topic modeling algorithm. This algorithm is based on the assumption that documents on similar topics use similar words. So, the latent topics that a certain document covers can be identified on the basis of probability distributions over words. Each document covers a number of topics that are derived by analyzing the words that are used in it. In simple terms, the model assigns each word from the document a probabilistic score of the most probable topic that this word could belong to and then groups the documents accordingly.
Figure 3. Speeches of Russian presidents before 2014, LDA topic modeling.

Figure 4. Speeches of Russian presidents in 2014 and after, LDA topic modeling.

Quite surprisingly, we discovered that the overall rhetoric of speeches is very similar for the two periods. Although some speeches do differ and the later corpus includes new vocabulary to reflect some changes (i.e “Crimea”, “war”) the most common words remain practically the same. Thus, regardless of the apparent shift in relations between the two countries, Russian leadership still relies on the same notions of collaboration, interaction, joint activities, etc. The narrative of “brotherhood” between the nations persists despite and beyond the obvious narrative of conflict.
To include a broader circle of Russia’s leadership we also looked at the surveys of the Russian elite conducted regularly by a group of researchers led by William Zimmerman and supported by various funders over the years (in 2016 – the National Science Foundation and the Arthur Levitt Public Affairs Center at Hamilton College). Seven waves of the survey already took place; the most recent one in 2016. The respondents were the representatives of several elite groups (government, including executive and legislative branches, security institutions, such as federal security service, army, militia, private business and state-owned enterprises, media, science and education; for practical reasons from Moscow only).
The survey revealed a number of interesting observations. For instance, while the prevailing Russian opinion on Russia’s occupation of Crimea had been that it was not a violation of international law, a closer look at the demographic characteristics of respondents shows that they were not as coherent as it might seem from the outside. While the “green” answers from respondents with backgrounds such as media or private business may have been anticipated, the number of members of the legislative and especially executive branch and the military that had at least some doubt on the legality was surprisingly quite sizable, and they even demonstrated some support of the “violation of law” interpretation.
Figure 5. Elite and public opinion on Russia’s annexation of Crimea.

Comparing these elite opinions to the public opinion poll by Levada Center conducted in the same year shows that even the general public is slightly more likely to choose the most extreme “full legality” option than the respondents from the executive branch.
Beyond the elite or general opinion polls, we tried to develop a metric that might allow us to track Russian sensitivities towards Ukraine. For that, we examined two different ways of expressing “in Ukraine” in Russian language: ‘на Украине’ (the ‘official’ Russian expression) vs. ‘в Украине’ (the version preferred by Ukrainians). [In English, this can be compared so saying ‘Ukraine’ vs ‘the Ukraine’.]
Our first visual plots how many search queries were done on Google Search with both versions over the last decade.
Figure 6. Search queries for “в Украине” (green) versus “на Украине” (red), Google Trends, 2009-2019.

We can clearly observe that during less turbulent times the more politically sensitive version is much more common. This however drastically changes during the peaks of Russia’s coercion towards Ukraine when the number of searches with the less politically correct term increases significantly.
A different trend can be observed if we look at official media publications stored in the Factiva database. We estimated the ratio of search volumes for each term and observed that until the beginning of 2013, about a third of articles and news reports used “in Ukraine”. This changed around January 2013 when the ratio starts to decrease for “in Ukraine” searches and plummets to a mere 10% of outlets still preferring this term.
Figure 7. The ratio of “в Украине” to “на Украине” occurrences in large Russia media (2009 – 2019), Factiva.

Tracking Coercion Itself
What is the track record of Russia’s actual coercion over this decade? For this, we turn to a few recent datasets that try to systematically capture verbal and material actions (words and deeds): the automated event datasets. The largest one of those, called GDELT (Global Database of Events, Language, and Tone), covers the period from 1979 to the present, and contains over three quarters of a billion events. It is updated every fifteen minutes to include all “events” reported in the world’s various news outlets. To exclude multiple mentions of the same event by one newswire, the events are “internally” deduplicated. The events are not compared across newswires.
An event consists of a “triple” coded automatically to represent the actor (who?), the action (what?) and the target (to whom?) as well as a number of other parameters such as type (verbal or material; conflict or cooperation; diplomatic, informational, security, military, economic), degree of conflict vs cooperation etc. Other similar datasets include ICEWS (Integrated Crisis Early Warning System) and TERRIER (Temporally Extended, Regular, Reproducible International Events Records). For this analysis, we filtered out only those events in which Russia was the source actor and Ukraine was the target country. We present two metrics: (1) the percentage of all world events that this subset of events represents and (2) the monthly averages of the Goldstein score, which captures the degree of cooperation or conflict of an event and can take a value from -10 (most conflict) to +10 (most cooperation). Also, to add a broader temporal perspective, we looked beyond the last decade. It can be clearly seen that the number of events before 2013 was significantly lower, especially in “material” domains. Some verbal assertions from Russia towards Ukraine happened during the Orange Revolution and so-called “gas wars”.
