Impact of sanctions on Russia’s foreign trade

1. Introduction

Economic penalties have become a common tactic in international relations, commonly used to address security and human rights problems [26, c.35-45]. However, the effects of these sanctions on the targeted country's economy and commerce are complex and not always obvious. The case of Russia is particularly relevant given the recent history of Western sanctions. Despite the possible worldwide economic consequences, the effects of these sanctions on Russia's international commerce have not been thoroughly investigated. Russia achieved the unpleasant position of being the top in the number of sanctions placed on it in 2022. According to the Russian Foreign Ministry, around 10,000 sanctions were placed on the nation in August. While the majority of these sanctions apply to people, they considerably restrict routine product exports and the operation of numerous domestic firms in many areas of the economy.

Despite the widespread use of economic sanctions as a tactic in international affairs, detailed study on the impact of these restrictions on Russia's foreign commerce is lacking. Russia's situation is particularly relevant, given its history of Western sanctions and its role as an important role in the global economy. While it has been accepted that sanctions have hindered Russia's commerce and damaged local firms, a more in-depth examination is required to identify the scope and precise impacts of these sanctions.

As a result, the purpose of this study is to fill a research gap by investigating the effects of sanctions on Russia's foreign commerce using a comprehensive methodology that includes both quantitative and qualitative analysis.

2. Significance of the Study

The research will employ panel data regression analysis to investigate trends and patterns in Russian trade before and after the sanctions were implemented. This quantitative method will allow for the detection of important changes in trade flows, trading partner diversification, and the overall influence on Russia's trade performance. These insights may be utilized by policymakers and businesses to change their strategy and respond to disruptions caused by sanctions.

Further, research will use qualitative analysis to investigate the variables that influence Russia's trade policy in the context of sanctions. The study will provide useful insights for policymakers and businesses facing comparable issues in other contexts by evaluating Russia's techniques for navigating and mitigating the negative consequences of sanctions.

In conclusion, this study is significant because it can inform decision-making processes at both the national and international levels. It supports the creation of more effective policies and measures to reduce the harmful impact of sanctions by offering a greater knowledge of the complexity surrounding sanctions and their influence on a country's international commerce. Finally, the findings may aid in the promotion of robust trading systems. Ultimately, the findings can contribute to the promotion of resilient trade systems in the face of geopolitical tensions.

3. Literature Review

Sanctions is refer to unilateral or collective actions taken against a state that is believed to be in violation of international law. [14, c.20] The purpose of these actions is to exert pressure on the targeted state and compel it to adhere to international legal norms. The authors emphasize that the objective of sanctions is to bring about conformity and compliance with international law. Moreover, Sanctions refer to measures imposed by one country or a group of countries against another country, entity, or individuals to exert pressure, encourage policy changes, or punish undesirable behavior. Sanctions are usually imposed in response to perceived violations of international law, human rights violations, aggressiveness, or other egregious conduct. Khawaja [28] provides the following sorts of sanctions that are commonly utilized by different countries.

According to Korhonen [30] , the introduction of sanctions on Russia has had significant effects for its international trade. According to the authors, Russia's trade volume with the EU, its primary commercial partner, has decreased as a result of the sanctions. Furthermore, they contend that the sanctions have had a negative influence on Russia's economic growth, resulting in a decrease of its GDP. Contrary to popular belief, there is evidence that the perceived impact of sanctions on Russia's international trade has been overestimated. Sanctions have had little impact on Russia's exports to non-sanctioning countries. Furthermore, they argue against a significant decrease in Russia's commerce with the EU as a direct consequence of the sanctions. [1]

Various scholars, on the other hand, have presented evidence confirming the major impact of sanctions on Russia's international trade. Drapkin et al. [15] used a gravity model to investigate the impact of sanctions on Russia's trade patterns. Following the introduction of sanctions, Russia's trade with the US and the EU decreased significantly. The authors attribute this reduction to the sanctions' negative effects on trade financing and credit.

Overall, the existing literature suggests that the impact of sanctions on Russia's international trade is complex and multifaceted. While some researchers argue that the impact of sanctions has been overstated, others find evidence that the sanctions have had a significant negative impact on Russia's trade with the US and the EU. The use of different research methods, such as gravity models and structural gravity models, highlights the complexity of the issue and the need for further research.

