Prague University of Economics and Business
Bachelor’s Thesis
2021 Anna Malkova
Prague University of Economics and Business
Faculty of International Relations Field: International Trade
Title of the Bachelor´s Thesis:
The Gravity Model of Trade between the Czech Republic and Russia
Author: Anna Malkova
Supervisor: Aliya Algozhina, Ph.D
D e c l a r a t i o n o f A u t h e n t i c i t y
I hereby declare that the Bachelor´s Thesis “The gravity model of trade between the Czech Republic and Russia” presented herein is my own work, or fully and specifically acknowledged wherever adapted from other sources. This work has not been published or submitted elsewhere
for the requirement of a degree program.
26/04/2021 Signature
Acknowledgements:
I would like to express my profound gratitude to my supervisor Aliya Algozhina, Ph.D. I would not have been able to conduct such research without the constant recommendations and good mentoring skills provided for me by my supervisor.
Content:
Introduction ... 1
1. Literature Review ... 4
2. Gravity model ... 9
2.1 Data description ... 10
2.2 The model ... 11
2.3 Empirical analysis... 11
3. Economic profile and bilateral trade between the Czech Republic and Russia ... 16
3.1 Economy of the Czech Republic ... 21
3.1.1 Macroeconomic indicators ... 21
3.1.2 Sectors of the Czech economy ... 24
3.2 Economy of Russia ... 28
3.2.1 Macroeconomic indicators ... 28
3.2.2 Sectors of the Russian economy ... 32
3.3 Czech-Russian trade review ... 37
Conclusion ... 42
References: ... 44
List of images, tables and graphs: ... 48
Introduction
In the modern world, national market economies are developing in close cooperation with each other. There does not exist any country that can produce the entire range of products, provide itself with hundreds of different services, highly qualified specialists, investment, and labor resources. International trade allows developing countries to acquire technologies from developed countries in order to increase their productivity and to increase the international competitiveness of the economy. In today's world, foreign trade and mutual economic cooperation are the main growth factors and the ongoing development of the economy of each country.
Relations between the Czech Republic and Russia are an example of successful economic cooperation in the post-socialist space. For both countries, it was important to overcome the difficulties of the transition period, build an innovative economy and effectively integrate into the global system of economic relations.
In the development of modern Czech-Russian economic relations there were rapid ups and downs that were caused by economic factors as well as political and historical contradictions.
For a long time, the centralized economy of the Czech Republic was based on the production of consumer goods in the member countries of the Council for mutual economic assistance (COMECON1). After political changes in the 1990s, the European market became the main market for Czech products.
Today, the Czech Republic is still highly dependent on foreign trade with the European Union countries. The main trading partner is Germany, due to the fact that a large number of Czech enterprises belong to German holdings.
In order to ensure economic security and increase the competitiveness of Czech goods, services and capital, since 2006 the state has taken measures to diversify foreign trade partners, stimulate the flow of foreign capital into strategic sectors of the Czech economy, develop Czech
1 An economic association of East European countries founded in 1949 and analogous to the European Economic Community. With the collapse of communism in Eastern Europe, the association was dissolved in 1991.
regions and improve the welfare of citizens. The Russian Federation is one of the twelve countries with which cooperation is recognized as a priority.
At the present stage, Czech-Russian economic relations in the investment, innovation and technology spheres are based on the interaction of private Czech companies directly with business structures in the Russian Federation in the energy sector, exists contracts between Russian monopolies and large Czech companies.
In recent years, Czech-Russian relations have seen a noticeable upturn and economic cooperation has revived. The Czech Republic has attracted the interest of Russian entrepreneurs for a reason – it is ahead of many other countries in terms of qualitative and quantitative parameters of economic development.
At present, trade is actually the main area of economic relations between the Czech Republic and Russia. Today, Russia ranks 12th in the Czech Republic's foreign trade. The Czech Republic, in turn, is one of the 20 leading trade partners of Russia (Workman, 2019).
The exchange of factors of production contributes to the development of the welfare of both countries. At present, Czech-Russian relations are acquiring the features of dynamically developing mutually beneficial cooperation.
A significant place in international economic integration is occupied by foreign trade, that overcomes the limited resources and narrowness of the internal regional and national market, creates the possibility of organizing mass production, increases the degree of equipment utilization, increases the efficiency of introducing new equipment and technologies, increases savings, economic growth rates and more efficiently uses countries’ resources.
In this paper, I will be using a useful tool that determines bilateral trade between countries.
It will be the gravity model of trade since it has verified itself to be relevant for the analysis of bilateral trade.
According to Newton's law of universal gravitation, the force of attraction of bodies is directly proportional to the square of the distance between them. Since the mid-20th century, due to the growing popularity of quantitative methods in economic geography, an attempt has been made to use an analog of this formula to describe the flows of goods and services between countries and regions. Later, as a development of this approach, the American economic
geographer W. Izard tried to apply Einstein's theory of gravitation to explain the interaction of economic objects located on the Earth's surface.
The main idea behind the approach of the gravity model of trade is a foreign trade turnover, which is directly dependent on the economic potential of the trading countries (GDP) and on the distance between them. The size of the economy determines supply and demand.
The distance between partners is important in terms of the costs of trade in goods, which increase with increasing distance between countries.
The gravitational interaction model is one of the main models for econometric analysis of integration and trade agreements between countries.
This paper constructs the gravity model of trade that examines bilateral trade between the Czech Republic and Russia over twenty-five years period from Q1 1995 to Q1 2020.
The purpose of this thesis is to identify important factors that influences trade between two countries and to test if bilateral trade between the Czech Republic and Russia is significant.
Thesis is divided into three main chapters. The first chapter touches on theoretical foundation of the gravity model of trade. The second chapter is aimed at constructing and analyzing the gravity model itself. The third chapter presents the economic profile, bilateral trade, as well as economic relations between the two countries. Its purpose is to describe bilateral and economic overview of the Czech Republic and Russia.
1. Literature Review
Gravity trade models were basically established as a beneficial tool of econometrics for the purpose of analyzing trade flows between different countries, which has become quite popular because of good empirical results.2 Not long-ago, gravity models of trade did not have a complete theoretical approval and a strict analytical conclusion of the hypothesis that were tested. But despite that, in the modern world, there are large numbers of works that assume the gravitational specification of the trade model from the premises of the most largely used basic theories of international trade in economic research. Eaton and Kortum (2002) mentioned David Ricardo’s theory of comparative advantages, where differences in production technology between countries play a decisive role. Deardorff (1998) indicated the Heckscher - Ohlin model (that was formalized in Samuelson’s (1948) paper) where trade occurs thanks to differences between countries in the endowment of production factors. Another example is Krugman's (1985) theory (that is also known as the "new theory of international trade"), which’s necessary component is monopolistic competition among other producers and thus the tendency for consumer product diversity.
