Location: Ukraine

Securing Women’s Safety at the Time of War

20220308 Securing Women’s Safety

As the Russian invasion of Ukraine continues, millions of women are facing grave risks from displacement, violence, and loss. On this International Women’s Day, it is crucial to recognize the unique challenges women experience during armed conflicts — from direct violence to long-term psychological and economic harm. Evidence from past wars shows that gender-based violence increases sharply during and after conflicts, demanding urgent international attention and support.

Women’s Vulnerability During the Ukraine War

The war in Ukraine has caused immense human suffering, forcing over 1.5 million people to flee by early March 2022. Russian attacks have targeted cities, disrupted humanitarian aid, and endangered civilians. Research shows that women in war zones face multiple layers of risk — including sexual violence, psychological abuse, and displacement-related exploitation. Gender-based violence often extends beyond physical assault, encompassing coercion, loss of freedom, and systemic mistreatment (Wirtz et al., 2014).

Sexual Violence as a Weapon of War

Scholars now recognize sexual violence in armed conflicts as a deliberate tool of warfare rather than random brutality (Skjelsbaek, 2001). Studies indicate that aggressors from gender-unequal societies are more likely to use such violence (Taylor, 1999; Meger, 2016; Guarnieri & Tur-Prats, 2020). Even after fleeing, women face heightened threats in refugee camps, where sexual and domestic violence often persist (Araujo et al., 2019; Stark & Ager, 2011).

Protecting Women in Conflict and Displacement

Governments, humanitarian organizations, and the international community must prioritize women’s safety, justice, and empowerment. Key steps include:

  • Ensuring safe evacuation from conflict zones.
  • Holding perpetrators of sexual violence accountable, with zero tolerance for impunity.
  • Including sexual violence in sanctions regimes, per UN Security Council Resolution 1820.
  • Involving refugee women in leadership roles in protection programs.
  • Providing training and awareness on gender-based violence prevention.
  • Enabling legal work opportunities for displaced women to prevent exploitation.
  • Offering mental health and trauma support for survivors.

A Call for Global Solidarity

As we hope for peace and the safe return of displaced families, this International Women’s Day should serve as a call to action — to strengthen protection for women, prevent gender-based violence in conflict, and ensure justice for survivors.

The FREE Network and the Forum for Research on Gender Economics (FROGEE) continue to advocate for women’s safety and empowerment, supported by the Swedish International Development Cooperation Agency (SIDA).

References

The Sanctions on Russia, and Their Impact on the Region

20220303 The Sanctions on Russia Image

As fighting across Ukraine escalates and the international community reacts, Stockholm Institute of Transition Economics (SITE) and the FREE Network invite you to join the webinar “The sanctions on Russia, and their impact on the region” on 3 March, 17:00 – 18:00 CET Stockholm.

The Sanctions on Russia, and Their Impact on the Region

Torbjörn Becker, Director of SITE will be joined by Larry Samuelson, Professor at Yale and Cowles Foundation, Lev Lvovsky, BEROC Research Fellow, Nataliia Shapoval, Chairman of KSE Institute and Yaroslava V. Babych, Academic Director of ISET Policy Institute and other experts with extensive policy experience for a live discussion about the economic effects of sanctions in Russia and the region.

Registration

Everyone is invited to join the webinar. Please use the Zoom registration platform to register (click here). After registration, you will receive a confirmation email which includes the Zoom link and passcode. Please also check the spam folder, not to miss the registration access details.

Disclaimer: Opinions expressed during events, seminars and conferences are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

A War No One Wants? The Political Economy of the Russia-Ukraine Conflict

Russian soldiers in military truck convoy representing conflict between Russia and Ukraine

The Forum for Research on Eastern Europe and Emerging Economies (FREE Network) with two of its members, the Kyiv School of Economics (KSE) and the Stockholm Institute of Transition Economics (SITE), will host an online seminar and discussion on the risk of war between Russia and Ukraine and potential consequences of military confrontation.

The Risk of War Between Russia and Ukraine

How can so many think that there will be a war between Russia and Ukraine when it is so hard to see any winner in such a war. Can the political gains for Russia’s leaders really outweigh the loss of a good neighbour, significant economic sanctions that will undermine growth for years to come and the failure of a new gas connection to Europe? What is the logic of the President of Russia and how does Russian public opinion perceive the war? What will be the response to further aggression in Ukraine as well as in the rest of Europe and the US? There are many questions but few hard answers, but the event will provide some thinking on these and other issues.

Seminar Speakers

Registration

The seminar will take place on 17 February 2022, from 17.00-18.30, CET (Stockholm time). The seminar will be organised via the Zoom platform and will be open to the public through digital channels. However, registration is required. Please register via the Eventbrite registration platform (click here). The Zoom link and passcode will be sent to your registered email account a few hours before the start of the online seminar.