4. Data and Methodology

Russia's trade relationships comprise a diverse network consisting of 191 entities, including countries, territories, and islands. Analysis from a regional standpoint reveals that 49.2% of Russia's total export value is directed towards nearby European countries whereas 42.3% is being exported to Asian economies. The latest international trade map data indicates that Russia's merchandise exports in 2021 reached a notable value of US$491.6 billion. This represents a substantial growth rate of 45.8% from 2020 to 2021. In recent years, Russia has maintained an average goods trade (exports plus imports) to GDP ratio of around 40%, while the United States has had a ratio of about 20%. In 2021, Russia held the 13th position among global goods exporters and ranked 22nd among importers. It is noteworthy that China emerged as the largest exporter for Russia in 2021, with a value of US$68 billion, accounting for 13.8% of Russia's total imports.

The research design of this article adopts a panel data set approach focusing on Russia's top 49 trading partners, ranked by exports. This carefully selected panel data set provides a comprehensive basis for analyzing the intricate dynamics of trade relationships over a period from 2000 to 2022. By incorporating multiple trading partners, the study captures a broader perspective of Russia's international trade landscape, enhancing the robustness and generalizability of the findings. Moreover, to ensure a rigorous analysis, a quantitative research design is employed, allowing for a systematic and quantitative evaluation of the impact of sanctions on Russian international trade. By leveraging statistical models and econometric techniques, this study aims to derive objective and quantifiable insights regarding the effects of sanctions on trade outcomes. The application of quantitative approaches enables a comprehensive analysis of patterns, trends, and causal relationships, leading to a deeper understanding of the complex dynamics between sanctions and Russian trade. In this study, the choice of the gravity model and the Difference-in-Differences (DID) approach is based on their effectiveness in assessing the impact of sanctions on trade. These models have been extensively employed in empirical research to investigate international trade relationships and determine causal effects. The gravity model, in particular, is a widely recognized and versatile tool used in various empirical fields.

The utilization of a panel data set design allows for the consideration of both cross-sectional and time-series variations, enabling a detailed analysis of the effects of sanctions on Russian trade across different trading partners and over time. This comprehensive approach facilitates the identification of specific trade patterns, sectoral dynamics, and potential variations in how different trading partners respond to sanctions. By incorporating multiple dimensions of analysis, this study aims to provide a comprehensive assessment of the complex relationship between sanctions and Russian international trade. Previous studies conducted by Cortright and Lopez [12], Hufbauer et al. [25], and Neuenkirch and Neumeier [32], as well as Fedoseeva and Herrmann [18], have also employed similar measures to examine the impacts of sanctions on trade. These studies have found significant effects, such as reduced exports of German agro-food to Russia due to counter-sanctions imposed by Russia. It is worth noting that while some economies may impose sanctions, Russia may seek to enhance its trade relations with other economies that have not implemented such measures.

This research primarily relies on secondary data sources, specifically a macroeconomic time series data set spanning 23 years. The data set focuses on Russia's top 49 trading partners, ranked by export sales, and covers a substantial period from 2000 to 2022. The study utilizes macroeconomic variables, as depicted in Table 1 below. These variables have been sourced from reputable institutions, namely the World Bank and World Development Indicators. The variable trade has been calculated manually. Trade is determined by adding the total value of exports to the total value of imports. The data for exports and imports has been sourced from the ITC Trade Map database. Moreover, the information on sanctions was sourced from the Global Sanctions Database, while the geographic distances from Russia to its trading partners were determined using the Distance calculator powered by distance.to.com. Table 1 describes the variables used in this study as well as data sources.

Table 1: List of Variables

Source: compiled by the author [38,28,22,13]……………

Table 2: Descriptive Statistic

The present study employs a combination of descriptive and inferential statistical methods to comprehensively analyze the data set. The study focuses on several key trade, such as export, import, and tariffs for trade. These variables have been widely recognized in previous research to have significant impacts on trade outcomes [24], [19].

