Ukraine and punishment: the real impact of sanctions on the Russian economy

Abstract

Since its invasion of Ukraine in 2022, Russia has been subjected to a slew of sanctions imposed by Western economies in order to contain the threat and prevent further escalation across the European continent. This study sought to assess the real impact of sanctions on the Russian economy.

Time series data was used to conduct quantitative analysis with the intensity of sanctions as the independent variable. The dependent variables are: Russia’s real GDP, euro-region and US real GDP, global oil price, ruble exchange rate against the US dollar, and euro exchange rate against the US dollar. The analysis focused on correlation, t-test (paired sample 2 t-test due to obvious monotonic data), and linear regression analysis to test the strength of the effects.

Findings show that the sanctions have no significant effect on Russia’s real GDP, the euro-region’s real GDP, the US’s real GDP, the ruble exchange rate, and the euro exchange rate. However, it had a significant positive effect on global oil prices, with every unit increase in sanctions increasing the price of oil by over $14. It was concluded that the study should be repeated once the sanctions are lifted. The data could be a limiting factor to the findings made from this study.

Introduction

The conflict between Russia and Ukraine actually started in March 2014, and it begat economic sanctions imposed by Western economies, including the members of the European Union. However, this conflict metamorphosed into a full invasion of Ukraine by the Russian Federation in 2022. Reporting for BBC News, Kirby (2022) looked at why Russia invaded Ukraine and what Putin actually wanted. In this special report, it was noted that Vladimir Putin’s ongoing invasion of Ukraine is the biggest war in Europe since World War II, under the justification that the modern, Western-learning part of Ukraine was a constant threat to the Russian Federation and Russia could not feel safe, developed, and at peace with its existence (Kirby, 2022). As at the time of this study, the invasion is still on and thousands of people have died from it. Towns and cities such as Mariupol lie in ruins, with over 13 million people displaced. The question still remains: what was the reason for the invasion, and is there any end to it? Answers to these questions abound, but they are outside the scope of this research.

This paper investigates the impact of sanctions on the Russian economy at the aggregate level. Thus, it contributes to the debate on whether sanctions imposed by the rest of the world are effective ways of addressing the Russian invasion of Ukraine, or whether some adverse effects (in terms of GDP growth and other macroeconomic factors) can be linked to the imposition of sanctions, as well as the actual effects, if any, on the overall outlook of the Russian economy in areas like oil-related revenues.

It is without doubt that businesses across the globe, especially those on the European continent, are afraid of the adverse outcomes of the sanctions, and a number of them (such as the Chinese government) have called for the removal of these sanctions. As pointed out in Deutsch-Russische Auslandshandelskammer (2016), these fears are valid as data from the UN Comtrade shows that exports of EU countries to Russia declined by about 14% in 2014 (following sanctions on Russia for its actions in Crimea) compared to 2013 records. The same data showed that out of the 28 member states, 25 experienced falls in their exports. Some had significantly huge drops in exports, like Malta with 78%, Cyprus with 47%, and Belgium with 27. The large economies of the European Union also experienced substantial losses from exports, with the UK and Germany experiencing an 18% decline, respectively, while Italy and France had a 12% decline. Going further, Eurostat data revealed that in 2015, this decline in exports intensified, with exports from the 28 member states to the Russian Federation dropping from 119.4 billion euros to 73.8 billion euros, a decline of almost 40% (Kholodilin & Netšunajev, 2018). To demonstrate this, from 2013 to 2015, exports from the UK to the Russian Federation declined by 51% (Kholodilin & Netšunajev, 2018). In 2016, the fall in EU28 exports reached its bottom of 72.4 billion euros, and it later increased to 86.2 billion euros in 2017, a 19% increase from the previous year.

Considering that the European Union (Council of the European Union, 2015) reached a decision together with the American government (President of the USA, 2018) to prolong the sanctions on the Russian Federation, it is imperative to state that all parties involved must understand that there is a price to be paid for such a political decision. Therefore, if these experiences were recorded during their previous sanctions on the Russian Federation from 2014 to 2018, it would be exceptional to review these issues and assess the impact of the 2022 sanctions. This would help in validating findings from the previous study.