Disclaimer: Opinions expressed during events, seminars and conferences are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Gender Quotas: Pros, Cons, Pitfalls

20211008 Gender Quotas - Pros, Cons, Pitfalls Image 01

Local elections in Ukraine took place in 2020. These elections were unique due to gender quotas introduced in the election legislation. Experts and panellists at the KSE conference “Gender quotas: Pros, cons, pitfalls” discuss:

  • To what extent gender quotas were efficient?
  • Did more women win seats in local councils?
  • Did all parties comply with the new norm? 

On the one hand, the analysis shows the success of women in elections to regional councils. Compared to 2015, the share of women winners in regional councils has doubled from 14% to 28%. At the same time, the electoral system still creates obstacles for women candidates. In the village and rural councils, on the other hand, the share of women decreased from 46% and 55% to 37% and 41%, respectively.

Discussants of the future of gender quotas in Ukrainian politics: 

  • Viola von Cramon-Taubadel, Member of the European Parliament
  • Liliana Palihovici, Special Representative of the OSCE Chairperson-in-Office on Gender 
  • Natia Jikia, Deputy Director of NDI Ukraine
  • Olena Ivanchenko, Head of Regional Office for Kyiv Oblast at ULEAD 
  • Yevhenia Kravchenko, Khotiv council 
  • Yuliia Horodchanina, Poltava city council 
  • Iryna Yaremchuk, Ukrainian Galician Party, Chairman of the Ternopil City Organization 

Moderators: 

  • Tymofii Brik, Head of the center of sociological research, decentralization, and local development in KSE 
  • Iryna Tyshko,  Gender expert and Co-founder of the Public Alliance “Political action of women” and of the #electionsWITHOUTsexism project. 

Land Market and a Pre-emptive Right in Farmland Sales

20211025 Land Market Image 01

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

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.

Vaccination Progress and the Opening Up of Economies

20210622 Reopening Soon Webinar Image 01

In this brief, we report on the FREE network webinar on the state of vaccinations and the challenges ahead for opening up economies while containing the pandemic, held on June 22, 2021. The current state of the pandemic in each respective country was presented, suggesting that infection rates have gone down quite substantially recently in all countries of the network, except in Russia which is currently facing a surge in infections driven by the delta-version of the virus. Vaccination progress is very uneven, limited by lacking access to vaccines (primarily Ukraine and Georgia) and vaccine scepticism among the population (primarily in Russia and Belarus but for certain groups also in Latvia, Poland and to some extent Sweden). This also creates challenges for governments eager to open their societies to benefit their economies and ease the social consequences of the restrictions on mobility and social gatherings. Finally, the medium to long term consequences for labour markets reveal challenges but also potential opportunities through wider availability of workfrom-home policies. 

Background

In many countries in Europe, citizens and governments are starting to see an end to the most intense impact of the Covid-19 pandemic on their societies. Infection and death rates are coming down and governments are starting to put in place policies for a gradual opening up of societies, as reflected in the Covid-19 stringency index developed by Oxford University. These developments are partially seasonal, but also largely a function of the progress of vaccination programs reaching an increasing share of the adult population. These developments, though, are taking place to different degrees and at different pace across countries.  This is very evident at a global level, but also within Europe and among the countries represented in the FREE network. This has implications for the development within Europe as a whole, but also for the persistent inequalities we see across countries.   

Short overview of the current situation

The current epidemiological situation in Latvia, Sweden, Ukraine, and Georgia looks pretty similar in terms of Covid-19 cases and deaths but when it comes to the vaccination status there is substantial variation.

Latvia experienced a somewhat weaker third wave in the spring of 2021 after being hit badly in the second wave during the fall and winter of 2020 (see Figure 1). The Latvian government started vaccinating at the beginning of 2021, and by early June, 26% of the Latvian population had been fully vaccinated.

Sweden, that chose a somewhat controversial strategy to the pandemic built on individual responsibility, had reached almost 15 thousand Covid-19 deaths by the end of June of 2021, the second highest among the FREE network member countries relative to population size. The spread of the pandemic has slowed down substantially, though, during the early summer, and the percentage of fully vaccinated is about to reach 30% of the population.

Figure 1. Cumulative Covid-19 deaths 

Source: Aggregated data sources from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, compiled by Our World in Data.