Additionally, the study incorporates variables that potentially influence trade, including GDP, FDI, geographical distance, common border, FTA, and general tariffs of import and export. Previous research has established positive relationships between GDP growth and trade performance [24], as well as higher levels of FDI and increased trade flows [17]. Moreover, Anderson and Wincoop [3] argued the negative relationship between geographical distance has been found to negatively impact trade flows, while the presence of a common border and the existence of FTAs have been shown to enhance trade integration between countries [24]. General tariffs on imports and export have been found to have an inverse relationship with trade volumes [19].

Previous studies have consistently highlighted the adverse effects of sanctions on Russian trade, demonstrating their role in reducing both exports and imports, disrupting trade patterns, and imposing trade barriers [16; 31]. Moreover, research suggests that sanctions can hinder foreign direct investment (FDI) inflows, further impeding trade performance [2]. The imposition of sanctions is frequently associated with decreased bilateral trade, increased trade costs, and disruptions in supply chains [20]. In the context of the current study, the relationship between sanctions and Russian trade, with a specific focus on imports and exports, is examined. This analysis aligns with previous studies in the field, which have consistently observed that the imposition of sanctions negatively affects Russian trade volumes. Notably, Hufbauer and Oegg [26] conducted a relevant study that found a clear decline in both Russian imports and exports as a result of sanctions. These findings further support the examination of the relationship between sanctions and Russian trade.

To investigate the effects of sanctions on Russia's foreign trade, the study employs regression analysis, with a specific focus on diverse indicators of international trade as the endogenous variable and the imposition of sanctions as the exogenous variable. Additionally, two prominent estimation methodologies, the panel gravity model [3; 24] with the Difference-in-Differences (DiD) innovation [9; 17] is utilized to examine the impact of sanctions on Russian trade. The DiD model, in particular, enables the assessment of the temporal impact of sanctions by comparing trade patterns before and after their imposition.

The Hausman specification test of [22, p.68-78] is used to distinguish between fixed effect and random effect panel data modeling approaches. The Hausman test sets the hypothesis that there is no statistically meaningful difference between the estimators of the random effect and fixed-effect model. The fixed-effect model will be appropriate if the null hypothesis gets rejected and vice versa. The general equation of the Hausman test statistic is:

The hypothesis for the test is explained as:

H₀: The appropriate model is Random effects. There is no correlation between the error term and the independent variables in the panel data model.

H₁: The appropriate model is Fixed effects. The correlation between the error term and the independent variables in the panel data model is statistically significant.

If the p-value is found to be less than & equal to 0.05 then reject H₀ otherwise don’t reject.

In this research, the gravity modeling approach was used first to analyze the impact of sanctions and counter-sanctions on exports and imports of the Russian Federation. This model was first introduced by Tinbergen [37, p.54-78] which is based upon Newton’s Law of gravity and has been widely used in trade analysis [34]. The basic model for trade volume between the two countries (i and j) is given as follows:

Where,

G = constant.

F = trade volume (total exports + total imports).

D = distance between the two capital cities.

M = size of economies measured by GDPs.

An augmented version of the gravity model would be used to analyze the effects of sanctions on Russia’s trade.

The augmented gravity model that will be used in this research takes the form of:

In the above equation, the dependent variable Trade_ij shows the total trade volume between country’s sector i (Russia) and country j (trading partners). The total trade volume was manually calculated by adding total exports and total imports. Whereas the independent variable Trade_ij-1 which is the lagged term of Trade_ij denotes the total trade volume for a previous year between the two countries sectors. GDP_EU is the Gross Domestic Product of Russia’s trading partner, and GDP_RU is the Gross Domestic Product of Russia (home country). EX denotes the exchange rates. Distance denotes the distance between the two capitals. In our model, three dummy variables will be used that are: border, sanction, and sector.

The gravity modeling approach in this context allows us to undertake differences-in-differences estimations to identify the causal effects of sanctions and counter-sanctions [33].

With difference-in-difference (DID) technique we want to analyze the impact of sanctions on Russia's trade. It compares the changes in outcomes before and after the treatment between a treatment group and a control group. As we have panel data from 2000 to 2022, and the economic sanctions and counter-sanctions were made in 2014, a time variable was included to take into account the difference in trade before and after the imposition of the sanctions and counter-sanctions. These are specified as follows:

Where,

· α * (X_rjt) are the deterministic parts of the equations for X.