One of the first papers to use the gravitational model of foreign trade was Tinbergen (1962). He estimated model (7) in logarithmic form using the method of least squares, where a country's GDP is a measure of the economic size, distance between countries is used as a measure of trade costs, fictitious variables of having a common border, and membership in trade unions. Modern researchers also use population size, country area, and GDP per capita as alternative measures of the size. Following factors are considered to be determinants of trade costs (they are systematized in Anderson and Wynkoop (2004)): customs tariffs; transport costs; membership in currency and trade unions, exchange rate volatility; political unions, military blocs; language barriers, colonial ties, shared religion; information barriers; contract costs; geographical variables (island state, no access to the sea).
2 Empirical works that use the gravitational equation usually work with aggregated trade flows. Models of foreign trade of certain types of goods are discussed for example in: Kadochnikov, Sinelnikov-Murylev and Chetverikov (2004).
At various years and decades there have been made various experiments with varying intensity in order to develop theoretical basis for the gravity model of trade. Mostly it was a research with already pre-known results that authors wanted to confirm econometrically. These papers are included in: Leamer and Stern (1970) with a probabilistic model, another example was mentioned by Leamer (1974), where variables were used from the gravitational equation and the Heckscher-Ohlin model. Also, it was mentioned by Anderson (1979), based on micro- foundations and the Armington’s assumption3, in Bergstrand (1990) and many others.
The first successful attempt to derive the gravitational equation of trade based on microeconomic theory became the work of Anderson (1979). In his general equilibrium model, the key was Armington's assumption that claimed that the same type of goods, which differ in the place of production, are imperfectly replaceable by agents who demand them (their preferences are usually set by the utility function with constant elasticity of substitution).
Improved versions of Anderson’s model were proposed in Bergstrand (1985) and in Anderson and Wynkoop (2003). The gravitational equation of Anderson and Wynkoop (2003) deserves special attention, since authors were able to present it in a simple and elegant form, which has already become canonical.4
3 Armington assumption - a special form of aggregation of consumption volumes of a single product, parts of which are produced in different countries.
4 In the model of Anderson and Wynkoop (2003), it is assumed that each country produces its own unique product, the supply of which is fixed. The representative consumer of the country maximizes the utility function with a constant elasticity of substitution from the consumption of the complete product set. Equations (1)–(2) are derived from solutions of consumer problems and the conditions of the balance of supply and demand in the market for each product. The case of a single product produced by a country is considered by authors only for simplicity of presentation. If a country produces several products, the resulting gravity model for the export of a single product will have a similar form.
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In equation (1) 𝑥𝑖𝑗 - is the value of exports from region i to j; 𝑦𝑖= 𝛴𝑥𝑖 - is the total revenue of region i; 𝑦𝑤 =𝛴𝑗𝑦𝑗 - is the nominal income of the world economy; σ - is the elasticity of substitution between goods of different countries (σ > 1); 𝑡𝑖𝑗 – is the cost of transportation from i to j; 𝑃𝑗 – is consumer price index in country j. Equation (2) contains some of the variables taken from (1) with the exception of 𝜃𝑗 – which is the share of country j in the world GDP.
Since 𝑃𝑖 and 𝑃𝑗 aggregate all bilateral trade barriers faced by the exporter and importer, respectively, these values are called indicators of multilateral trade resistance: 𝑃𝑖 – resistance to exports from region i, 𝑃𝑗 - resistance to imports to region j. The relationship between variables is explained in the following way: if two different regions i and j have difficulties in trade with all the other regions, these two regions will have more incentives for mutual trade.
Later it became clear that the dependence of the gravitational type can be derived from the assumptions of any of the most well-known theories of international trade - the classical model of the Heckscher – Ohlin; the Ricardian model; the new theory of international trade.
Chaney (2008); Helpman (2008); Eaton (2011) mentioned the latest theory of trade, where differences in firms’ productivity allow to allocate an extensive and intensive component of exports. Despite the different initial assumptions and interpretation of the parameters, gravity models of the mentioned above works are united by the fact that they can all be reduced to the form (1).
The discrepancy between the theoretically based model (1) and the "traditional" model (7) has led to the development of a number of alternative methods for empirical estimation of gravitational specifications that correctly take into account multi-sided resistance mentioned by Anderson and Wynkoop (2003); Baier and Bergstrand (2009), etc. The usage of methods has significantly improved the quality of the assigned estimates of foreign trade factors. In some cases, it even led to a revision of earlier empirical results (for example, the "state border paradox", which means that the volume of trade between two regions of the same country exceeds the volume of trade between two regions of different countries). In the future, methods for estimating gravity specifications were also improved.
One of the most famous works of the gravity model of trade is the work written by McCallum (1995), in which an observational result was obtained, and it became known as the
"border puzzle". It studied the effect of the national border barrier on trade in Canada's
provinces and states of the United States between them in 1988. From the estimation of the traditional gravity model, it follows that the trade between two provinces of Canada should be 22 times higher than trade between a province and a state of the United States. Anderson and Wynkoop (2003), including multi-lateral resistance in the model, showed that the border effect for the provinces of Canada in the work of McCallum (1995) is overestimated by more than 2 times. In the paper written by McCallum (1995), the following relationship (3) was estimated:
𝑥𝑖𝑗 - is the export from region i to region j, y - is regions’ GDP, 𝑑𝑖𝑗 - is the distance between regions, δ - is a dummy variable that equals one if regions are located in the same country and equals zero if they are located in different countries. This equation (3) was estimated from the 1988 trade data according to trade of ten Canadian provinces and thirty US states.
Implementation of methods for accounting the problem of multilateral resistance in the gravity model written by Anderson (1979), Bergstrand (1985), Bayer (2009), and Wynkoop (2003) is somehow connected with technical difficulties. The simplest method that was first used in the paper written by Harrigan (1996) is the most popular method.
It is possible to see that in the original empirical log-linear model for spatial data, the unobservable values are 𝑙𝑛 𝛱𝑖1−𝜎 and ln𝑃𝑗1−𝜎 . They represent the individual fixed effects of countries i and j. Therefore, this model can be rewritten as:
In this equation (4) 𝑎𝑖 = ln 𝛱𝑖1−𝜎 and 𝑎𝑗 = ln𝑃𝑗1−𝜎 are estimated parameters; 𝐷𝑖 and 𝐷𝑗 are dummy variables of the exporter and importer.