The present study looks at how the sanctions have influenced certain macroeconomic variables on both the Russian side and that of the Euro region. The European Union consists of 27 countries that share the same currency and monetary policy. As highlighted earlier, the EU region had tight international trade and financial ties with the Russian Federation before the conflict escalated in 2014, and as the ties seemed to be returning, Russia invaded Ukraine, bringing about more economic sanctions and deferments in agreements.

As at the time of the study, this is one of the few papers that seek to evaluate the effects of sanctions on Russia’s economic growth as well as that of the euro-region economies. As such, there is limited literature on the economic effects of sanctions on Russia. In the work of Oja (2015), emphasis was placed on the sectorial effect of sanctions on the Baltic economies. Pestova and Mamonov (2017) focused their own study on the effects of sanctions on the Russian economy. This present study is modelled around the work of Kholodilin and Netšunajev (2018), to evaluate the effects of sanctions on economic growth in Russia and the euro-region economies. However, unlike the work of Kholodilin and Netunajev (2018) that was primarily domiciled on the 2014 to 2018 sanctions, this present study includes the 2022 sanctions. The inclusion allows for comparative assessment of findings from the outcomes of present sanctions and previous sanctions. The main reason for the lack of related studies can be linked to the fact that sanctions have been relatively introduced in recent times. Thus, there are a small number of observations made during the period when sanctions were in full force. However, this study made use of the most recent data, as it allowed for quantification of current effects without building claims as to the long-term perspective.

Based on the work of Dreger, Fidrmuc, Kholodilin, and Ulbricht (2016), the researchers were able to construct an aggregate index that was used to measure the extent of the economic sanctions imposed by different global economies on the Russian Federation. To account for the intensity of the sanctions, we took into account all measures, including those that were targeted at individuals, businesses, different industries, and the entire Russian economy. This adopted the trade weighted index of the sanctions, implying that the sanctions meted by a country were weighted by the volume of its trade (import and export) with the Russian Federation. The magnitude of the index approximates the intensity of the sanction and is dependent on the country that is imposing the sanction and the sector where the sanctions are imposed. If the sanction index is equal to zero, it implies that no sanctions are imposed. The advantage of this method is that it makes it easier to analyze the effects the researchers are interested in, as it allows for: a) an assessment of how the economies dynamically respond to the sanctions; b) a clearer understanding of how the sanction shocks contributed to different key macroeconomic indicators; and c) an easier estimation of the counterfactual time series, in the case of this study, the GDP, following the assumption that there was no sanction. For this study, we consider the oil prices, real effective exchange rates (REER), and the GDP of Russia and the euro-region economies.

To identify the sanction shock, the paper adopted a proposal made by Antolin-Diaz and Rubio-Ramirez (2016) to look at a combination of narrative sign restrictions. By doing so, we were able to differentiate between the effects of intensified sanctions as well as the rapid decline in the price of oil that occurred almost simultaneously. The effects of the sanction shocks on the price of oil were narrowed down by the restrictions, which implies that sanction shocks cannot have a strong effect on the variable under consideration. Therefore, the contribution of sanctions to fluctuations in the price of oil is not beyond 10%. While the most recent data was used in this work, it is important to note that the sample size was too small to use time-varying parameters for model estimations, so we cannot account for structural parameter shifts.

The other parts of the paper are organized as follows. Section 2 contains literature reviews and discussions focused on the economic impacts of sanctions. Section 3 describes the research method and data used in the study. Section 4 presents the econometric model, main findings, and discussions. The conclusion is contained in the final part, Section 5.

Literature review

Sanctions against Russia imposed by the European Union and the United States

In response to Russia’s military action against Ukraine, the EU adopted a number of sanctions packages against Russia. Some of these sanctions actually started in 2014, but emphasis will be placed on those continued or implemented during its 2022 invasion of Ukraine. Overall, the sanctions aim to weaken the Kremlin's ability to finance the war and to impose an economic and political cost on Russia's political elites who are behind the invasion (Council of the European Union, 2015).These sanctions include individual sanctions; economic sanctions; restrictions on the media; and diplomatic measures.

On the side of individual restrictive measures, the European Union has frozen assets and restricted travel within its region for the top Russian elite. Overall, 1212 individuals and 108 entities have been subjected to asset freezes and travel bans because of their actions in undermining the independence, sovereignty, and territorial integrity of Ukraine (Council of the European Union, 2015).