The situation changes radically starting from 2013. The proportion of events increases with some especially evident peaks (i.e. during the occupation of Crimea). The verbal events remain quite neutral while the actions towards Ukraine move from some fluctuations to steadily conflictual.
Figure 8. Russia’s negative assertiveness towards Ukraine, 2000-2019.

Measuring Influence
We have seen that the past decade was exceptional in the scale of Russian assertiveness towards Ukraine. But what do we know about Russia’s influence on Ukraine and Ukraine’s dependence on Russia? Influence measures the capacity of one actor to change the behavior of the other actor in a desired direction. In an international context this often concerns the relations between countries. Influence can be achieved by various means, one of which is to increase the dependence of the target country upon the coercive one. This strategy is frequently employed by Russia willing to regain and/or increase control over the former post-Soviet countries. The Formal Bilateral Influence Capacity (FBIC) Index developed by Frederick S. Pardee (Center for International Future) looks at several diplomatic (i.e. intergovernmental membership), economic (trade, aid) and security (military alliances, arms import) indicators allowing to identify the level of dependence of one country upon another. This is especially interesting from a comparative perspective. Figure 9 shows that countries such as Armenia and Belarus remain highly dependent on Russia. For half of the decade, Ukraine was number three on this list. Today the situation has changed. Ukraine’s dependence on Russia has gradually decreased and has become even smaller than Moldova’s, moving closer to the steadily low level of dependence of Georgia. This may signify a positive trend and a break of a decade-long relationship of dependence.
Figure 9. Dependence of post-Soviet countries on Russia, FBIC.

Conclusion
Consequently, Russia and Ukraine have become much more visible in the international academic and policy research efforts. This can be measured through a number of instruments, including a comprehensive mapping of the academic landscape itself with regard to salience and topics that are being studied, analysis of the word choice (that could be represented by the use of the terms to describe events in Ukraine by the government media and Google search users (“на Украине” versus “в Украине”); speeches of Russian presidents that use the same rhetoric of collaboration when talking about Ukraine despite the obvious change in relationships) and material coercion (significant increase in number of assertive conflictual Russia’s actions towards Ukraine). Some findings do give hope for change: the opinions of the Russian elite on recent Russian actions towards Ukraine while remaining generally unfavourable are not as cohesive as it might appear and Ukraine’s dependence on Russia has decreased significantly.
Disclaimer
This research is a part of a larger research effort titled RuBase funded by the Carnegie Foundation of New York and implemented jointly by The Hague Centre for Strategic Studies and Georgia Tech with the help of the Kyiv School of Economics StratBase team in Ukraine. The ‘Ru’ part of the title stands for Russia; and ‘base’ has a double meaning – both the knowledge base built during the project, and the (aspirationally) foundational nature of this effort. The project intends to look beyond the often-shallow traditional understanding of coercion and apply innovative tools and instruments to study coercion in its multifaceted form. This is only a small selection of the tools that have been successfully tested in the course of this (ongoing) research project and applied to the study of Russia’s coercion in different domains. The prospects of any progress in resolving the Russian-Ukrainian conflict are currently slim, thus further work that would allow identifying patterns and trends that the human eye may oversee to understand Russia better and develop an informed foreign policy strategy both for Ukraine and the West is crucially important.
References
- Boschee, Elizabeth et al. (2019). “ICEWS Automated Daily Event Data.” (November 12, 2019).
- Clarivate Analytics (2019). “Web of Science Core Collection.” Web of Science Group. (January 20, 2020).
- Dow Jones (2019). “Factiva – Global News Monitoring & Search Engine.” Dow Jones. (December 2, 2019).
- Elsevier (2019). “Scopus.” (December 3, 2019).
- Google (2018). “Google Trends – The Homepage Explained – Trends Help.” (January 20, 2020).
- Holynska, Khrystyna, Yevhen Sapolovych, Mikhail Akimov, and Stephan De Spiegeleire (2019). “Events Datasets and Strategic Monitoring: Method Piece” (Forthcoming). The Hague Centre For Strategic Studies.
- Levada-Center (2019). “Levada Center.” (December 3, 2019).
- Moyer, Jonathan D., Tim Sweijs, Mathew J. Burrows, and Hugo Van Manen (2018) “Power and Influence in a Globalized World.” Atlantic Council. (November 26, 2019).
- OU Event Data (2018). “Terrier (Temporally Extended, Regular, Reproducible International Event Records)”. (January 29, 2020).