According to the article written by Anderson and Wynkoop (2003), two important problems are resulted from the lack of clear theoretical arguments in previous econometric
(4) (3)
papers and consequently not correct specification of the gravitational equation. The first one is caused by the prejudice of coefficient assessment that was resulted from variables that were missing in the regression equation. The second one is partially an effect of the first problem and consists of not correct results of research in the field of comparative statics.
The gravity model of trade that was developed in the article by Anderson and Wynkoop (2003), in a certain sense, became a classic because it was later widely used in scientific research. In addition, some modifications of the model that are developed by authors are suggested in a number of papers. So, in Ackerman and Forslid (2009) as additional variable in the gravity equation was introduced the level of GDP per capita, in Chen and Novy (2009) was announced multilateral resistance, that was changing over the period of time, and in Novy (2013) was introduced a modification of the gravity model for panel data.
In Baier and Bergstrand (2009) is introduced a method that significantly simplifies the econometric evaluation of the theoretical gravity model of trade that was written in Anderson and Wynkoop (2003) and differs from McCallum (1995) (3). Writer’s concept of the article was clear and very effective: to develop definitions for price indices 𝑃𝑖 that describes the "multi- sided resistance" into a Taylor series using the formula 𝑓(𝜉𝑖) = 𝑓(𝜉)+ 𝑓′(𝜉)(𝜉𝑖 − 𝜉)2+ 𝑜((𝜉𝑖 − 𝜉)2) at the point 𝜉.
Due to this, the gravitational equation was reduced to the form:
In (5) 𝑑𝑖𝑗 - is the distance between regions i and j; 𝐵𝑜𝑟𝑑𝑒𝑟𝑖𝑗 - is a fictitious variable for the existence of a state border that equals one if two regions are located in different countries, and zero if they belong to the same country.
Exists a large number of researches that examine the gravity model of trade. According to the investigated papers, the most popular estimation technique is OLS (ordinary least squares). This technique was mostly used in the 2000s. Examples of these techniques are described in: Endoh (1999), Rose (2000), Musila (2005), Sohn (2005), Elliot (2007) and many others. The second widely used estimation technique is OLS with fixed effects, which research was made between 2003 and 2007 by: Fukao (2003), Gopinath and Echeverria (2004),
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Kandogan (2005), Nowak-Lehmann (2007) and others. Other popular estimation techniques are OLS with two-way fixed effects: Baltagi (2003); OLS with two-way random effects:
Carrere (2006); GLS (generalized least squares) in Kalirajan (2007) and many other techniques.
The most common explanatory variables that were mentioned in the papers are the following: bilateral trade flows, exports, and imports. The most common dependent variables that were mentioned are: GDP; GDP per capita; distance between countries; currency union;
common language; common border; population; trade agreements; the similarity between countries; colony.
2. Gravity model
Currently, the gravity model is an important tool for empirical analysis of international and interregional trade flows. The widespread usage of the model is explained by the fact that the gravitational equations are of high quality: the coefficient of determination 𝑅2 is in the range (0.8;0.9), which is 80-90% of the variation in exports or imports of goods or services. That is due to the changes in the equation factors. Also, coefficients for variables have the correct sign and are significant, so gravity models can be considered as one of the most stable relationships that exist in the economy.
The gravity model in economics is based on Newton's law of universal gravitation, which states that the force of gravitational attraction F between two physical bodies is directly proportional to the masses of these bodies, 𝑚1 and 𝑚2 (6), and inversely proportional to the square of the distance d between them where G is the gravitational constant.
Using equation (6) as a basis, Tinbergen (1962) claimed the existence of a relationship (7) between the value of exports from country i to country j (𝑥𝑖𝑗), the economic size of the
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exporter and importer (𝑦𝑖 and 𝑦𝑗), and the cost of trade between countries (𝑡𝑖𝑗). Where α > 0, β
> 0, γ > 0. This relationship is called the gravity model of international trade.
The classical form of the gravity model (7) suggests a positive relationship between the volume of trade and GDP of respective countries, which characterizes the size of their economies, and a negative relationship between the volume of trade and distance, which characterizes the cost of delivering goods from the market of one country to the market of another.
The simplest form of the gravity model was described in the following way (8): 𝐸𝑖𝑗 - is the export from country i to country j, 𝑌𝑖 - is GDP of country i, 𝑌𝑗 - is GDP of country j, 𝐷𝑖𝑗 - distance between countries i and j, 𝛼𝑖 - estimated export volume elasticity coefficients for the corresponding variables.
2.1 Data description
In my estimation, I study bilateral trade between the Czech Republic and Russia during the time period Q1 1995 – Q1 2020. The following variables will be used: imports and exports from the Czech Republic to Russia and vice versa; GDP of the Czech Republic and Russia;
population of both countries; distance between two capitals; trade agreement between countries.
Bilateral trade data is taken from the International Monetary Fund. The data of GDP of both countries is taken from the World Bank, Czech Statistical Office, and Federal State Statistics Service since in different models I used different currencies. The data on the population size is taken from the World Bank. Distance between Prague and Moscow is taken from Google Maps. Fictitious variable is trade agreement WTO.
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2.2 The model
GDP is considered to be the most essential variable in international trade since it shows countries’ economic size. In the model, GDP of both countries is supposed to be positively related to the trade. It is anticipated that the higher is GDP of a country, the more it will trade with its partner country.
Another important variable is distance between capitals. This variable is supposed to give a negative effect to international trade due to transportation costs, consequently reducing trade.
Population variable is included in the gravity model as it shows the market size of both countries. Thus, the bigger is the population, the more countries will trade with each other. This variable is expected to provide a positive impact on the model.
The gravity model of trade between the Czech Republic and Russia will be built in a statistical software EViews that is suitable for time series analysis. All regressions were obtained by the method of ordinary least squares.
The formulation of the model can be generalized in a following way:
𝑋𝑖𝑗 = 𝑌𝑖𝛽1𝑌𝑗𝛽2𝐿𝑖𝛽3𝐿𝑗𝛽4𝐷𝑖𝑗𝛽5𝐴𝛽𝑖𝑗6
𝑋𝑖𝑗 is the volume of exports from country 𝑖 to country 𝑗, 𝑌𝑖 and 𝑌𝑗 represent GDP of country 𝑖 and 𝑗, 𝐿𝑖 and 𝐿𝑗 represent populations of country 𝑖 and 𝑗, 𝐷𝑖𝑗 is the distance between two capitals, 𝐴𝑖𝑗 indicates dummy variable WTO and 𝛽s are parameters of the model.