The economic sanctions have been active since 2014. In 2014, the EU imposed economic sanctions that were targeted at Russia in certain economic sectors. In 2015, the leaders of the EU reached the decision to align the existing sanction regime with the full implementation of the Minsk agreements, and this was extended to 2016. These sanctions are currently extended until January 31^st, 2023. They include restrictions in the energy sector, financial sector, transport, defense, and raw materials and other goods (Council of the European Union, 2015).

On media, the EU suspended the broadcasting activities of state-owned media outlets from Russia as the Russian government has been using these outlets as an instrument for manipulating information and promoting disinformation about its invasion of Ukraine, which includes propaganda targeted at destabilizing its neighboring states and the member states of the EU (Council of the European Union, 2015).

Finally, on the side of diplomatic measures, the EU-Russia summit was cancelled, with the EU member states deciding not to hold regular bilateral summits with Russia. This included the suspension of bilateral talks with Russia on visas. As against the G8 summit in Sochi, a G7 meeting was held without Russia – in Brussels – and further meetings have continued to be held in the G7 format. EU countries have also suspended negotiations with Russia as it relates to joining the Organisation for Economic Co-operation and Development (OECD) and the International Energy Agency (IEA). Additionally, in February 2022, a decision was reached by the EU that Russian diplomats, other officials from Russia, and business people may no longer benefit from their facilitated visa provisions, which made it possible for privileged access to the EU (Council of the European Union, 2015). However, ordinary Russian citizens are not affected by this decision. Similar decisions to those of the EU were also imposed by the US.

The economic impacts of sanctions

Findings in relation to the economic impacts of sanctions are mixed. For instance, trade restrictions can raise costs and cause inflation in the target economy, but can also have an adverse effect on the sanctioning economy. Thus, countries that have strong ties are specifically hit by reduced growth perspectives. Caruso (2003) adopted the gravity regression approach to report on the negative effects of economic sanctions on trade. When sanctions are multilaterally implemented, they are known to cause greater damage. However, in the case of unilateral implementation, the target might be able to buy or sell goods and raw materials with another economy in a non-sanctioning country.

In another, yet related, study, Neuenkirch and Neumeier (2015) looked at the economic impacts of sanctions that were imposed by the USA and UN. In this study, a panel data estimation technique was used on a sample of 64 economies covering the period from 1976 to 2012. According to the study's findings, UN sanctions had a reasonably large and statistically significant effect on reducing the target state's real per capita GDP growth rate by around 2.3–3.5 percent; the effect of US sanctions was much smaller, accounting for a 0.5–0.9 point decrease in the target state's GDP growth rate. In any case, one needs to understand that the effects coming from international sanctions imposed by the UN might be relatively different when compared to similar effects from the bilateral sanctions imposed by Western countries and Russia in particular.

Assessing the impact of sanctions on Russian stock market returns, Hoffmann and Neuenkirch (2015) discovered that stock returns decreased with conflict intensification. In particular, the escalation and de-escalation of the Ukraine conflict resulted in a total of 6.5 percentage variation points on the Russian stock market.

Dreger et al. (2016) conducted an assessment of the effects of sanctions in relation to the Russian-Ukrainian conflict as well as the effect of a fall in oil prices on the daily exchange rate of the ruble. It was discovered in this study that oil prices have more effect on the exchange rate than economic sanctions have. A conditional forecast was used by Pestova and Mamonov (2017) to evaluate the effects of sanctions on the Russian economy, but the end effects are quite unclear once the statistical uncertainty is considered. Kholodilin and Netšunajev (2018) assessed the impact of sanctions on the real side of the Russian economy and the economies of the euro-region states. A structural vector autoregression was used to assess the effects of sanctions. They discovered weak evidence of GDP decline in both Russia and the euro-region's economies as a result of the sanctions they imposed on one another. The effects of the sanctions are also so small on the real effective exchange rate.