- The GDELT Project (2015). “GDELT 2.0: Our Global World in Realtime.” GDELT Blog. (October 11, 2018).
- Zimmerman, William, Sharon Werning Rivera, and Kirill Kalinin. (2019). “Survey of Russian Elites, Moscow, Russia, 1993-2016”. Version 6.” (November 26, 2019).
- Президент России (2019). “Президент России.” Президент России. (November 26, 2019).
Russia’s Real Cost of Crimean Uncertainty
The annexation of Crimea has real costs to the Russian economy beyond what is measured by some items in the armed forces’ budget; social spending in the occupied territories; or the cost of building a rather extreme bridge to solve logistics issues. Russia’s real cost of the annexation of Crimea is also associated with the permanent loss of income that the entire Russian population is experiencing due to increased uncertainty, reduced capital flows and investment, and thus a growth rate that is significantly lower than it would have been otherwise. Since the years of lost growth are extremely hard to make up for in later years, there will be a permanent loss of income in Russia that is a significant part of the real cost of annexing Crimea and continuing the fighting in Eastern Ukraine. It is time to stop not only the human bleeding associated with Ukraine, but also the economic.
Estimating the real cost of Russia’s annexation of Crimea and the continued involvement in Eastern Ukraine is complicated since there are many other things going on in the Russian economy at the same time. In particular, oil prices fell from over $100/barrel in late 2013 to $30/barrel in 2016 (Figure 1). Becker (2016) has shown that 60-80 percent of the variation in GDP growth can be explained by changes in oil prices, so this makes it hard to just look at actual data on growth to assess the impact of Crimea and subsequent sanctions and counter sanctions.
Figure 1. Russian GDP and oil price

Source: Becker (2019)
The approach here is instead to focus on one channel that is likely to be important for growth in these circumstances, which is uncertainty and its impact on capital flows and investment.
From uncertainty to growth
The analysis presented here is based on several steps that link uncertainty to GDP growth. All the details of the steps in this analysis are explained at some length in Becker (2019). Although this brief will focus on the main assumptions and estimates that are needed to arrive at the real cost of Crimea, a short description of the steps is as follows.
First of all, in line with basic models of capital flows, investors that can move their money across different markets (here countries) will look at relative returns and volatility between different markets. When relative uncertainty goes up in one market, capital will leave that market.
The next step is that international capital flows affect investment in the domestic market. If capital leaves a country, less money will be available for fixed capital investments.
The final step is that domestic investments is important for growth. Mechanically, in a static, national accounts setting, if investments go down, so does GDP. More long term and dynamically, investments have a supply side effect on growth, and if investments are low, this will affect potential as well as actual growth negatively.
These steps are rather straightforward and saying that uncertainty created by the annexation of Crimea leads to lower growth is trivial. What is not trivial is to provide an actual number on how much growth may have been affected. This requires estimates of a number of coefficients that is the empirical counterparts to the theoretical steps outlined here.
Estimates to link uncertainty to growth
In short, we need three coefficients that link: domestic investments to growth; capital flows to domestic investments; and uncertainty to capital flows.
There are many studies that look at the determinants of growth, so there are plenty of estimates on the first of these coefficients. Here we will use the estimate of Levine and Renelt (1992), that focus on finding robust determinants of growth from a large set of potential explanatory variables. In their preferred specification, growth is explained well by four variables, initial income, population growth, secondary education and the investments to GDP ratio. The coefficient on the latter is 17.5, which means that when the investment to GDP ratio increases by 10 percentage points, GDP grows an extra 1.75 percentage points per year. Becker and Olofsgård (2018) have shown that this model explains the growth experience of 25 transition countries including Russia since 2000 very well, which makes this estimate relevant for the current calculation.
The next coefficient links capital flows to domestic investments. This is also a subject that has been studied in many empirical papers. Recent estimates for transition countries and Russia in Mileva (2008) and Becker (2019) find an effect of FDI on domestic investments that is larger than one, i.e., there are positive spillovers from FDI inflows to domestic investments. Here we will use the estimate from Becker (2019) that finds that 10 extra dollars of FDI inflows are associated with an increase of domestic investments of 15 dollars.
Finally, we need an estimate linking uncertainty with capital flows. There are many studies looking at risk, return and investment in general, and also several studies focusing on international capital flows and uncertainty. Julio and Yook (2016) look at how political uncertainty around elections affect FDI of US firms and find that FDI to countries with high institutional quality is less affected by electoral uncertainty than others. Becker (2019) estimates how volatility in the Russian stock market index RTS relative to the volatility in the US market’s S&P 500 is associated with net private capital outflows. The estimate suggests that when volatility in the RTS goes up by one standard deviation, this is associated with net private capital outflows of $30 billion.