I transformed equation (9) into a linear form (10) by logarithmic transformation. The linear form of the model can be written as:
log(𝑋𝑖𝑗) = 𝛽1𝑙𝑜𝑔(𝑌𝑖) + 𝛽2𝑙𝑜𝑔(𝑌𝑗) + 𝛽3 𝑙𝑜𝑔 (𝐿𝑖) + 𝛽4log(𝐿𝑗) − 𝛽5log(𝐷𝑖𝑗) + 𝛽6 log (𝐴𝑖𝑗)
2.3 Empirical analysis
The first step in constructing any econometric model is to plot all variables and see their descriptive statistics, to spot any errors in data and make initial observations about trending
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variables. To test formally for a linear trend or unit root, the Augmented Dickey-Fuller (ADF) test is run for all variables used in the model. If the null hypothesis of ADF unit root test is not rejected, then the variable is trending or non-stationary, and therefore it needs to be transformed, since only stationary variables can be used in simple OLS regressions.
Table 1 shows the results of ADF test. The null hypothesis of unit root for all variables is not rejected.
Table 1. Unit root test.
Test ADF
Czech Republic’s GDP (in billions of dollars) -1.666732 (0.7586) * Russia’s GDP (in billions of dollars) -1.826121 (0.6846) * Czech Republic’s GDP (in millions of Czech crowns) -2.402279 (0.3760) * Russia’s GDP (in billions of Russian rubles) -2.140582 (0.5165) * Czech Republic’s population -2.598346 (0.2821) *
Russia’s population -0.512764 (0.9815) **
Notes: ***/**/* denotes rejection of the null hypothesis at 1%/5%/10% level. Probabilities are in brackets.
The results of ADF test that are presented in Table 2 rejects the null hypothesis for all variables. It means that all variables are stationary and now they can be used for estimating regressions of the gravity model of trade.
Table 2. Unit root test.
Test ADF
Czech Republic’s GDP (in bil. USD) -8.010888 (0.0000) ***
Russia’s GDP (in bil. USD) -7.461105 (0.0000) ***
Czech Republic’s GDP (in mil. CZK) -6.858694 (0.0000) ***
Russia’s GDP (in bil. RUB) -6.235256 (0.0000) ***
Czech Republic’s population -10.57443 (0.0000) ***
Russia’s population -9.668012 (0.0000) ***
Notes: ***/**/* denotes rejection of the null hypothesis at 1%/5%/10% level. Probabilities are in brackets.
The main results of the gravity model are presented in Table 3, while in Tables 4-5 additional estimations are made as the sensitivity checks. The column “Coefficient” measures the effect of the explanatory (independent) variable on the dependent variable. The column
“Probability” shows the significance level of that effect. The effect is significant at 1%, 5%, and 10% significance levels if the probability coefficient is below 1%, 5%, and 10%, respectively. R-squared measures the overall success of regression and shows the percentage of variation in the dependent variable explained by the independent variables. Adjusted R- squared is more precise and takes into account a number of explanatory variables. Durbin- Watson (DW) statistic tests the serial correlation in the residuals of the model, i.e., whether the residuals commove together. DW has to be around 2, which means that there is no autocorrelation, otherwise the coefficients are wrongly estimated. If it is lower than 2 (around 0), then there is a positive autocorrelation. If it is higher than 2 (around 4), then there is a negative autocorrelation. In both latter cases, the linear model is invalid, since some explanatory variables might be missing or the functional form of relationship is not linear, therefore cannot be estimated by OLS. The column “N” means number of observations.
In the regression 1, the dependent variable is the exports from the Czech Republic to Russia. In this regression I used Czech Republic’s GDP in billions of dollars and Russia’s GDP in billions of dollars.
Regression 1 has 3 significant variables. The highly significant variable is GDP of Russia.
Its coefficient has a value of 0,9%. It means that if GDP of Russia increases by 1%, it leads to an increase in Czech exports to Russia by 0,9%. Its probability coefficient has a value of 0%
and is highly significant at the 1% level. Another significant variable is population of the Czech Republic. Its coefficient has a value of 32,8% and means that if the population of the Czech Republic increases by 1%, it leads to an increase in exports to Russia by 32,8%. Its probability coefficient has a value of 2,9% and is significant at the 5% level. The last significant variable is population of Russia. Its coefficient has a value of 17,8% and means that if the population of Russia increases by 1%, it leads to an increase in exports from the Czech Republic to Russia by 17,8%. Its probability coefficient has a value of 1,8% and is significant at the 5% level.
This regression can be interpretated in the following way: if Russia’s GDP is on the high level, then Czech’s exports to Russia increases; the bigger is the Czech Republic’s population, the more it exports to Russia; the bigger is the Russian population, the more Czech Republic exports to Russia.
Table 3. Gravity model. Regression 1.
Variable Coefficient Probability
Distance 0.000260 0.9176
GDP of the Czech Republic 0.338750 0.2339
GDP of Russia 0.938113 0.0000
Population of the Czech Republic 32.86921 0.0292 Population of Russia 17.88821 0.0180
WTO -0.041686 0.2014
R-squared 0.424606
Adjusted R-squared 0.394000
Durbin-Watson stat N
2.462697 100
In the regression 2, the dependent variable is the exports from the Czech Republic to Russia. In this regression I used Czech Republic’s GDP in millions of Czech crowns and Russia’s GDP in billions of Russian rubles.
Table 4. Gravity model. Regression 2.
Variable Coefficient Probability
Distance -0.001875 0.7617
GDP of the Czech Republic 0.163935 0.7577
GDP of Russia 0.141337 0.3488
Population of the Czech Republic 40.39860 0.0320 Population of Russia 14.66130 0.1102
WTO -0.051103 0.2609
R-squared 0.101672
Adjusted R-squared 0.052314
Durbin-Watson stat N
1.952291 97
The only significant variable of the 2nd regression is population of the Czech Republic.
Its coefficient has a value of 40%. It means that if the population of the Czech Republic increases by 1%, it leads to an increase in exports to Russia by 40%. Probability coefficient has a value of 0,032 and is significant at the 5% level, which means that this variable influences positively on trade between the Czech Republic and Russia. In other words, Czech exports are dependent on the population of the Czech Republic and its increase causes an increase in exports to Russia.