Following a severe second wave, the number of infected in Ukraine started to go down in the winter of 2020, with the total deaths settling at about 27 thousand in the month of February. Then the third wave hit in the spring, but the number of new daily cases has decreased again and is currently three times lower than at the beginning of the lastwave. However, a large part of the reduction is likely not thanks to successful epidemiological policies but rather due to low detection rates and seasonal variation

In June 2021, Georgia faces a similar situation as Ukraine and Latvia, with the number of cumulative Covid-19 deaths per million inhabitants reaching around 1300 (in total 2500 people) following a rather detrimental spring 2021 wave. At the moment, both Georgia and Ukraine have very low vaccination coverage relative to other countries in the region(see Figure 5).

In contrast to the above countries, Russia started vaccinating early. Unfortunately, the country is now experiencing an increase in the number of cases (as can be seen in Figure 2), contrary to most other countries in the region. This negative development is likely due to the fact that the new Covid-19 delta variant is spreading in the country, particularly in Moscow and St. Petersburg. Despite the early start to vaccinations, though, the total number of vaccinated people remains low, only reaching 10.5% of the population.

Figure 2. New Covid-19 cases

Source: Aggregated data sources from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, compiled by Our World in Data.

In some ways similar to Sweden, the government of Belarus did not impose any formal restrictions on individuals’ mobility. According to the official statistics, in the month of June, the rise in the cumulative number of covid-19 deaths and new daily infections has declined rapidly and reached about 400 deceased and 800 infections per one million inhabitants, respectively. Vaccination goes slowly, and by now, around 8% of the population has gotten the first dose and 5% have received the second.

There were two major waves in Poland during the autumn 2020 and spring 2021. In the latter period, the country experienced a vast number of deaths.  As can be seen in Figure 3, the excess mortality P-score – the percentage difference between the weekly number of deaths in 2020-2021 and the average number of deaths over the years 2015-2019 – peaked in November 2020, reaching approximately 115%. The excess deaths numbers in Poland were also the highest among the FREE Network countries in the Spring of 2021, culminating at about 70% higher compared to the baseline. By mid-June, the number of deaths and cases have steeply declined and 36% of the country’s population is fully vaccinated.

Figure 3. Excess deaths

Turning to the economy, after a devastating year, almost all countries are expected to bounce back by the end of 2021 according to the IMF (see Figure 4). Much of these predictions build on the expectations that governments across the region will lift Covid-19 restrictions. These forecasts may not be unrealistic for the countries where vaccinations have come relatively far and restrictions have started to ease. However, for countries where vaccination rates remain low and new variations of the virus is spreading, the downside risk is still very present, and forecasts contain much uncertainty.

 Figure 4. GDP-growth

Vaccination challenges

Since immunization plays such a central role in re-opening the economy and society going back to normal, issues related to vaccinations were an important and recurring topic at the event. The variation in progress and speed is substantial across the countries, though.

Ukraine and Georgia are still facing big challenges with vaccine availability and have fully vaccinated only 1.3% and 2.3% of the population by the end of June, respectively. Vaccination rates have in the recent month started to pick up, but both countries face an uphill battle before reaching levels close to the more successful countries.

Figure 5. Percent fully vaccinated

Other countries a bit further ahead in the vaccine race are still facing difficulties in increasing the vaccination coverage, though not so much due to lack of availability but instead because of vaccine skepticism. In Belarus, a country that initially had bottleneck issues similar to Ukraine and Georgia, all citizens have the opportunity to get vaccinated. However, Lev Lvovskiy, Senior Research Fellow at BEROC in Belarus, argued that vaccination rates are still low largely because many Belarusians feel reluctant towards the vaccine at offer (Sputnik V).

This vaccination scepticism turns out to be a common theme in many countries. According to different survey results presented by the participants at the webinar, the percentage of people willing or planning to get vaccinated is 30% in Belarus and 44% in Russia. In Latvia, this number also varies significantly across different groups as vaccination rates are significantly lower among older age cohorts and in regions with a higher share of Russian-speaking residents, according to Sergejs Gubins, Research Fellow at BICEPS in Latvia.

Webinar participants discussed potential solutions to these issues. First, there seemed to be consensus that offering people the opportunity to choose which vaccine they get will likely be effective in increasing the uptake rate. Second, governments need to improve their communication regarding the benefits of vaccinations to the public. Several countries in the region, such as Poland and Belarus, have had statements made by officials that deviate from one another, potentially harming the government’s credibility with regards to vaccine recommendations. In Belarus, there have even been government sponsored disinformation campaigns against particular vaccines. In Latvia, the main problem is rather the need to reach and convince groups who are generally more reluctant to get vaccinated. Iurii Ganychenko, Senior Researcher at KSE in Ukraine, exemplified how Ukraine has attempted to overcome this problem by launching campaigns specifically designed to persuade certain age cohorts to get vaccinated. Natalya Volchkova, Director of CEFIR at NES in Russia, argued that new, more modern channels of information, such as professional influencers, need to be explored and that the current model of information delivery is not working.