These estimates now only need one more thing to allow us to estimate how much Crimean uncertainty has impacted growth and this is a measure of the volatility that was created by the annexation of Crimea.
Measuring Crimean uncertainty
In Becker (2019), the measure of volatility that is used in the regression with net capital outflows is the 60-day volatility of the RTS index. Since we now want to isolate the uncertainty created by Crimea related events, we need to take out the volatility that can be explained by other factors in order to arrive at a volatility measure that captures Crimean induced uncertainty. In Becker (2019) this is done by running a regression of RTS volatility on the volatility of international oil prices and the US stock market as represented by the S&P 500. The residual that remains after this regression is the excess volatility of the RTS that cannot be explained by these two external factors. The excess volatility of the RTS index is shown in figure 2.
It is clear that the major peaks in excess volatility are linked to Crimea related events, and in particular to the sanctions introduced at various points in time. From March 2014 to March 2015, there is an average excess volatility of 0.73 standard deviations with a peak of almost 4 when the EU and the USA ban trade with Crimea. This excess volatility is our measure of the uncertainty created by the annexation of Crimea.
Figure 2. RTS excess volatility

Source: Becker (2019)
From Crimean uncertainty to growth
The final step is simply to use our measure of Crimean induced uncertainty together with the estimates that link uncertainty in general to growth.
The estimated excess volatility associated with Crimea is conservatively estimated at 0.7 standard deviations. Using this with the estimate that increasing volatility by one standard deviation is associated with $30 billion in capital outflows, we get that the Crimean uncertainty would lead to $21 billions of capital outflows in one quarter or $84 billions in one year. If this is in the form of reduced FDI flows, we have estimated that this means that domestic investments would fall by a factor of 1.5 or $126 billions.
In this period, Russia had a GDP of $1849bn and fixed capital investments of $392bn. This means that $126 billions in reduced investments correspond to a reduction in the investments to GDP ratio of 7 percentage points (or that the investments to GDP ratio goes from around 21 percent to 14 percent).
Finally, using the estimate of 17.5 from Levine and Renelt, this implies that GDP growth would have been 1.2 percentage points higher without the estimated decline in investments to GDP.
In other words, the Crimean induced uncertainty is estimated to have led to a significant loss of growth that has to be added to all the other costs of the annexation of Crimea and continued fighting in Eastern Ukraine. Note that recent growth in Russia has been just barely above 1 percent per year, so this means that growth has been cut in half by this self-generated uncertainty.
Of course, the 1.2 percentage point estimate of lost growth is based on many model assumptions, but it provides a more sensible estimate of the cost of Crimea than we can get by looking at actual data that is a mix of many other factors that have impacted capital flows, investments and growth over this period.
Policy conclusions
The annexation of Crimea and continued fighting in Eastern Ukraine carry great costs in terms of human suffering. In addition, they also carry real costs to the Russian economy. Not least to people in Russia that see that their incomes are not growing in line with other countries in the world while the value of their rubles has been cut in half. Some of this is due to falling oil prices and other global factors that require reforms that will reorient the economy from natural resource extraction to a more diversified base of income generation. This process will take time even in the best of worlds.
However, one “reform” that can be implemented over night is to stop the fighting in Eastern Ukraine and work with Ukraine and other parties to get out of the current situation of sanctions and counter-sanctions. This would provide a much-needed boost to foreign and domestic investments required to generate high, sustainable growth to the benefit of many Russians as well as neighboring countries looking for a strong economy to do trade and business with.
References
- Becker, T, (2019), “Russia’s macroeconomy—a closer look at growth, investment, and uncertainty”, forthcoming SITE Working paper.
- Becker, T. and A. Olofsgård, (2018), “From abnormal to normal—Two tales of growth from 25 years of transition”, Economics of Transition, vol. 26, issue 4.
- Becker, T. (2016), “Russia and Oil – Out of Control”, FREE policy brief, October.
- Julio, B. and Yook, Y. (2016), ‘Policy uncertainty, irreversibility, and cross-border flows of capital’, Journal of International Economics, Vol. 103, pp. 13-26.
- Levine, R. and Renelt, D. (1992). ‘A Sensitivity Analysis of Cross-Country Growth Regressions’, American Economic Review, 82(4), pp. 942–963.
- Mileva, E. (2008), ‘The Impact of Capital Flows on Domestic Investment in Transition Economies, ECB Working Paper No. 871, February.
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.