In the regression 3, the dependent variable is the exports from the Czech Republic to Russia. In this regression I used Czech Republic’s GDP in millions of Czech crowns and Russia’s GDP in billions of Russian rubles in another difference. In Table 4, it was a fourth difference of GDPs because of their seasonality and to remove it properly along with making the series stationary at the same time, the fourth difference has been taken. Whereas in all other tables, it is a first difference of all variables, including GDPs.
The only significant variable in the 3rd regression is GDP of the Czech Republic. Its coefficient has a value of 1,2%. It means that if the GDP of the Czech Republic increases by 1%, it leads to an increase in exports to Russia by 1,2%. Probability coefficient has a value of 0,02 % and is highly significant at the 1% level. It can be interpretated in the following way - the bigger is the Czech Republic’s GDP the more it exports to Russia.
Table 5. Gravity model. Regression 3.
Variable Coefficient Probability
Distance -0.000454 0.8682
GDP of the Czech Republic 1.265630 0.0002
GDP of Russia 0.290687 0.1563
Population of the Czech Republic 16.30688 0.2814 Population of Russia 4.417335 0.5584
WTO -0.037707 0.2595
R-squared 0.426087
Adjusted R-squared 0.395560
Durbin-Watson stat N
1.661218 100
3. Economic profile and bilateral trade between the Czech Republic and Russia
Russia and the Czech Republic are long-standing partners in the field of trade and economic relations. Not only did not they stop trading with each other after the Czech Republic joined the EU, but also their relationships received a new dimension. Over time, they moved from the specifics of the COMECON era to the more rational and pragmatic ones at the present stage.
The Czech Republic is one of Russia's important trade and economic partners in the Central and Eastern Europe. Despite the small size of its own economy (relative to other EU members), the Czech Republic is a major consumer of Russian raw materials and primarily, energy.
Despite the cultural, historical, political, and diplomatic differences, Russia and the Czech Republic have a lot in common. Russia and the Czech Republic occupy similar positions in the international division of labor, acting as importers of advanced technologies and capital.
In foreign policy relations, Russia and the Czech Republic also have similar goals: to deepen integration processes at the regional level and strengthen partnership relations in the international arena.
The list of areas of possible mutually beneficial cooperation between Russia and the Czech Republic is extremely extensive and diverse, both in trade, scientific and technical cooperation, as well as in the implementation of investment projects.
The Czech Republic is not a big country in the EU structure, but it has a well-developed diversified economy. Most of the GDP that is produced in the country is sold on foreign markets. The forward movement of the Czech economy is largely determined by the state and development of its foreign economic relations and their export orientation.
How does the Czech Republic manage to successfully operate in such an important area?
Its recipe for a successful operation (at least for itself) is associated with an extremely simplified system of direct and hidden investments in the economy, the privatization of trade, engineering, and other industries. Its result is the development and prosperity of most of the country's major economic entities.
A number of industries can serve as a clear manifestation of this policy. For example, the CHKD GROUP concern, which became the property of German machine builders after complete reconstruction, became one of the main European manufacturers of medium-speed locomotives, although not always under its own brand. Another case is "Skoda-Volkswagen", which acts both as an assembly site and as a trademark based on the know-how and technologies of the parent company. Another example is the “Batya” company. When the production was dying, it became part of an international network with all the technological and financial advantages. Even the pride of the Czech Republic – the brewing industry is largely owned by Heineken N. V, Anheuser-Busch InBev, and SABMiller.
Russia, as a long-standing partner of the Czech Republic, annually occupies one of the leading positions in its foreign trade.
In 1999-2010, it was ranked 7th, 8th, and 10th position in the turnover of the Czech Republic and 12th in 2019 (Workman, 2019). Russia's export position is more modest – from 15th place in 2000 to 9th place in 2010 and was in top 20 in 2019 (Workman, 2019). But imports to the Czech Republic show a strong dependence on supplies from Russia – the lowest position of the Russian Federation was taken in 2004 (7th place), and the highest, 2nd place, in 2000, 2001 and 2005 (Workman, 2019).
The commodity structure of the Czech Republic's foreign trade has not changed significantly since joining the EU in 2004. The leading item of both exports and imports is machinery, equipment, and vehicles.
Russian exports are traditionally based on mineral products. In 2019, Russia exported mineral fuels, oils, distillation products. The second, third and fourth places were occupied by not specified commodities, aircraft, spacecraft, inorganic chemicals, precious metal compound, isotope.
Table 6. Russia exports to the Czech Republic
Source: Trading economics, 2019.
Machinery, nuclear reactors, boilers were imported by Russia from the Czech Republic in 2019, as well as vehicles other than railway, tramway, electrical, electronic equipment and pharmaceutical products.
Table 7. Russia imports from the Czech Republic
Source: Trading economics, 2019.
Source: Trading economics, 2020.
At present, it is possible to state a small increase in trade between both countries after 2014 applied sanctions (Figure 1).
This is primarily due to the relatively low world prices for Russian energy carriers compared to the high prices for them in the pre-crisis years.
The most important component of the entire complex of bilateral trade and economic relations is cooperation in the field of energy. Its roots go back to the times of COMECON and the USSR. Russian (then Soviet) energy supplies played a major role in the economic development of the Czech Republic and had a positive impact on its macro-economic indicators.
Energy infrastructure facilities built according to the Soviet technologies and using Soviet components still provide not only supplies, but also transit of energy carriers. Moreover, the importance of Russia in these deliveries to the Czech Republic is as great today as it was 20 years ago (Figure 2).
Figure 1. Russia exports to the Czech Republic
Figure 3. Figure 2. Russia exports to the Czech Republic
Figure 3. GDP of the Czech Republic (in bil. USD) between 2010 – 2020.
Figure 4. Employment rate in the Czech Republic (in %) between 2017 – 2020Figure 3. Figure 3. Russia exports to the Czech Republic
Figure 3. Figure 4. Russia exports to the Czech Republic
Figure 1. Russia exports to the Czech Republic (in bil. USD) between 2010 – 2019.
Figure 2. Czech Republic imports from Russia (in bil. USD) between 1994-2019.
Source: Trading economics, 2019.
3.1 Economy of the Czech Republic
The Czech Republic is one of the most industrially developed countries of Central Europe. The country covers an area of 78,866 square kilometers, which is 0.05% of the size of the total world territory (114th in the world).