Research method

Data

Before estimating data, it was critical to select the variable that would be included in the model. Reaching that decision was not easy; hence, the need to rely on empirical literature for growth. One such example is the work of Bernanke and Blinder (1992), which assessed the effects of monetary policies on different real economic variables (like industrial production). On a similar note, Sims (1992) focused on the impact of monetary policy on industrial production, utilizing short-term interest rates, consumer price index, monetary aggregate, an index of the foreign exchange value of the domestic country, and commodity price index. Kapetanios et al. (2012) examined the effects of QE on GDP growth, the yield spread, M4 growth, CPI inflation, and stock prices. Mertens and Ravn (2010) had a different outlook as their study focused on the effect of fiscal policy by making use of only three variables: GDP, private consumption of nondurable goods, and government consumption expenditures. In another study, Berument, Ceylan, and Dogan (2010) conducted an assessment of the impact of oil shocks on economic performance and employed the SVAR for their variables: real exchange rates, world oil prices, output growth, and inflation. An investigation of the effects of both fiscal policy and openness on economic growth was undertaken by Nursini (2017), based on openness (ration of trade to GDP) and different measures of government expenditure. Therefore, while the choice of the core variables that can be included in a study significantly depends on the question setting, the researchers were able to identify different variables that are most commonly included when developing related models in economic growth within the international context. They are: CPI, interest rates (due to their spreads), exchange rates, commodity prices, and the openness of the economy. Additionally, oil prices were also included since the Russian economy heavily depends on the export of natural resources, especially oil and gas.

Thus, for this analysis, six variables were used. All the adopted data is seasonally adjusted. The data was monthly and ran from January 2019 to July 2022. They are founded on the following formula:

The intensity of the sanction

_St represents the index of the intensity for the sanctions that were imposed on the Russian by the Euro region nations and USA. The reason for choosing the USA and the Euro region is that they have strong trade ties with Russia and they are the main economies that imposed sanctions against the Russians. Dreger et al. (2016) developed and thoroughly described the index. For this study, the researchers readopted the adjustments made by Kholodilin and Netšunajev (2018), which included sanctions introduced after 2015, to include sanctions introduced after Russia’s invasion of Ukraine in February 2022 and covering the period since its invasion down to the time of this report (July 31st, 2022). Before 2022, it is assumed that the index will take the value of zero.

As contained in Figure 1, the European Union and the USA already have sanctions on Russia as of 2019, due to its military activity in Crimea. The intensity of the sanction increased in December 2021 before reaching its present peak in February 2022. Some of these sanctions already have an existing shelf-life of up to 2023, as documented earlier. Therefore, it is expected that the intensity will either be sustained or continue to move upwards in the coming year. This is illustrated in Figure 1.

Figure 1. Intensity of the sanction 

Change in Russia’s real GDP

Δ_yt^RU This is the difference in the log of Russian monthly real GDP (x100) as obtained from Statista. The percentage change of the GDP relative to the previous month is recorded. See Figure 2.

Figure 2. Change in Russia’s real GDP

Change in EU and USA real GDPs

Δ_yt^EA This is the first difference obtained in the log of the European Union and the USA on monthly real GDP (x100) obtained from Eurostat and the Bureau of Economic Analysis of the US Department of Commerce. For this data, there was no monthly data available. Thus, the quarterly data is distributed across each quarter of the year. See Figure 3.

Figure 3. Change in EU and USA real GDPs

Changes in oil price

Δ_pt^oil This represents the difference between the log of the price of oil as accessed from Datastream. Given the importance of oil and gas in Russian exports, this variable accounts for changes in oil prices. The price of oil, like other variables, entered the model on a monthly percentage growth basis. 

Figure 4. Changes in oil price

Real effective exchange rate of the Russian rubble

e_t^RU This is the log of the real effective exchange rate of the Russian ruble as obtained from the Bank for International Settlements.

Figure 5. Real effective exchange rate of the Russian rubble

Real effective exchange rate of Euro

e_t^EA The log of the real effective exchange rate of the US dollar and euro as obtained from the bank for international settlement. See Figure 6.

Figure 6. Real effective exchange rate of the Russian rubble

Data Analysis

Descriptive Statistics

Table 1 looks at the descriptive statistics of the variables loaded in the analysis. Focusing on the mean values, it is evident that in the course of the period under analysis: a) Russia has faced an average of 2 sanctions from the US and the EU; Russia's GDP grew by 0.53%, while the EU saw growth of 0.29% and the US grew by 2.29%; the price of oil sold at an average of $63.83 per barrel; the Russian rubble exchanged for 71.15 per US dollar; and the Euro exchanged for 1.14 per US dollar.