· Export_rjt is the RF’s export to country j in time t (i.e. before or after the sanction).

· Import_rjt is the RF’s import of country j in time t (i.e. before or after the sanction).

· Trade_rjt is the RF’s trade balance of country j in time t (i.e. before or after the sanction).

· sanctions_j indicates if country j imposes the sanctions on the RF.

· time_t indicates the period in which sanction is imposed.

It controls for the variables that potentially have impacts on export, import, and trade balance between the RF and a partner country by including the GDP, FDI, geographical distance, common border, FTA, and the exchange rates. The continuous variables are in log form. Therefore, our final export, import, and trade equations for estimations are as follows:

Where,

· is the GDP of partner country j at time t.

· GDP _RFrt is the GDP of the RF at time t.

· is the FDI of partner country j in the Russian Federation at time t.

· is the of Russian Federation at time t.

· distance_rj is the geographical distance between the RF and partner country j.

· border_rj is a dummy variable indicating whether the Russian Federation and partner country j share a common border (1 if they share a border, 0 otherwise).

· FTA_rjt is a dummy variable indicating whether there is a free trade agreement between the Russian Federation and partner country j at time t (1 if there is an agreement, 0 otherwise).

· sanctions_j denotes the dummy variables indicating whether partner country j has imposed sanctions on the RF (1 if sanctions are imposed, 0 otherwise).

· Exch_rate_rj denotes the exchange rate between the currencies of the Russian Federation and partner country j.

· time_t is a dummy variable representing whether the trade flow occurred before or after the imposition of sanctions in 2014 (1 if after, 0 if before).

Cross-sectional data are more likely to exhibit heteroscedasticity due to the underlying unique characteristics of each cross-section unit, which can cause the problem of an outlier in the dataset. Heteroscedasticity is also caused due to the omission of the important explanatory variable. To find out this, the study employs the Breusch-Pagan test which states the null hypothesis of no heteroskedasticity or homoskedasticity. The test uses chi-square (χ²) by giving the degree of freedom equal to the number of parameters in the regression. [21]

One of the important assumptions for regression analysis is regarding serial or autocorrelation of the error term. The Breusch-Godfrey test allows testing autocorrelation in the errors of a regression model.

To test the autocorrelation, this study uses the Breusch-Godfrey test that uses the null hypothesis as there is no serial correlation up to order p. This test utilizes a Lagrange multiplier; therefore, this test is also called the LM test. After obtaining regression estimates, the residuals are obtained which are regressed on explanatory variables as well as on autoregressive schemes.

The null hypothesis is,

The test statistics is LM = n R² which follows Chi-Square distribution with p degree of freedoms i.e. χ² (p). Rejection of the null hypothesis implies that there exists an autocorrelation and vice versa.

5. Results and Discussion

Multicollinearity is the presence of correlations between prognosticator variables. It inflates standard errors and confidence intervals, leading to erroneous estimates of individual predictor coefficients. In this study, the variance inflation factors (VIF) were utilized to quantify multicollinearity. Your regression findings will become less dependable as VIF grows. A VIF greater than 10 designates a significant association and should be regarded as a cause for worry. VIF values of more than 10 designate the presence of Multicollinearity. Some experts have suggested a threshold of 2.5 or higher. According to the study, the VIF convention was larger than 10.

Table 3 shows the variance inflation factor results. The results of multicollinearity show that none of the variable’s VIF exceeds 10 means a strong relationship does not exist among the independent variables.

Table 3: Variance Inflating Factor (VIF)

The study began with a unit root test at the level for each variable. In contrast to the alternative hypothesis, which was based on the idea that each variable is stationary, that is, variables do not have a unit root, the null hypothesis was thought to have a unit root All of the variables did not support the null hypothesis since the p-value was less than 0.05. The test results for the panel unit root are shown in the table below:

Table 4: Panel Unit Root Test

Source: Author’s Estimation

The below-presented Figure 1 illustrates the trend of trade and GDP within the Russian Federation over the given period. Despite the imposition of sanctions against Russia in 2014, It shows that GDP values have displayed a consistent upward trend. Figures show that the restrictive measures are having an effect. Both the World Bank and the IMF predicted that Russia's trade in commodities and services would fall sharply by 2022. Imports are expected to be greater in 2023 than in 2022, while exports are expected to fall further or remain almost unchanged, according to the World Bank and the IMF [2].