3.1.1 Macroeconomic indicators
This part of the paper, as well as 3.2.1 will be describing macroeconomic indicators that were relevant for the gravity model.
GDP
Due to the new cov-19 coronavirus pandemic in the second half of 2020, the Czech economy experienced the deepest downturn in its history. Gross domestic product decreased, in real terms, by 11% (Sidorov, 2020). Numbers provided in sections 3.1.1 and 3.1.2 are based on the Czech statistical report (Sidorov, 2020). The main contribution to the very deep economic downturn was the reduction in the surplus of the balance of foreign trade in goods and services. This was related to the suspension of operations in some domestic and foreign enterprises. Thus, in addition to the cessation of exports, the demand from foreign buyers also decreased. Expenditure on gross capital formation also had a negative impact on GDP growth.
The investment activity itself in the 2nd quarter of the year decreased by 4.8%.
Source: Trading economics, 2020.
Figure 3. GDP of the Czech Republic (in bil. USD) between 2010 – 2020.
Domestic consumption also contributed to the decline in GDP, in particular household consumption, which fell by 7.6%.
Population and labor market
The total population of the Czech Republic is 10,7 million. During the pandemic, total employment fell by 1.4%, falling mainly in the manufacturing and services sectors. However, the general unemployment rate rose only slightly due to the large share of people who left the labor market during the crisis (foreigners, working seniors). Employment rate in the Czech Republic reduced to 74%.
Source: Trading economics, 2020.
The average salary increased slightly (on a less than one percent). The development was also significantly diversified. The greatest decline in average wages was in the accommodation, catering, and hospitality sector, but wages also fell in other services. The strong decline in wages also affected the manufacturing industry. On the contrary, average wages increased in information and communication activities and in services dominated by the public sector or in the energy sector. However, due to the significant price growth, the average wage in the economy actually fell.
Imports and Exports
Figure 4. Employment rate in the Czech Republic (in %) between 2017 – 2020.
In the 2nd quarter of 2020, exports of items that are key to the Czech economy fell most significantly, and the most affected industry was the production of motor vehicles. In the 2nd quarter, the value of exports of motor vehicles decreased by 117 billion CZK (43.6%). The second biggest fall was in machinery and equipment sectors. Exports also fell in the fields related to the production of automobiles, electrical equipment, rubber, and plastic products.
Further strong falls were recorded for the metal products, base metals, and chemicals.
The evolution of imports and exports was very significant in terms of the material structure. In the 2nd quarter, the value of imports of motor vehicles fell the most. Very strong declines were also recorded for machinery, equipment, oil, gas, and base metals, mainly due to the falling prices. For several quarters, the decline in the cost of importing chemicals and drugs has deepened to 13.8 billion CZK (-19.2 %). On the contrary, the most significant increase in the value of imports since 2018 was for computers of electronic and optical devices. A series of strong increases lasting from December 2019 was interrupted only by March 2020. Imports of textiles also had an extremely high increase. Most of all imports of textiles grew in April and May.
A more pronounced decrease in the value of exports compared to imports also led to a deterioration in the balance of foreign trade in goods. Surplus in the 2nd quarter of 2020 reached 11.4 billion CZK and decreased by 45.6 billion CZK. In the direction of deterioration of the balance sheet in the second quarter, the most influential trade was with China, Spain, and Great Britain. The significant decrease in the positive trade balance was with France and Slovakia.
Trade with Russia improved, also due to the fall in oil and gas prices, the balance ended up in a trade surplus. Trade with Poland, Japan and the Netherlands also contributed to the improvement in the balance of trade.
The decrease in the trade surplus of motor vehicles contributed most to the deterioration of the balance sheet in the 2nd quarter. Further deepening of the deficit in computers, electronic and optical devices also led to the deterioration of the surplus in electrical equipment by 9.2 billion CZK. A significant deterioration in the balance sheet was also observed in textiles, rubber and plastic products, other means of transport, machinery, and equipment. On the other hand, the impact of prices has led to a significant improvement in the balance of trade in oil, natural gas, and base metals. The deficit in chemicals also decreased.
Source: Trading economics, 2020.
Source: Trading economics, 2020.
3.1.2 Sectors of the Czech economy
Manufacturing industry
The manufacturing industry is one of the most important sources of gross domestic product in developed economies. The decline in the value added in the domestic economy by almost a half was the result of a noticeable decline in the manufacturing industry in the 1st half
Figure 5. The Czech Republic’s exports (in mil. CZK) between 2016 – 2020.
Figure 6. The Czech Republic’s imports (in mil. CZK) between 2016 – 2020.
of 2020 due to coronavirus. At the same time, this industry, due to its importance, dragged down the entire economy in both the 1st and 2nd quarter of 2020.
Non - manufacturing industry
Non-manufacturing industries also showed weaker performance, by more than a tenth.
The effect of a few weeks of production interruption, motivated by hygiene measures, was felt, and this also reduced the demand.
Construction industry
The construction industry was much better developed, where the consequences of the coming economic downturn were initially not noticeable. In the 1st quarter of 2020, its performance increased slightly due to the high stock of orders and favorable weather. The already difficult administrative process of building preparation was further complicated by the declared state of emergency and many projects were suspended. The sector was limited by the lack of manpower. Many workers returned slowly to the Czech Republic after the reopening of the borders. This factor also extended to the primary sector, which was reflected in the reduction of working hours. The high stock of public as well as private contracts, together with the very favorable weather have been reflected in the continued growth of construction production.
However, in the following period the industry gradually began to face problems. The restrictive measures imposed by the government made the administrative process of preparing buildings more difficult, various tenders were interrupted, thereby disrupting the smoothness of construction. This has also exacerbated the already acute problem of labor availability in the construction sector. The performance of construction industry fell in the 2nd quarter of 2020.
Thus, the more than three-year period of growth was brought to an end.
Agricultural production sector
Gross value added grew for the third year in a row. This was obtained thanks to the stabilized situation in the primary agricultural production sector, which has not been adversely affected by regulatory influences except for the strengthening of hygiene measures. Meat production in the 1st part of the year was stagnated, domestic producers sold more milk in kind.
The growth of the entire primary sector was also supported by the continued natural logging.
Tertiary sphere
The trade, transport, accommodation, and hospitality segments also felt a strong macroeconomic impact. Gross value added fell by an eighth due to the fundamental restrictions of the international movement of persons. The influence of other branches of the tertiary sphere was lower. A smaller decrease was also noticeable in professional, scientific, and technical activities, as well as in finance or real estate activities. In contrast, in the provision of public services, gross value added has stagnated.