Labor Market Adaptation of Internally Displaced People: The Ukrainian Experience
This brief is based on research that investigates the probability of employment among displaced and non-displaced households in a region bordering territory with an ongoing military conflict in Eastern Ukraine. According to the results, internally displaced persons (IDP) are more educated, younger and more active in their job search than locals. Nevertheless, displaced individuals, particularly males, have experienced heavy discrimination. After controlling for personal characteristics, the structure of the household, location, non-labour incomes and endogeneity of displacement, IDP males are 17% less likely to be formally employed two years after resettlement than locals.
Internally displaced persons in Ukraine
In 2014, 23 years after independence, Ukraine suddenly found itself among the top-ten of countries with the largest internally displaced population. During the period 2014–2016, 1.8 million persons registered as internally displaced. Potentially, about 1 million more reallocated to Russia and about 100,000 to other countries nearby, where they sought refugee or labour migrant status (Smal, 2016).
The Ministry of Social Policy of Ukraine (MSPU) has regularly published very general reports on displaced persons. According to these reports, at the end of February 2016, the internally displaced persons in Ukraine included 22,000 individuals from Crimea and over 1.7 million citizens from Eastern Ukraine. These are mostly individuals who registered as IDPs to qualify for financial assistance from the state and some non-monetary benefits. Among them, 60% are retired people, 23.1% are individuals of working age, 12.8% are children and 4.1% are people with disabilities (Smal and Poznyak, 2017). In fact, the MSPU registers not only displaced persons but also those who de facto live in the occupied territories and occasionally travel to territories controlled by the Ukrainian authorities to receive their pension or social benefits (so called ‘pension tourism’). On the other hand, some IDPs did not register either to avoid bureaucracy or because they were unable to prove their status due to lack of documents. Recent publications that are based on surveys portray a more balanced distribution: 15% are retired people, 58% are individuals of working age, 27% are children and 13% are people with disabilities (IOM and the Ukrainian Centre for Social Reforms, 2018).
Only limited information is available about IDPs’ labour market activity. According to the State Employment Service (SES), between March 2014 and January 2016, only 64,300 IDPs or 3.75% referred to the SES for assistance (Smal and Poznyak, 2017). On the one hand, this figure reflects the relatively low reliance of displaced Ukrainians on the SES services in their job search. On the other hand, the geographical variation in the share of SES applicants suggests that Ukraine’s IDPs who moved further from the war zone and their homes were more active in trying to find a job.
Data
Our primary data were collected in June–August 2016 by REACH and provided by the Ukraine Food Security Cluster (UFSC) as a part of the needs assessment in Luhansk and Donetsk oblasts of Ukraine – two regions that were directly affected by the conflict. These two regions have hosted roughly 53% of all IDPs in Ukraine (Smal and Poznyak, 2017). We argue that households that did not move far from the place of conflict are most likely to be driven by conflict only, while long-distance movers may combine economic and forced displacement motives.
The data set offers information on 2500 households interviewed in 233 locations and is statistically representative of the average household in each oblast. It includes respondents currently living in their pre-conflict settlements (non-displaced, NDs) and respondents who report a different place of residence before the conflict (IDPs). The IDP group comprises individuals with registered and unregistered status and from both sides of the current contact line. The non-IDP group includes only households living on the territory controlled by the Ukrainian Government that did not move after the conflict had started.
Our sample covers 1,135 displaced households that came from 131 settlements. Most of the reallocations took place in early summer 2014 with the military escalation of the conflict in Eastern Ukraine. Thus, the average duration of displacement up to the moment of the interview was 637 days (or 21 months). This is a sufficiently long period for adaptation and job search. However, there is enough variation in this indicator – some families left as early as March–April 2014, while others were displaced in June 2016, just a few days before the interviews started.
Results
Simple comparison shows that heads of displaced households are on average almost four years younger than those of non-displaced households (Table 1). In terms of education, displaced households are found to be more educated than non-displaced households, as there are significantly more IDP household heads with tertiary education and significantly fewer individuals with only primary, secondary or vocational degrees. In particular, 37% of IDP household heads hold a university degree compared with 22% of household heads among the local population. This seems to suggest positive displacement selection. IDPs are slightly more likely to be headed by females and unmarried persons, although these differences are statistically insignificant. Displaced households include more children aged under five (0.35 vs. 0.22 children per non-displaced household) and 6 to 17 years (0.42 vs. 0.34, respectively) and fewer members aged over 60 years (0.58 vs 0.66, respectively). There is no difference in the number of working-age adults or disabled individuals per household among IDPs and non-IDPs. The average household size is statistically similar for the groups (2.74 vs. 2.65 persons per IDP and non-IDP household, respectively).