Giorgi Papava, Lead Economist at ISET PI in Georgia, suggested that researchers can contribute to solving vaccine uptake issues by studying incentive mechanisms such as monetary rewards for those taking the vaccine, for instance in the form of lottery tickets. 

Labour markets looking forward

Participants at the webinar also discussed how the pandemic has affected labour markets and whether its consequences will bring about any long-term changes.

Regarding unemployment statistics, Michal Myck, the Director of CenEA in Poland, made the important point that some of the relatively low unemployment numbers that we have seen in the region during this pandemic are misleading. This is because the traditional definition of being unemployed implies that an individual is actively searching for work, and lockdowns and other mobility restrictions have limited this possibility. Official data on unemployment thus underestimates the drop in employment that has happened, as those losing their jobs in many cases have left the labour market altogether. We thus need to see how labor markets will develop in the next couple of months as economies open up to give a more precise verdict.

Jesper Roine, Professor at SITE in Sweden, stressed that unemployment will be the biggest challenge for Sweden since its economy depends on high labor force participation and high employment rates. He explained that the pandemic and economic crisis has disproportionately affected the labor market status of certain groups. Foreign-born and young people, two groups with relatively high unemployment rates already prior to the pandemic, have become unemployed to an even greater extent. Many are worried that these groups will face issues with re-entering the labour market as in particular long-term unemployment has increased. At the same time, there have been more positive discussions about structural changes to the labour market following the pandemic. Particularly how more employers will allow for distance work, a step already confirmed by several large Swedish firms for instance.

In Russia, a country with a labour market that allowed for very little distance work before the pandemic, similar discussions are now taking place. Natalya Volchkova reported that, in Russia, the number of vacancies which assumed distance-work increased by 10% each month starting from last year, according to one of Russia’s leading job-search platforms HeadHunter. These developments could be particularly beneficial for the regional development in Russia, as firms in more remote regions can hire workers living in other parts of the country.

Concluding Remarks

It has been over a year since the Covid-19 virus was declared a pandemic by the World Health Organization. This webinar highlighted that, though vaccination campaigns in principle have been rolled out across the region, their reach varies greatly, and countries are facing different challenges of re-opening and recovering from the pandemic recession. Ukraine and Georgia have gotten a very slow start to their vaccination effort due to a combination of lack of access to vaccines and vaccine skepticism. Countries like Belarus and Latvia have had better access to vaccines but are suffering from widespread vaccine skepticism, in particular in some segments of the population and to certain vaccines. Russia, which is also dealing with a broad reluctance towards vaccines, is on top of that dealing with a surge in infections caused by the delta-version of the virus.

IMF Economic Outlook suggests that most economies in the region are expected to bounce back in their GDP growth in 2021. While this positive prognosis is encouraging, the webinar reminded us that there is a great deal of uncertainty remaining not only from an epidemiological perspective but also in terms of the medium to long-term economic consequences of the pandemic.

Participants

  • Iurii Ganychenko, Senior Researcher at Kyiv School of Economics (KSE/Ukraine)
  • Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS/ Latvia)
  • Natalya Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR at NES/ Russia)
  • Giorgi Papava, Lead Economist at the ISET Policy Institute (ISET PI/ Georgia)
  • Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC/ Belarus)
  • Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
  • Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
  • Anders Olofsgård, Deputy Director of SITE and Associate Professor at the Stockholm School of Economics (SITE / Sweden)

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.

Creative Industries: Impact on the Development of Ukraine’s Economy

Image of coloured umbrellas representing impact of creative industries

This brief is based on research investigating the effects of creative industries on the development of the Ukrainian economy. The results indicate that capital investment in creative industries has a significantly greater effect on economic growth than a simple increase in the consumption of the respective industry’s products. Thus, we conclude that to achieve a more substantial economic effect of spending in creative industries, it is necessary not only to increase the expenditures in these industries and boost consumption of their products but also to support these industries in developing production capacity. The underlying study “Creative Industries: Impact on the Development of Ukraine’s Economy” was prepared by the Kyiv School of Economics in cooperation with the Ministry of Culture and Information Policy of Ukraine. The first results from the study were presented at the international forum “Creative Ukraine” in 2020.

Background

In 2019, the United Nations (UN) General Assembly declared 2021 as the International Year of Creative Economy for Sustainable Development. This nomination was a recognition of the growing role of creative industries in the economic development of both developed and developing countries. The program of events taking place under the theme of the International Year of the Creative Economy for Sustainable Development includes forums, conferences, and intergovernmental meetings, which intend to draw attention to the problems that hinder the development of creative industries (CI) and the opportunities that these areas create.