The role of information and communication activities in the pandemic period was gaining more importance. The least affected sectoral sections were information and communication activities. While some sectors of the sector benefited from the increased demand during the coronavirus crisis (telecommunications, ICT), others were affected to varying degrees (publishing, programming, broadcasting, and in particular, the film and music industry), where sales fell by more than a third.
The transport and storage sector were characterized by diverse development. The deep drop in air and land transport were contrasting with the growing demand for postal and courier activities. In the accommodation, catering and hospitality sectors, sales fell by almost two- fifths. In the accommodation itself, the decline was significant. The administrative and support sector recorded a drop of almost a quarter of sales. The professional, scientific, and technical sectors were also affected by a reduction of almost a tenth. The negative impact was mainly due to the lower performance of other professional and scientific activities, as well as architectural and engineering activities. Legal and accounting activities remained the least affected.
Production industry
It is not surprising that the deepest decline in the production occurred in the 1st half of the year 2020 in the production of motor vehicles. From the beginning of the 2nd quarter, capacity utilization in this sector was only 50%.
Output in the coal mining and the leather industry also fell by about a quarter, but this was a long-term trend. Much more significant was the decline in engineering output, reflecting a significant drop in the private investment across the economy.
The important metal industry, metallurgy, and the production of beverages have reduced their production by one seventh. In contrast, the food industry achieved only a slight decline,
as the restrictive measures had a fairly small impact on household spending on short-term consumer goods. Similarly, the relatively diversified sector of the rest of the manufacturing industry was supported by the growing demand for medical supplies. This was partly due to the higher production of the pharmaceutical industry.
The woodworking and paper industry were also slightly growing up that year, which is probably a consequence of the growing natural logging in recent years.
3.2 Economy of Russia
The prospects of a new economic crisis occupy a significant place in 2019-2020.
Economists and politicians have been discussing what could be the trigger of a new crisis. Thus, at the beginning of 2020, various factors appeared, such as: politicization of economic processes, trade wars and the Chinese coronavirus, as a factor affecting the global economy.
Now it can be stated that the coronavirus has overshadowed all possible triggers, which in comparison with it seems like a minor annoyance.
The rapid spread of the pandemic in 2020 has caused all further economic problems. The Russian economic transformation plan is a set of investment, institutional and structural measures that are formed around the national goals and priority national projects set by the President of Russia.
The key characteristic of the current socio-economic situation in Russia is the gap between extremely favorable monetary and financial (macroeconomic) parameters and low socio-economic dynamics.
On the one hand, there is a surplus in budget, unprecedented low (below the target of the Central Bank) inflation, close to the historical maximum level of gold and foreign exchange reserves, extremely low public debt, positive balance of payments and trade. Also, low unemployment and high credit activity of the population, including demand for mortgages.
On the other hand, economic growth rates are low (below the global average), living standards are stagnant, and investment activity is low.
This gap is most clearly reflected in the significant excess of savings over investment as a share of GDP. The Russian economy now has a lot of money, including on the accounts of individuals and firms, but these financial resources are not transformed into investments.
3.2.1 Macroeconomic indicators
GDP
In the beginning of 2020, there was a sharp fall of GDP due to the coronavirus pandemic.
But by September 2020 the decline in GDP slowed to -3.8%, compared to -8.0% in the 2nd quarter of 2020 (Ministry of Economic Development of the R.F, 2020). Numbers provided in
sections 3.2.1 and 3.2.2 are based on the report of the Ministry of Economic Development of the Russian Federation, September 2020.
Among the basic industries, the main contribution to the decline in the recession was made by the improvement in the dynamics of trade and manufacturing, as well as the growth of output in agriculture. In addition, the lifting of quarantine restrictions for Q3 2020 led an improvement in the dynamics in the service industries. However, under the terms of the OPEC + agreement5, aimed at supporting oil prices, as well as restrictions in the transport sector continued to make a significant negative contribution to GDP dynamics. The decline in GDP for the first 9 months of 2020 is estimated at -3.5 %.
The recovery in September was supported by a gradual improvement in the performance of the mining and transport complex. In the mining sector, the decline slowed slightly in September, partly due to improved natural gas production. Under these conditions, the dynamics of cargo turnover continues to improve, mainly due to pipeline, as well as sea and inland water transport.
Figure 7. Dynamics of Russia's GDP (in %) between 2015-2020.
Source: Analytical Center under the Government of the Russian Federation, 2020.
5 Opec+ refers to the alliance of crude producers, who have been undertaking corrections in supply in the oil markets since 2017.
Population and labor market
The total population of Russia is 145,9 million. The labor market improved in September, but the overall situation remains tense. The unemployment rate fell to 6.3% from 6.4% in August but remains significantly higher than levels of the 1st quarter of 2020. The total number of unemployed, excluding seasonality, decreased for the first time since March by 50.4 thousand people. The increase in the number of employees amounted to 74.7 thousand people, after a decrease in August. As a result, the number of employed people, without taking into account seasonality, increased by 24.3 thousand people compared to August.
Data also shows an increase in demand for labor: the dynamics of vacancies in September came out in a positive area, amounting to +6 % after zero dynamics in August and a decrease of -2 % in July.
In September, the rate of registration of the population in the employment service also slowed down. The average daily increase in the number of citizens registered with the employment service slowed to 2.2 thousand people on average per day in September. As of the end of September, the number of officially registered unemployed was 3.7 million people. The ratio of registered unemployment and unemployment in September exceeded 75% and at the same time, in the first half of October, there was a downward trend in registered unemployment (up to 3.6 million people in October).
Source: Trading economics, 2020.
Figure 8. Labour market in Russia (in %) between November 2019 – May 2020.
Statistics on wages for August indicate stabilization after the high indicators of July. In August, wage growth in nominal terms slowed to 3.7% from 6.4% in July. The growth of real wages in July–August, according to estimates, contributed to the improvement of income dynamics. Decline in real disposable income decreased to - 4.8% and the overall drop in January-September is estimated at - 4.3%.
Source: Trading economics, 2020.
Imports and exports
Russia's foreign trade in 2020 exceeded negative expectations during the first wave of COVID-19. Although the share of energy exports fell to a 20-year minimum, the supply of food and precious metals increased by 50%.
Russian exports of goods amounted to 338.2 billion $, returning to the level of 2015- 2017. Imports did not lag far behind by value of the previous two years. As a result, net exports exceeded 100 billion $ and stayed at the level of the year 2016. The positive balance of foreign trade supported GDP.