Table 1. Selected descriptive statistics
| Internally displaced households | Non- displaced households | |
| Household head employed | 0.43*** | 0.48*** |
| Household head characteristics | ||
| Age (years) | 48.10*** | 52.85*** |
| Male | 0.49 | 0.52 |
| Education | ||
| vocational | 0.42*** | 0.49*** |
| university | 0.37*** | 0.22*** |
| Household characteristics | ||
| Size (persons) | 2.74 | 2.65 |
| Number of children 0-5 | 0.35*** | 0.21*** |
| Number of children 6-17 | 0.42*** | 0.34*** |
| Number of members 60+ | 0.58** | 0.66** |
| IDP payments | 0.50*** | 0*** |
| Humanitarian assistance | 0.78*** | 0.28*** |
There are further differences in the types of economic activity and occupations among IDPs and non-IDPs. Prior to the conflict, displaced respondents were more likely (than non-displaced persons) to be employed as managers or professionals and less likely to hold positions as factory or skilled agricultural workers. This result also speaks in favor of a positive displacement selection story.
As expected, the conflict has had a negative effect on human capital in the government controlled areas of Donetsk and Luhansk regions. We observe some deskilling at the time of the interviews, which is especially pronounced for IDPs. In particular, the share of managers among the IDPs had reduced from 12% to 5% and that of technicians from 15% to 12%, while the proportion of service and sales employees had increased from 10% to 13%, that of factory workers from 11% to 15% and that of skilled agricultural workers from 2% to 6%.
Considering the economic activity in the current location, we can note that on average the heads of displaced households are 5% less likely to be employed than those of non-displaced households (43% vs. 48%, respectively). In both groups, a large share of respondents report difficulties in their job search, but IDPs are 13% more likely to experience this problem. They report changing their pre-conflict occupation three times more often than non-IDPs (37% vs. 11%).
Government and non-government assistance may also drive the differences in employment. Economic theory states that individuals are less likely to work if they have some backup in the form of non-labour earnings. Financial support and humanitarian assistance are widely used to smooth a displacement shock. At the same time, improperly designed assistance schemes may reduce the stimulus to search for a job.
IDPs are 9% less likely to include earnings in their household’s top three main sources of income than the non-displaced population (46% vs. 55%, respectively), meaning that they rely more on various social payments and pensions. In addition, displaced households may be slightly more reluctant to search for a job due to displacement assistance from the government (received by 50% of IDPs compared with 0% for non-IDP households), although the amounts are quite modest. According to the existing legislation, IDPs can receive regular monthly state payments and one-time state payments. Regular monthly payments can be received by any IDP and cannot exceed UAH 3,000 (~$111) for an ordinary household, UAH 3,400 for a household with disabled people and UAH 5,000 (~$185) for a household with more than 2 children. Eligibility and the size of the one-time payment are determined by the local government. In the data set, 95% of IDPs receive less than UAH 3,000 while the 2016 average monthly wage was UAH 6,000 in Donetsk and UAH 4,600 in Luhansk regions.
In addition, IDPs are three times more likely to receive humanitarian assistance (78% vs. 28% among displaced and non-displaced persons, respectively). This support includes mostly food and winterisation items but also cash (26% among displaced vs. 12% among non-displaced assistance receivers). On the other hand, to cover reallocation and adaptation costs, some IDPs use their financial reserves, and as a result they are by 10 p.p. more likely to report no or already depleted savings. This may increase their stimulus to engage in a more active job search.
After taking into account the observed and unobserved differences between the groups as well as controlling for the location fixed effect, we find that the difference in the probability of employment between displaced and non-displaced persons increases from a casually observed slit of 5% to a chasm of 17.3%. This result suggests that IDPs are [negatively] discriminated despite being younger, more educated, skilled and more ‘able’ in the labour market. Specifically, 7 out of 17 p.p. (41% of the gap) are due to the variation in observed household head characteristics and family composition, while unobserved displacement-related features (such as attitude towards change, activism, mental and physical ability to reallocate) account for 5 p.p. (29%) of the gap. Controlling for particularities of a current location does not substantially affect the estimated differences.
Figure 1. Main results
We re-estimate these regressions using an employment indicator that includes both formal and informal employment (as defined by the respondents), accounting for occasional and irregular employment, including subsistence agricultural work. Since informal work is more common among IDPs, this definition of employment leads to a reduction in the average casually observed gap from 5% to 3%. However, after controlling for all the factors, we obtain the same result – a 17.8% difference between displaced and non-displaced households.
Conclusion
Policy makers and international donors should not be misled by the seemingly comparable probability of employment among IDPs and non-IDPs based on simple statistics. The average 0–5% difference in unconditional employment rates conceals the actual 17% gap in the likelihood of having a job. The contribution of unobserved displacement-related factors in hiding the true gap is large, especially for males seeking formal employment. Without adjusting for it, we would underestimate the real difference in employment probability by one-third to one-half.