The importance of CIs, which lie at the crossroads of art, business, and technology, is constantly growing both at the national level and in terms of international competition between countries. CIs have become a strategic direction for increasing competitiveness, productivity, employment, and sustainable economic growth (UNCTAD 2019) [1]. Exceptional rates of growth in turnover, creation of new jobs, and resilience to the economic crisis make creative industries an attractive area for investment at both the private and governmental levels. (UNCTAD 2004) [2]. On the other hand, the scope of knowledge about the economic role of CIs and their impact on the development of other sectors of the economy is quite limited.

This brief describes the economic effect of spending in CIs. Particularly, using input-output and computable general equilibrium models, we outline CI multiplier effects on the development of other industries and discuss implications for government support of CI.

Creative Industries in Ukraine

Although the term creative industry is becoming more common, countries have different approaches to the definition. There have been attempts to introduce an international standard, but the goal has not yet been achieved [3].

Ukrainian law define CIs as “types of economic activity aimed at creating added value and jobs through cultural (artistic) and/or creative expression”.

Currently, the Cabinet Ministers of Ukraine list 34 basic economic activities belonging to CIs, including visual arts, performing arts, publishing, design, fashion, IT, audiovisual arts, architecture, advertising, libraries, archives and museums, folk arts and crafts.

The gross value added (GVA) of CIs in Ukraine is growing rapidly. In 2013, the GVA of creative industries amounted to UAH 31 billion (3% of total value added), and in 2019 it amounted to UAH 117.2 billion (3.9% of total value added) (Figure 1). The number of companies and employees in the field of CI is also growing rapidly. In 2019, there were 205.5 thousand business entities and more than 350 thousand employees. 

Figure 1. Gross value added of CI in Ukraine

Source: State Statistics Service of Ukraine

Most GVA of CIs is generated by information technology (IT) activities. In 2019, the IT sector generated UAH 63.7 billion of GVA or 54.3% of the national CI GVA (Figure 2). In second place, there is Advertising, ¢Marketing and PR – UAH 20.2 billion of GVA or 17% of national GVA. In third place with a small gap there is Audiovisual Art – UAH 19.4 billion of GVA or 17% of national GVA.

Figure 2. Structure of Gross Value Added CI in Ukraine, 2019.

Source: State Statistics Service of Ukraine

Methodology and Data

To assess the economic effect of creative industries, we employ a computable general equilibrium (CGE) approach. CGE estimates a general equilibrium model of an economy using real-life economic data. It models interactions of individual markets – such as manufactured goods, services, and factors of production – encompassing the entire economic system. In doing so, the model takes into account reactions of economic agents – economic sectors, households, government, external sectors – and assumes that markets are perfectly competitive. The resulting set of simultaneous equations then employs real data from the economy in question to estimate the equilibrium in these markets by balancing supply and demand in all markets via the appropriate choice of prices.

In this way, the CGE model is a good reflection of a studied economy. In particular, in application to our research question, it allows us to distinguish the economic impact of additional consumption and capital investments in creative industries, and therefore to form reasonably precise recommendations for policy measures. This feature makes the CGE approach much more relevant than the alternative methods, such as the input-output approach.

Limitations of the CGE approach include increased analytical difficulty and computational demands, calibration and the use of estimated parameters, etc.

Data utilized by the CGE model are given by the Social Accounting Matrix (SAM). The SAM structure is related to the input-output table. Each row and column reflects the income and expenses of a particular economic agent. The main principle of SAM is balance, i.e., income from the sale of goods and services equals expenditures.

As a result, the availability of input-output table data is a crucial factor for our analysis. The State Statistics Service of Ukraine publishes an input-output table for 42 industries, which is not sufficient to distinguish creative industries from other sectors of the economy. To compensate for these deficiencies, we use the following sources:

  • input-output table for Ukraine for 2018.
  • input-output table for Poland for 2015 (latest available) to approximate the intermediate consumption of creative industries, not available from Ukrainian input-output tables.
  • annual report on state budget expenditures of Ukraine for 2018.
  • balance of payments of Ukraine for 2018.
  • structural business statistics of Ukrainian enterprises in part of gross value added and sales volume for 2018.

Results

The results of the CGE model suggest a strong effect of investment in CIs.  The sizes of the multipliers across the most creative industries are similar. The exception is the programming industry, for which for a one hryvnia investment leads to a total GDP growth of 3.2 hryvnias. This value is the highest among all sectors of the economy, not only among the CIs. For the rest of the CIs, the multiplier ranges from 1.9-2.2, which is comparable to the multipliers of the construction and finance and insurance sector (Figure 3). Accordingly, the increase in GDP for one hryvnia of investment by the industry is:

  • UAH 2.2 for libraries, museums, archives.
  • UAH 2.1 for publishing.
  • UAH 2.1 for architecture.
  • UAH 2.0 for performing and other arts.
  • UAH 2.0 for production of jewellery, costume jewellery, musical instruments.
  • UAH 2.0 for public relations, marketing, advertising.
  • UAH 2.0 for design, photography, translation.
  • UAH 1.9 for audiovisual and audio art.