In terms of the standard deviation from the mean, the intensity of sanctions and the Euro to US$ exchange are closer to the mean, while real GDP for all three economies is farther from the mean. The case of the real GDP is expected because of the huge volume of value that comes with it. The same farter deviations were recorded for the oil price and the real exchange value of the Russian ruble. The skewness and Kurtosis values show that the data is normally distributed and can be used for further analysis.

Table 1. Descriptive Statistics

	N	Min	Max	Mean	Std. Dev	Var	Skewness	Kurtosis
St	43	1.00	5.00	1.8605	1.35544	1.837	1.709	1.624
ΔytRU	42	-9.50	11.00	.5262	4.24615	18.030	-.150	.451
ΔytEA EU	42	-11.30	11.90	.2857	4.58957	21.064	-.012	3.710
ΔytEA US	42	-31.20	33.80	2.2857	12.85177	165.168	-.235	3.748
Δptoil	42	21.04	116.80	63.8331	21.40578	458.207	.648	.653
etRU	43	51.45	100.98	71.1495	7.59449	57.676	.944	5.041
etEA	42	1.05	1.22	1.1395	.04456	.002	.153	-.668
Valid N (listwise)	42

Correlation

The correlation was based on Spearman's rho because of the possibility of a monotonic relationship as the data is not perfectly linear. On the same note, two-tailed analysis was conducted because the effect could go either way (whereby an increase in a variable could mean a decrease in effects on another variable).

A number of findings emerge from the correlation analysis. However, since the focus is on the intensity of the sanctions, and it is positively correlated with the dependent variables, On the basis of real GDP, the intensity of sanctions is 33.6% correlated to Russian GDP and 53.9% correlated to the GDP of the Euro region. The intensity of sanctions is also 79% correlated to the price of oil; 27.4% correlated to the Russian Ruble’s real exchange rate; and 8.9% correlated to the Euro's real exchange rate. Thus, the intensity of sanction had a positive correlation with all the dependent variables. One major highlight, although outside the scope of this study, from the analysis is that an increase in oil prices is negatively correlated to both the Russian Ruble and the Euro. This is likely based on the fact that, globally, oil is mainly traded in US dollars.

Table 2. Spearman's rho

	St	ΔytRU	ΔytEA EU	ΔytEA US	Δptoil	etRU	etEA
St	1.000
ΔytRU	.336*	1.000	.
ΔytEA EU	.539**	.367**	1.000
ΔytEA US	.026	.093	.493**	1.000
Δptoil	.790**	.555**	.541**	-.014	1.000
etRU	.274*	-.162	.150	.174	-.059	1.000
etEA	.089	.072	.303*	.670**	-.136	.429**	1.000
*. Correlation is significant at the 0.05 level (1-tailed).
**. Correlation is significant at the 0.01 level (1-tailed).

Paired sample t-test

In this study, the intensity of sanctions is considered an intervention in the performance of the Russian economy. Thus, paired sample t-test analysis is computed to assess the effect of the independent variable on the dependent variables as detailed below.

In a paired sample t-test, if p<0.05, it implies that the mean difference is significant and the H0 will be rejected while the H1 will be accepted. Thus, based on Table (3), a number of findings can be made. Basically, we are looking at the before and after effects of the impact of sanctions on Russia’s economic performance. Thus, sanctions had no significant effect on Russia's real GDP (0.067, p > 0.05); 2) they had a significant effect on Euro economies' real GDP (0.044, p 0.05); 3) it has no significant effect on US real GDP (0.804, p > 0.05); 4) it has a significant effect on the price of oil and the Russian ruble's real exchange rate (0.001 and p 0.05); and 5) it has a significant effect on the Euro-real exchange. Thus, it leads to the following conclusions:

• Sanctions have no significant effect on Russia’s real GDP.
• Sanctions have little significant effect on the real GDP of the European economies.
• Sanctions have no significant effect on the real GDP of the American economy.
• Sanctions have a significant effect on global oil prices.
• Sanctions have a significant effect on the Russian ruble’s real exchange rate.
• Sanctions have a significant effect on the euro real exchange rate.