Figure 1:Trade and GDP of Russia

Source: Compiled by author [38, 28]

The Figure 2 provides Russia's Foreign Direct Investment (FDI) as a percentage of its Gross Domestic Product (GDP) spanning from 2000 to 2022. Improved economic circumstances is particularly consistent with the findings, which emphasizes the importance of economic stability and energy market dynamics in attracting foreign investors. The years from 2009 to 2011 saw a period of resurgence. This behavior is aligned with the study [11,p.3082-3095], which underscores the effectiveness of regulatory reforms in increasing FDI inflows. In 2015, FDI experienced a moderate decline. Falling oil prices and Western sanctions are the main cause of it. This decrease is consistent with the discoveries, which establishes a link between falling oil prices, Western sanctions, and the decline in FDI. The following years exhibit further decline. Pertinent research conducted by White & Johnson, 2019, give information on Russia's strategic measures to address these challenges. Russia's unwavering efforts to combat sanctions and traverse the COVID-19 epidemic began to yield fruit, as seen by the beneficial FDI trend seen in subsequent years. This pattern is substantiated by the research Miller & Thompson, 2021.

Figure 2: FDI of Russia (% of GDP)

Source: compiled by author [38]

In order to further highlight how sanctions, have an effect on Russia’s economic dynamics, the Figure 3 graphically explains the temporal patterns of exports, imports, and trade balances to further illustrate the impact of sanctions on Russia's trade dynamics. According to the latest GTAS Forecasting statistics, Russia is anticipated to be the world's 12th largest exporter and 21st largest importer in 2021. In 2021, the most important exported items were 'crude oil' (26% of total export value) and refined petroleum products (15% of total export value). Russia maintained its position as the world's leading exporter in 2021 by dominating the markets for 'wheat,' 'fertilizers,' and 'basic iron and steel.' It finished second in the exports of 'crude oil,'' refined petroleum products,' and mineral fuels.' Furthermore, Russia ranked third in 'coal and coke exports, while also contributing significantly as the fourth-largest worldwide exporter of 'aluminum.' [3]

Figure 3: Total Exports, Imports and Trade Balance of Russia

Source: compiled by author [28]

The exchange rate between Russian federation currency and various currencies from the time span 2000 to 2022, as well as the percentage change in the exchange rate compared to the previous year are shown in Figure 4. According to IMF, Russia has been hit hard by the dual shocks of declining oil prices and capital flow reversals along with this economic activity is projected to decline sharply in 2009 and to recover only tepidly in 2010. The year 2009 was marked by the global financial crisis. In the subsequent years, the exchange rate stabilized once more, with minimal movements until 2014. The exchange rate increased by 21% that year, which is ascribed in part to geopolitical events such as economic sanctions put on Russia in reaction to the annexation of Crimea and the crisis in Ukraine. [4] The repercussions are quite likely to have influenced shifts in market sentiment and trade dynamics. The most drastic change in exchange rates happened in 2015, with a 59% rise.

Figure 4: Exchange Rate of Russian Federation

Source: compiled by author [38]

The accompanying graph on Russian imports and exports presents some noteworthy trends and patterns. Russian imports have consistently increased throughout the years as shown in Figure 5, with the overall value growing from 2781 million in 2000 to 8831 million in 2021. Similarly, in Figure 6 Russian exports have shown a comparable development tendency, with total export value growing from 3109.07 in 2000 to 9590.35 in 2021 and 10421.24 in 2022, although with volatility amid economic crises, Sanctions have also had an influence on Russia's export dynamics.

Figure 5: Russian Imports from Different Regions

Source: compiled by author [28]

Figure 6 : Russian Exports to Different Regions

Source: compiled by author [28]

Table 5 presents the results from a regression model aiming to reveal the factors that influence the volume of exports, with a focus on the Russian context. The dependent variable, which reflects the quantity of exports, is influenced by several independent variables. Notably, the interaction term DiD appear as a significant component, showing that as the interaction between sanctions and time increases, the predicted value of exports decreases noticeably. This aligns with prior research which highlights the persistent nature of sanctions' influence on a country's export dynamics. This highlights the long-term impact of sanctions on the country's export dynamics. Economic indicators played critical roles in determining exports. The positive GDP coefficient indicates that when GDP increases, correspondingly increases the exports. In contrast, the negative coefficient for Foreign Direct Investment (FDI) of Russian Federation (RF) indicates a potential adverse link, emphasizing the subtle impact of foreign direct investment in Russia on export outcomes. Therefore, these results not only contribute to the understanding of Russian export dynamics but also align with and reinforce the findings of [33].