Figure 3. GDP change per one hryvnia of capital expenditures*

* Estimated assuming 5% increase in capital Source: Our calculations are based on data from State Statistics Service of Ukraine and Poland, as described in the data section.

While the above results are obtained by estimating GDP response to a 5% increase in capital, the results are quite similar for different sizes of investments.

Conclusion

Our estimations show that investment in creative industries has a considerable impact on GDP. Investment in the IT sector has the highest multiplier, even compared to “non-creative” sectors of the economy. Other CIs’ multipliers can be compared to the construction and finance and insurance sector. Therefore, the results suggest that creative industries offer a highly valuable investment opportunity.

We also find that increase in capital investment in a creative industry has a stronger positive impact on GDP than an increase in the consumption of the respective industry’s products. An immediate policy implication of this finding is that, to achieve a more significant economic effect of government spending in creative industries, it is necessary not only to increase the expenditures on these industries or boost consumption of their products but also to support them in expanding production capacity.

References

  • Nikolaeva, O., Onoprienko, A., Taran, S., Sholomitskyi, Y. and Iavorskyi, P., 2020. Creative Industries: Impact on the Development of Ukraine’s Economy. Ministry of Culture and Information Policy of Ukraine.
  • UNCTAD, 2019. How can the creative economy help power development? https://unctad.org/news/how-creative-economy-can-help-power-development
  • UNCTAD, 2004. Creative Industries and Development. https://unctad.org/system/files/official-document/tdxibpd13_en.pdf

 Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Ukraine’s Integration into the EU’s Digital Single Market

Blue EU flags in front of European Commission representing Ukraine’s integration into the single market

This brief is based on a study that investigates how Ukraine’s integration into the EU Digital Single Market (DSM) could affect EU-Ukraine bilateral trade as well as Ukraine’s GDP growth.  The major benefits of integration are expected to come from 1) reduction of cross-border regulatory barriers and restrictions to EU-Ukraine digital trade 2) acceleration of the development of Ukraine’s digital economy in line with EU standards. According to the results, enhanced regulatory and digital connectivity between Ukraine and the EU is expected to increase Ukraine’s exports of goods and services to the EU by 11.8-17% and 7.6-12.2% respectively. At the same time, the acceleration of the digital transformation of the Ukrainian economy and society will produce a positive effect on its productivity and economic growth – a 1%-increase in the digitalization of the Ukrainian economy and society may lead to an increase in its GDP by 0.42%.

Background

Integration into the EU has been one of the key topics on Ukraine’s political agenda for a number of years. Recently, more emphasis has been put on an essential component of issue – integration into the EU’s Digital Single Market (DSM). The DSM is a strategy aimed at uniting and enhancing digital markets and applying common approaches and standards in the digital sphere across the EU. The Ukraine-EU Summit, held on October 6, 2020, stressed the paramount importance of the digital sector in boosting its economic integration and regulatory approximation under the EU-Ukraine Association Agreement. Implementation of the provisions of this agreement, in particular the updated Annex XVII-3, would introduce the latest EU standards in the field of electronic communications in Ukraine. The country is also gradually approximating its regulations with regard to other components of the EU DSM – electronic identification, electronic payments and e-payment systems, e-commerce, protection of intellectual property rights on the Internet, cybersecurity, protection of personal data, e-government, postal services, etc. These steps will, in turn, ensure Ukraine’s gradual integration into the EU’s Digital Single Market, which will facilitate digital transformations within the country and open a new window of opportunity for individuals and businesses.

This brief summarizes the results of our recent work (Iavorskyi, P., et al., 2020), in which we estimate the effect that Ukraine’s integration into DSM could have on EU-Ukraine bilateral trade as well as Ukraine’s GDP growth.

Benefits of Integration into the EU DSM

The EU DSM strategy comprises three pillars: (1) better access for consumers and businesses to digital goods and services across Europe; (2) creating the right conditions and a level playing field for digital networks and innovative services to flourish; (3) maximizing the growth potential of the digital economy (EC, 2021).

These goals suggest that the major benefits of Ukraine’s integration into the DSM are likely to come from 1) reduction of cross-border regulatory barriers and restrictions to EU-Ukraine trade, 2) acceleration of the development of Ukraine’s digital economy in line with EU standards.