Table 3. Paired Samples Test

		Paired Differences					t	Df	Sig. (2-tailed)
		Mean	Std. Dev	Std. EM	95% Confidence Interval of the Difference
					Lower	Upper
Pair 1	St  - ΔytRU	1.26	4.34	.67	-.094	2.61	1.88	41	.067
Pair 2	St  - ΔytEA EU	1.50	4.67	.72	.044	2.96	2.08	41	.044
Pair 3	St  - ΔytEA US	-.50	12.98	2.00	-4.54	3.54	-.25	41	.804
Pair 4	St  - Δptoil	-62.05	20.33	3.14	-68.38	-55.71	-19.78	41	.000
Pair 5	St  - etRU	-69.29	7.45	1.14	-71.58	-66.99	-61.00	42	.000
Pair 6	St  - etEA	.65	1.29	.199	.24	1.04	3.24	41	.002

Regression coefficient analysis

For the effect of sanctions on Russia’s real GDP, the null hypothesis is supported as 0.640 is higher than the accepted limit of p<0.05. Thus, sanctions do not have a negative effect on the real GDP of the Russian economy.

For the effect of sanctions on the Euro region’s real GDP, the null hypothesis is supported as 0.640 is higher than the accepted limit of p<0.05. Thus, sanctions do not have a negative effect on the real GDP of the Euro region’s economy.

For the effect of sanctions on the US’s real GDP, the null hypothesis is supported as 0.751 is higher than the accepted limit of p<0.05. Thus, sanctions do not have a negative effect on the real GDP of the US.

For the effect of sanctions on global oil prices, the null hypothesis is rejected as 0.000 is less than the accepted limit of p<0.05. Thus, sanctions have a positive effect on the global oil price. For every unit increase in sanctions, the global oil price increases by $14.183. This is understandable as sanctions will likely block demand for Russian oil, leading to excess demand and a resulting higher price for the demanded commodity.

For the effect of sanctions on the Russian Ruble exchange rate, the null hypothesis is supported as 0.206 is higher than the accepted limit of p<0.05. Thus, sanctions do not have a negative effect on the real exchange rate of the Russian ruble.

For the effect of sanctions on the euro exchange rate, the null hypothesis is slightly supported as 0.051 is higher than the accepted limit of p<0.05. Thus, sanctions do not have a negative effect on the real exchange rate of the euro.

Table 4. Coefficients^a

Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.
		B	Std. Error	Beta
ΔytRU	St	.247	.523	.074	.471	.640
ΔytEA EU	St	.266	.566	.074	.471	.640
ΔytEA US	St	-.507	1.587	-.050	-.320	.751
Δptoil	St	14.183	1.405	.847	10.096	.000
etRU	St	1.102	.858	.197	1.285	.206
etEA	St	-.011	.005	-.303	-2.013	.051
a. Dependent Variable: ΔytRU, ΔytEA EU, ΔytEA US, Δptoil, etRU, etEA

Discussion and conclusion

This paper sought to assess the effect of sanctions on the performance of the Russian economy as well as that of neighboring economies (the Euro region and the US). The choice of the neighboring economies was based on the fact that they are the ones imposing sanctions on Russia, although they had strong trade relations with the Russians, and it is imperative to assess whether or not there is a reverse effect on their own performance.

A quantitative study was conducted with time series data to attain this objective. The data ranged from January 2019 to July 2022. The reason for choosing the said range is that it covers the period when the western nations started imposing sanctions on Russia prior to its invasion of Ukraine, all the way down to the period when it actually invaded Ukraine and got sanctioned more. To test the effects of the sanctions, the intensity of the sanction was used as the independent variable. The dependent variables were the Russian economy's, the Eurozone economy's, and the US economy's real GDP; the Russian ruble and euro's real exchange rates; and the global price.

Findings from the study indicate that the sanctions did not have a significant effect on Russia’s real GDP and the real exchange rate of the Russian Ruble. Furthermore, no significant effect was observed for real GDP in the United States and the Eurozone, as well as the real exchange rate of the US dollar against the Euro. However, a significant positive effect was recorded for global oil prices as the sanctions increased oil prices.

Therefore, it is concluded from this study that the sanctions don’t actually have any significant effect on the Russian economy, as well as that of the economies of the sanctioning states. However, it’s imperative to state that the range of data (3 years and 7 months) used in this study might be a limiting factor. It is recommended that the study be repeated once the sanctions imposed on the Russian economy have been removed in order to understand the actual effects on the performance of the Russian economy, as one might consider the sanctions imposed due to Russia’s invasion of Ukraine as being in its early stages and having highly possible effects.

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