In April 2020, at the peak of the first wave of the pandemic, the Bank of Russia estimated that exports of goods in 2020 will collapse to 250 billion $, and imports to 207 billion $ due to falling oil prices. The final results of the year were better than predictions. This is primarily due to the recovery in energy prices.
Figure 9. Wages in Russia (in %) between October 2019 – July 2020.
Despite the recovery in oil and gas prices in the second half of 2020 to relatively comfortable levels for Russia, at the end of the year the share of mineral exports fell to 51%, the lowest since 1999.
For the first time in two decades, energy exports accounted for less than a half of total Russian exports (49.6%). Russian oil supplies abroad fell by 11% in 2020 in physical terms and by 41% in value, to 72.4 billion $.
Russian companies managed to partially compensate the drop in oil and gas exports by food, agricultural, raw materials, and precious metals. These categories showed export growth in monetary terms in 2020. Total exports of food and precious metals increased 1.5 times, to 60 billion $.
3.2.2 Sectors of the Russian economy
Russia is fully provided with various types of mineral raw materials and occupies a leading position among the largest countries in the world in terms of their proven reserves. With a population of 2.5% of the world's population and a mineral resource base estimated at 25%
of the world's resources, it consumes up to 4 % of the world's resources (NatWorld, 2017).
Russia is the largest producer of fuel and raw materials (11.6 % of world oil production, 30% of gas production, 12% of coal, 10.2% of iron ore, 10-15% of non - ferrous and rare metals, 26.3 % of diamonds) (NatWorld, 2017). Russia has half of the world's wood reserves, ¼ of phosphorites and apatites, 2/5 of potash salts, and 1/15 of hydroelectric resources (NatWorld, 2017). The main feature of all resources is their uneven distribution across the country. Forest resources have a multi-purpose purpose. On the one hand, they are raw materials for industry and other industries, on the other hand, they are fuel. The area covered by forests is 8.1 million km2 (NatWorld, 2017).
Fuel and energy complex
Fuel and energy products continue to dominate the structure of Russian exports, accounting for about 2/3 of its value. Negative external factors, including the decline in the world oil prices (despite the efforts of OPEC+ countries to stabilize the market), caused a
reduction in the value of Russia's exports of crude oil, petroleum products, and natural gas. The expansion of external supplies of liquefied natural gas, as a result of the commissioning of new lines of the Yamal LNG project, provided some support to Russian energy exports.
At the end of 2018, Russia had the 1st position in the world in terms of exports of fuel and energy products, in value terms with a share of 9.5%. In 2019, in terms of physical exports, Russia was ranked as 1st in terms of gas exports, 2nd in terms of oil exports (after Saudi Arabia), and 3rd in terms of coal exports (after Indonesia and Australia). Over the past ten years, there has been a steady growth in Russian product supplies to foreign markets in physical terms.
Export volumes increased from 663.6 million tons in 2010 to 865.4 million tons in 2019.
Average annual growth rate of fuel and energy exports for the ten-year period was 2.99%. At the end of 2019, the export of fuel and energy goods, in value terms, amounted to 262.5 billion dollars. For the period 2010-2019, coal showed an increase in exports among the main commodity groups. Exports of oil, petroleum products and natural gas, on the contrary, showed a decrease. The decline in energy exports in value terms in 2014-2016 was caused by falling prices on world markets with relatively stable volumes of physical supplies from Russia. The increase in export volumes was due to an increase in the cost of oil and gas, and the reduction in 2019 was due to its reduction. In December 2020, the energy sector grew by 4.7% compared to December 2019. In December 2020, oil production amounted to 39.1 million tons, a decrease of 12.6% compared to December 2019. In general, in 2020 were produced 478 million tons of oil, which is 9.3% less than a year earlier. Production of combustible gas in December 2020 amounted to 58.4 billion m3, a decrease of 1.0% compared to December 2019.
Agri-food sector
Food exports in 2019 declined slightly from the previous year's peak as a result of a smaller wheat harvest in 2018-2019. At the same time, this sector remains one of the most promising and rapidly growing export-oriented sectors. The main contribution to the positive dynamics of food exports is made by cereals (wheat, barley).
Agri-food sector is one of the key drivers of Russian export growth over the past ten years – average annual growth rate in 2010-2019 years has exceeded 10%. Russia's share in the world food exports increased from 0.7% in 2010 to 1.6% in 2018. Russia entered the ranking of the world's twenty largest exporters. In contrast to the leading countries in the export of food products, the structure of Russian supplies is dominated by goods with low added value
(primarily cereals and frozen fish), with a pronounced meat and dairy orientation of food exports of key exporting countries. At the end of 2019, exports of agri-food products reached 24.8 billion USD. Food production decreased by 0.2% in December 2020, compared to December 2019.
Chemical products
Exports of Russian chemical products in 2019 showed a negative trend, as a result of a decrease in the cost of deliveries of mixed fertilizers, ammonia, synthetic rubber, and tires. The continued focus of Russian exports on the supply of large-capacity chemical products increases the country's dependence on fluctuations in world prices. Chemical industry products account for 6.4% of Russian exports. In 2019, exports of chemical products increased by 2.7% in physical terms, with a decrease of 1.5% in monetary terms, mainly due to a drop in export prices for mixed fertilizers and ammonia. The largest share in the structure of chemical exports is occupied by fertilizers, which accounted for 31% of the value of chemical exports in 2019.
Non-organic products accounted for 19% of the value of exports, 14% for organic chemicals, and 11% each for plastics and rubber. In 2020, the production of medicines and materials used for medical purposes increased by 23.0% compared to the previous year. In December 2020, production increased by 82.0% compared to December 2019.
Metallurgical branch
Metallurgical products have the largest share in Russian commodity exports, after fuel and energy products. A significant positive increase in Russian exports was observed only in relation to nickel, world prices for which increased in 2019. In 2018, Russia entered the top ten in the world in terms of exports of metallurgical products in value terms. Compared to 2010, Russia moved up in one position, overtaking France, while the share of Russian metal exports in global exports decreased. This is due to the fact that China, over the same period, significantly increased its share in global exports of metallurgical products. Considering the rating of countries for the supply of certain metals abroad, the share of Russia in world exports of ferrous metals is 5.5% (5th place), copper - 3.3% (7th place), aluminum - 3.4% (6th place).
Russia ranks 3rd in the world in terms of nickel market volume, after Canada and the United States.