Indeed, the trade of goods and services is increasingly becoming “digital” – i.e., involving “digitally enabled transactions in goods and services that can be either digitally or physically delivered” (OECD, 2019). Trade digitalization (e.g., electronic contracts, electronic payments, e-customs, etc.) simplifies export and import procedures, reduces trade costs for exporters, and creates new opportunities for trade with the EU, in particular for SMEs. Therefore, the reduction of regulatory restrictions on cross-border digital trade reduces the overall level of restrictiveness of trade in goods and services.

Thus, digitalization is expected to facilitate and intensify the total EU-Ukraine trade in goods and services. It is also anticipated to increase the productivity of Ukraine’s economy which will have a positive impact on the country’s economic growth.

Major benefits include lower prices and greater access to EU online markets for Ukrainian consumers and business, digital innovative products and services, greater online consumer protection, lower transaction costs for businesses, improved quality and transparency of public digital services and e-government as well as an intensification of innovation development in Ukraine.

At the same time, Ukraine’s integration into the DSM entails several obligations: to align national legislation and standards with EU legislation and standards; to ensure institutional and technical capacity as well as interoperability of digital systems. For businesses in Ukraine, this means facing new EU requirements aimed at improving consumer and personal data protection, as well as increased competition from European companies in digital markets. However, these changes are necessary if the country wants to build a common economic space with the EU, especially given the growing impact of digital technologies on international trade and economy.

Ukraine in International Digital Rankings

Many international digital development rankings show that Ukraine lags behind EU countries, including its neighbors that recently joined the EU.

According to the UN e-Government Development Index (EGDI) for 2020, Ukraine ranks 69th among 193 countries and is included in the group of countries with high levels of e-government development. It received the lowest scores for Telecommunications Infrastructure and Online Services, and the highest for Human Capital. Nevertheless, Ukraine is lagging behind its neighboring EU members, – Poland, Hungary, Slovakia, Romania, Bulgaria, Lithuania, etc., – which belong to the group of countries with very high levels of e-government development (UN, 2020).

In the Network Readiness Index (NRI) ranking for 2019, Ukraine ranked 67th among 121 countries. As for the components of the index, Ukraine ranks worst in the following indicators: Future technologies (82nd out of 121), ICT Use by Government and Online Government Services (87th), and Regulatory Environment (72nd). Neighboring EU countries have higher rankings (Poland – 37, Latvia – 39, Czech Republic – 30, Croatia – 44). Other neighboring countries do somewhat better than Ukraine (Turkey is ranked 51st, Russia – 48th) or occupy positions close to Ukraine (Belarus – 61, Moldova – 66, Georgia – 68) (Portulans Institute, 2019).

In 2019, the country ranked 60th among 63 countries included in the World Digital Competitiveness Ranking (WDCR) rating. Just as in the other rankings, Ukraine scored well in the Knowledge component (40th among 63 countries), while in terms of Technology and Future Readiness it was at the bottom (61st and 62nd position respectively) (IMD, 2019).

Hence, it is primarily the technological and regulatory issues, that need to be addressed in order to improve Ukraine’s digital position in the region and the world.

Methodology

Measuring Ukraine’s Digitalization level

In order to estimate the impact of digitalization, a Composite Digitalization Index is calculated for Ukraine, the EU, and other countries included in the model. This index is based on 11 digital indicators, combined into five components that characterize different areas of the digital economy and society Connectivity, Use of the Internet by citizens, Human capital, Integration of digital technology by businesses, and Digital public services.

Our results confirm that the level of digital development in Ukraine is far below the EU average. It also lags behind the new EU Member States, which have a lower level of digital development compared to the other EU countries. As of 2018, the widest gaps between Ukraine and the EU average are found in Digital Public Services, Connectivity and Use of Internet by citizens. At the same time, Ukraine performed better in Human Capital and Integration of digital technology by businesses.

Measuring Digital Services Trade Restrictiveness in Ukraine

To assess the impact of digital regulatory barriers on trade, we use the Digital Services Trade Restrictiveness Index (Digital STRI) (OECD, 2020). It quantifies the regulatory barriers in five different policy areas (communication infrastructure, electronic transactions, electronic payments, intellectual property, other restrictions) that affect trade in digital services (Ferencz, J., 2019). OECD calculates Digital STRI for OECD countries and some non-OECD countries. As Ukraine is not included in this index, we estimate it for 2016-2018 using the OECD methodology.

Our estimations show that the level of digital services trade restrictiveness in Ukraine is much higher than the EU average. The regulatory differences in the digital sphere between Ukraine and the EU increase the cost of cross-border digital transactions between countries.