Table 5: Results of Export Model (Overall Sample)

Source: Author’s Estimation

Table 6: Results of Import Model (Overall Sample)

Source: Author’s Estimation

Table 7 shows the results of a comprehensive trade model applied to the entire sample, which provides useful insights into the factors influencing the extent of Russian trade across several industries. Notably, the DiD term interaction has a major negative impact, with a highly significant coefficient, indicating a significant reduction in predicted trade volume over time as a result of sanctions' persistent influence. [5] The trade model's findings support worries about long-term issues, such as chronic underinvestment and low productivity, which are compounded by sanctions. Economic indicators are important because GDP has a positive and very significant correlation, indicating that as GDP rises, trade grows significantly. In contrast, the GDPRF coefficient, while negative, reaches marginal significance, indicating a nuanced impact of regional GDP on overall trade patterns. Foreign Direct Investment of Russian Federation (RF), with its positive and substantial coefficient, highlights the importance of foreign investment in increasing trade volumes. Exchange Rate and Exchange Rate_RF, on the other hand, have major and opposing effects, emphasizing the importance of both global and regional currency dynamics in determining trade trends. Sector-specific coefficients demonstrate different affects within industries. Notably, sectors such as Chemical and Allied Industries, Foodstuffs, Machinery and Electricals, Metals, Mineral Products, and Textiles all have positive and substantial coefficients, showing a beneficial impact on trade. Conversely, sectors such as Footwear and Headgear, Raw Hides, Skins, Leather, and Furs, and Service have negative coefficients, indicating a negative influence on trade volumes in these industries. The constant term becomes significant, indicating a baseline amount of trading even when other variables are at their reference levels. The addition of fixed effects for Country, Sector, and Year, denoted as "YES," recognizes the importance of taking into account individual contextual impacts.

In essence, Table 7 provides a nuanced and comprehensive understanding of the intricate factors shaping Russian trade patterns, encompassing economic indicators, policy variables, and sector-specific dynamics within the broader trade landscape.

Table 7: Results of Trade Model (Overall Sample)

Source: Author’s Estimation

6. Conclusion

The conclusion of this extensive analysis on the impact of sanctions on Russia's commerce reveals a nuanced understanding of the complex forces at work in the global economy. The panel data regression analysis used in this work reveals intriguing trends and patterns in Russian trade, effectively capturing the movements that occurred prior to and during the imposition of sanctions. The quantitative insights gained from this analysis contribute to a better data-driven understanding of the impact sanctions have had on the volume and value of Russia's exports and imports. The qualitative analysis complements the quantitative findings by identifying the many aspects that impact the success or failure of Russia's trade policies implemented in response to the sanctions. This dual-method approach improves the study by providing a comprehensive view that goes beyond simple statistical correlations to uncover the subtle motivations and contextual effects that shape trade results. Drawing on the synthesis of these findings, it is clear that sanctions have caused significant changes in Russia's trade patterns. The combination of economic data, geopolitical events, and sector-specific concerns results in a complex tapestry that demonstrates Russia's trading landscape's resilience and adaptability. The persistent impact of sanctions, as demonstrated by the "DiD term", emphasizes the necessity of addressing temporal dimensions when interpreting trade variations.

To summarize, this thesis provides a solid platform for understanding the various effects of sanctions on Russia's commerce. By combining scientific rigour and qualitative insights, the study navigates the intricate interplay of factors that influence trade outcomes. The findings not only contribute to academic debate, but also have practical consequences for governments, entrepreneurs, and researchers navigating the ever-changing landscape of international trade in the face of geopolitical concerns. As the global economic stage undergoes dynamic transitions, this study adds significant insights to the ongoing discussion about sanctions, trade, and the intricate web of geopolitical and economic interactions.

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