Our study produces firm evidence that displaced individuals in Ukraine, particularly males, have been discriminated against in terms of employment. Our results further suggest that male heads of displaced households experience more discrimination in the formal labour market, while the situation is the opposite for females, who are more likely to face unequal treatment in the informal sector. Policy makers and volunteers should take this difference into account in the adaptation of male- and female-headed households.
Humanitarian assistance to displaced individuals was found to have no negative effect on their employment, which suggests that it is provided in an effective manner. Thus, this tool can be used to mitigate the discrimination.
References
- IOM and the Ukrainian Centre for Social Reforms. (2018). ’National Monitoring System Report on the Situation of Internally Displaced Persons.’
- Smal, V. and O. Poznyak. (2017) ‘Internally displaced persons: social and economic integration in hosting communities’, PLEDDG Project.
- Smal, V. 2016. ’Внутрішньо Переміщені Особи: Соціальна та економічна інтеграція в приймаючих громадах.’
- Vakhitova, H. and P. Iavorskyi, “Employment of Displaced and Non-displaced Households in Luhansk and Donetsk Oblasts”, Europe-Asia studies, (forthcoming).
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.
Conflict, Minorities and Well-Being
We assess the effect of the Russo-Georgian conflict of 2008 and the Ukrainian-Russian conflict of 2014 on the well-being of minorities in Russia. Using the Russian Longitudinal Monitoring Survey (RLMS), we find that the well-being of Georgians in Russia suffered negatively from the 2008 Russo-Georgian conflict. In comparison, we find no general effect of the Ukrainian-Russian conflict of 2014 on the Ukrainian nationals’ happiness. However, the life satisfaction of Ukrainians who reside in the southern regions of Russia in close proximity to Ukraine is negatively affected. We also show that the negative effect of conflict is short-lived with no long-term legacy. Additionally, we analyze the spillover effect of conflict on other minorities in Russia. We find that while the well-being of non-Slavic and migrant minorities who have recently moved to Russia is negatively affected, there is no effect on local minorities who have been living in Russia for at least ten years.
Militarized conflict affects a myriad of socioeconomic outcomes, such as the level of GDP (Bove et al. 2016), household welfare (Justino 2011), generalized trust and trust in central institutions (Grosjean 2014), social capital (Guriev and Melnikov 2016), and election turnout (Coupe and Obrizan 2016). Importantly, conflict has also been found to directly affect individual well-being (Frey 2012, Welsch 2008).
However, previous research studying individual well-being in transition countries largely abstracts from heightened political instability and conflict proneness, while this has been particularly pertinent in transition countries. Examples of transition countries facing various types of conflicts are abound, such as Yugoslavia, Ukraine, Tajikistan, Russia, Armenia, Azerbaijan, Moldova, and so on. Therefore, it is imperative to explore how conflict shapes well-being in transition countries.
In a new paper (Gokmen and Yakovlev, forthcoming), we add to our understanding of well-being in transition in relation to conflict. We focus on the effect of Russo-Georgian conflict of 2008 and the Ukrainian-Russian conflict of 2014 on the well-being of minorities in Russia. The results suggest that the well-being of Georgians in Russia suffered negatively from the 2008 Russo-Georgian conflict. However, we find no general effect of the Ukrainian-Russian conflict of 2014 on the Ukrainian nationals’ happiness, while the life satisfaction of Ukrainians who reside in the southern regions of Russia in close proximity to Ukraine is negatively affected. Additionally, we analyze the spillover effect of conflict on other minorities in Russia. We find that while the well-being of non-slavic and migrant minorities who have recently moved to Russia is negatively affected, there is no effect on local minorities who have been living in Russia for at least ten years.
Data and Results
We employ the Russian Longitudinal Monitoring Survey (RLMS) which contains data on small neighborhoods where respondents live. Starting from 1992, the RLMS provides nationally-representative annual surveys that cover more than 4000 households with 10000 to 22000 individual respondents. The RLMS surveys comprise a broad set of questions, including a variety of individual demographic characteristics, health status, and well-being. Our study utilizes rounds 9 through 24 of the RLMS from 2000 to 2015.
In this survey, we identify minorities with the question of “What nationality do you consider yourself?” Accordingly, anybody who answers this question with a non-Russian nationality is assigned to that minority group.
We employ three measures of well-being. Our main outcome variable is “life satisfaction.” The life satisfaction question is as follows: “To what extent are you satisfied with your life in general at the present time?”, and evaluated on a 1-5 scale from not at all satisfied to fully satisfied. Additionally, we use “job satisfaction” and “health evaluation” as outcomes of well-being.