For Ukraine, most barriers are related to cross-border electronic payments and settlements, protection of intellectual property rights on the internet, cross-border electronic transactions (for example, the divergence of the national requirements for foreign trade agreements, including electronic ones, from international practices and standards, lack of practical mechanisms for the application of the electronic digital signature in foreign trade contracts, lack of mutual recognition of electronic identification and electronic trust services between Ukraine and major trading partners, etc.), other barriers (requirements for the use of local software and cryptography, etc.). These regulatory restrictions significantly hinder the development of cross-border cooperation and Ukraine’s integration into the European and global digital space.

Ukraine’s integration scenarios

In the event of Ukraine’s integration into the EU DSM, the country’s regulatory environment and digital development are expected to gradually approach the EU averages. We model it through assuming that the regulatory differences between Ukraine and the EU (captured by the Digital STRI Heterogeneity Indices – see OECD, 2020) will be decreasing, and level of digitalization in the country (captured by the Digitalization Index – OECD, 2020) will converge towards that of EU-DSM members.

We considered three integration scenarios that imply high, medium, and low levels of Ukraine’s approximation to the regulatory environment and digital development of the EU. For instance, the high scenario implies the highest level of Ukraine’s digital development and the lowest level of regulatory differences between Ukraine and the EU.

Models

We study the effect of reduced regulatory differences in the digital sphere on Ukraine-EU trade using a gravity model – one of the traditional approaches in the international trade literature. A gravity model predicts bilateral trade flows based on the size of the economy and trade costs between countries (affected by distance, cultural differences, FTAs, tariffs, etc.)

The study uses the following specification of the model for exports of goods and services in 2016-2018:

• Dependent variable – the total export flow of goods and services from country into country j (all possible pairs of countries).

• Independent variables – distance between countries and common characteristics (borders, language, law), existence of a free trade agreement, level of tariff protection (for goods), level of regulatory heterogeneity in the digital sphere between the two countries, and a set of fixed effects for each country.

We also estimate how digital development affects technical modernization, productivity, and economic growth. Technically, we use a Cobb-Douglas production function to describe each country’s output and model its total factor productivity component as a function of digital development (captured by the Digitalization index).

Results

The results suggest that Ukraine’s integration into the EU DSM will be beneficial for both Ukraine and the EU. Under all integration scenarios, bilateral trade between Ukraine and the EU is expected to intensify considerably due to enhanced regulatory and digital connectivity between the two.

Ukraine’s total exports of goods and services to the EU are estimated to grow by 11.8-17% ($2.4-3.4 billion) and 7.6-12.2% ($302.5-485.5 million), respectively – a cumulative increase throughout the period of implementation of reforms aimed at regulatory and digital approximation of Ukraine to the EU.

 Figure 1. The impact of Ukraine’s integration into the EU’s DSM on the exports of services from Ukraine to the EU*: three integration scenarios

Source: Authors’ own calculations. The current level of Ukraine’s exports of services to the EU – as of 2018

Figure 2. The impact of Ukraine’s integration into the EU’s DSM on exports of goods from Ukraine to the EU*: three integration scenarios

Source: Authors’ own calculations. The current level of exports of Ukrainian goods to the EU as of 2018

The EU would increase its exports of goods and services to Ukraine by 17.7-21.7% ($4.1-5 billion) and 5.7-9.1% ($191-305 million), respectively.

The acceleration of Ukraine’s digital development will bring productivity gains that would transform into higher GDP growth. It is estimated that a 1% increase in Ukraine’s digitalization level is expected to raise its GDP by 0.42%. As a result, the country’s gradual approximation to EU levels of digitalization would result in additional Ukraines GDP growth of 2.4-12.1% ($3.1-15.8 billion), depending on the scenario.  

Figure 3. Impact of digitalization on Ukraine’s GDP growth: three digitalization increase scenarios

Source: own calculations. The left axis – GDP growth (%), the right axis – the level of digitalization. The current level of digitalization of Ukraine as of 2018.

Conclusion

According to our estimations, improved digitalization and reduction of regulatory barriers in the digital sphere between Ukraine and the EU will have a positive effect on trade for both Ukraine and the EU. There is also a significant potential for economic growth to be attained in Ukraine by increasing digitalization and productivity of various spheres of the economy and society.

Realization of this potential would, however, require a substantial regulatory approximation on the Ukrainian side to achieve alignment with the EU DSM. The main emphasis needs to be put on electronic identification and transactions, payment systems and electronic payments, protection of intellectual property rights on the internet, cybersecurity, and personal data protection.

References

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?

Kiev in snow during. the winter with condominiums under 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.

A Decade of Russian Cross-Domain Coercion Towards Ukraine: Letting the Data Speak

20200217 A Decade of Russian Cross-Domain Coercion FREE Network Policy Brief Image 01

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