Our results suggest that our primary indicator of well-being, life satisfaction, for Georgian nationals has gone down in the Russo-Georgian conflict year of 2008 compared to the Russian majority (see Figure 1). The magnitude of the drop in life satisfaction is about 39 percent of the mean life satisfaction. Our estimates for the other two well-being indicators, job satisfaction and health evaluation, also indicate a dip in the conflict year of 2008. Lastly, our estimates show that the negative impact of the conflict does not last long. Although there is a reduction in the well-being of Georgians both on impact in 2008 and in the immediate aftermath in 2009, the rest of the period until 2015 is no different from the pre-2008 period.
Figure 1. Life Satisfaction of Georgian Nationals in Russia

Source: Authors’ own construction based on RLMS data and diff-in-diff estimates.
Furthermore, when we investigate the effect of the Ukrainian-Russian conflict of 2014, we find no negative effect on the life satisfaction of Ukrainians. One explanation for why the happiness of Ukrainians in Russia does not seem to be negatively affected in 2014 is that the degree of integration of Ukrainians into the Russian society is much stronger than the degree of integration of Georgians. On the other hand, our heterogeneity analysis reveals that in the southern parts of Russia closer to the Ukrainian border, where there are more Ukrainians who have ties to Ukraine, Ukrainian nationals are differentially more negatively affected by the 2014 conflict. The differential reduction in the happiness of Ukrainians is about 19 percent of the mean life satisfaction.
Moreover, we also look into whether there is any spillover effects of the Russo-Georgian and the Ukrainian-Russian conflicts on the well-being of other minorities. We first carry out a simple exercise on non-Slavic minorities of Russia. We pick the sample of non-Slavic ex-USSR nationals that are similar to Georgians in their somatic characteristics, such as hair color and complexion. This group of people include the nationals of Azerbaijan, Kazakhstan, Uzbekistan, Kyrgyzstan, Turkmenistan and Tajikistan. We treat this group as “the countries with predominantly non-Slavic population” as their predominant populations are somatically different from the majority Russians, and thus, might either have been subject to discrimination or might have feared a minority backlash to themselves during the times of conflict. This conjecture finds some support below in Figure 2 in terms of violence against minorities. We observe in Figure 2 that hate crimes and murders based on nationality and race peak in 2008.
Our estimates also support the above hypothesis and propose that there is some negative effect of the 2008 conflict on non-slavic minorities’ happiness as well as their job satisfaction, whereas 2014 conflict has no effect.
Figure 2. Hate Murders in Russia over Time
Source: Sova Center
Next, we investigate the spillover effects of conflict on Migrant Minorities. Migrant minorities are minorities who have been living in their residents in Russia for less than 10 years. We conjecture that these minorities, as opposed to the minorities who have been in place for a long time, could be more susceptible to any internal or external conflict between Russia and some other minority group for fear that they themselves could also be affected. Whereas other types of longer-term resident minorities, which we call Local Minorities, are probably less vulnerable since they have had more time to establish their networks, job security, and most likely also have Russian citizenship. Our estimates back up the above conjecture and demonstrate that migrant minorities suffer negatively from the spillover effects of the 2008 conflict onto their well-being captured by any of the three measures, and not from the 2014 conflict, whereas there is no negative impact on local minorities.
Conclusion
In this paper, instead of focusing on the direct impact of conflict on happiness in war-torn areas, we contribute to the discussion on conflict and well-being by scrutinizing the well-being of people whose country of origin experiences conflict, but they themselves are not in the war zone. Additionally, we show that some other minority groups also suffer from such negative spillovers of conflict. Being aware of such negative indirect effects of conflict on well-being is essential for policy makers, politicians and researchers. Most policy analyses ignore such indirect costs of conflict, and this study highlights the bleak fact that the cost of conflict on well-being is probably larger than it has been previously estimated.
References
- Bove, V.; L. Elia; and R. P. Smith, 2016. “On the heterogeneous consequences of civil war,” Oxford Economic Papers.
- Coupe, T.; and M. Obrizan, 2016. “Violence and political outcomes in Ukraine: Evidence from Sloviansk and Kramatorsk”, Journal of Comparative Economics, 44, 201-212.
- Frey, B. S., 2012. “Well-being and war”, International Review of Economics, 59, 363-375.
- Gokmen, Gunes; and Evgeny Yakovlev, forthcoming. “War and Well-Being in Transition: Evidence from Two Natural Experiments”, Journal of Comparative Economics.
- 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, 424-451.
- Guriev, S.; and N. Melnikov, 2016. “War, inflation, and social capital,” American Economic Review: Papers & Proceedings, 106, 230-35.
- Justino, P., 2011. “The impact of armed civil conflict on household welfare and policy,” IDS Working Papers.
- Welsch, H., 2008. “The social costs of civil conflict: Evidence from surveys of happiness” Kyklos, 61, 320-340.
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.

