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Theoretical and Applied Economics
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Gravity model of international trade in EAEU, SCO and BRICS countries / Модель международной торговли по типу взаимного притяжения стран ЕАЭС, ШОС и БРИКС

Озарнов Руслан Владиславович

аспирант, Департамент мировой экономики и мировых финансов, Финансовый университет при Правительстве РФ, г.Москва, РФ

125993, Россия, г. Москва, Ленинградский проспект, 49, Департамент мировой экономики и мировых финансов

Ozarnov Ruslan

Post-graduate student, the department of World Economics and Finances, Financial University under the Government of the Russian Federation

125993, Russia, g. Moscow, Leningradskii prospekt, 49, Departament mirovoi ekonomiki i mirovykh finansov

ozarnovr@gmail.com
Другие публикации этого автора
 

 

DOI:

10.25136/2409-8647.2020.4.33954

Дата направления статьи в редакцию:

17-09-2020


Дата публикации:

24-09-2020


Аннотация.

Статья посвящена исследованию факторов, влияющих на финансово-экономическое сотрудничество стран с развивающимся рынком в рамках трех блоков, а именно ЕАЭС, БРИКС и ШОС, посредством построения модели по типу взаимного притяжения. Отдельно рассматривается принадлежность стран к бывшему СССР. Установлен временной период исследования, который начинается в кризисном для России 2014 году и продолжается до настоящего времени. Предметом анализа являются финансово-экономические отношения, возникающие в процессе сотрудничества стран с развивающимся рынком, входящих в такие объединения как ЕАЭС, ШОС и БРИКС. Автор рассматривает факторы, оказывающие воздействие на финансово-экономическое сотрудничество стран с развивающимся рынком: глубина и масштабы внешнеторгового оборота, валовый внутренний продукт на душу населения, индекс торговой открытости, темп роста курса валюты страны-импортера и страны-экспортера, расстояние между странами, причастность страны с развивающимся рынком к таким объединениям как ЕАЭС, БРИКС, ШОС. Исследование основано на общенаучных методах познания (анализ, синтез, сравнение), представлении табличной и графической интерпретации статистической информации, временных рядов, эконометрическом моделировании с использованием программного продукта EViews. Новизна статьи заключается в определении, с применением эконометрических методов, факторов, влияющих на финансово-экономическое сотрудничество стран с развивающимся рынком в рамках трех блоков, а именно ЕАЭС, БРИКС и ШОС, посредством построения модели по типу взаимного притяжения. Особенность представленной модели заключается в наличии лаговых обменных курсов. Включение лага относительного изменения курса валюты экспортера привело к тому, что данные за 2014 год оказались исключенными из выборки. Целесообразно учитывать полученные результаты при сотрудничестве России как на двусторонней основе, так и в многостороннем формате в рамках ЕАЭС, БРИКС и ШОС.

Ключевые слова: международные финансы, международная торговля, мировая экономика, международные экономические отношения, гравитационная модель, российско-китайское сотрудничество, эконометрика, ЕАЭС, БРИКС, ШОС

Abstract.

This article is dedicated to examination of factors influencing the economic and financial cooperation of countries with developing market, namely EAEU, SCO and BRICS, by means of building a gravity model of trade. Special attention is given to affiliation of the countries to former USSR. The established timeframe starts with the crisis for Russia 2014 and continuous until the present. The subject of this research is the economic and financial relations emerged in the process of cooperation of EAEU, SCO and BRICS member-states with developing market. The author examines the factors affecting economic and financial cooperation of these countries, such as the size of external trade, gross domestic product per capita, trade openness index, currency appreciation rate of importing and exporting countries, distance between the countries, affiliation of the country with developing market to EAEU, SCO and BRICS. Using econometric methods, the author determines the factors impacting the cooperation of countries with developing markets within the three blocks – EAEU, SCO and BRICS based on the gravity model of international trade. This defines the scientific novelty of this work. The peculiarity of this model consists in availability of lag exchange rates. Inclusion of lag in reference to the currency rate of the exporter led to exclusion of 2014 data from sampling. The acquired results should be taken into account Russia’s cooperation on bilateral and multilateral basis within the framework of EAEU, SCO and BRICS.

Keywords:

Sino-Russian cooperation, gravity model, international economic relations, world economy, international trade, international finance, econometrics, EAEU, BRICS, SCO

INTRODUCTION

The research of financial and economic cooperation among emerging countries being the members of such blocks as the EAEU, SCO, and BRICS, as well as determining the factors influencing the given cooperation, with the aim of further strengthening and expanding financial and economic relations among the countries in question is paramount in terms of increasing imbalances in the world economy and world finance, along with the protectionist policy of the largest world economies, the growth of geopolitical and economic contradictions, the use of sanctions policy, it is relevant to.

The gravity model demonstrates the way the ties among countries influence the volume and quality of mutual trade. In the literature, this model is found quite frequently as the study of the character and the degree of influence allows to adjust international policy. The directions largely favouring the improvement of mutual trade relationships are distinguished. Thus, the efficiency of the international production system and world GDP growth.

The gravity model allows setting the volume of trade in the lack of restrictions. Comparison of trade volumes predicted by the model with actual trade volumes makes it possible to determine the trade changes which are likely to take place if the restrictions are removed. Gravity models can be used both for analyzing the bilateral trade and the trade of the country in question with all countries at large, which essentially represents the sum of bilateral trade flows. [3]

If it is politically necessary to exert a certain influence on trading partners, for example, a sanctions policy, this model allows one to calculate the distant financial and economic prospects of restrictive measures and find weak points. This makes it possible to increase the effectiveness of restrictive policies.

The connections being included in gravity models can be divided into three main groups:

1) location, geography, boundaries;

2) economic production, raw materials and human potential, economic development rates;

3) features of the management system, political system, block membership.

METHODS

The author has applied econometric data analysis using the EViews software as the main research method. The author uses statistical data of the World Bank Group [9], the International Trade Center [7], and the “FINAM” investment company. [8] Besides, the author makes use of general scientific methods of cognition such as analysis, synthesis, comparison, presentation of tabular and graphical interpretation of statistical information and time series.

MAIN PART

The main purpose of the given research is to build up the gravity model for trade partners of the Russian Federation within three blocks, namely the EAEU, BRICS and SCO. The countries' belonging to the former USSR is considered separately. It should be noted that a strategic partnership has been established between Russia and China, and China's economic strategy presupposes a global foreign economic offensive with elements of trade expansion to foreign markets. [4] The research data starts with the crisis in Russia of 2014 and proceeds up to now. Table 1 provides a list of countries belonging to the EAEU, SCO and BRICS, as well as the ISO two-letter international classification codes for these countries. The countries of associations that were part of the former USSR are enumerated separately.

Table 1. List of countries united in blocks EAEU, SCO and BRICS and their belonging to the former USSR.

Countries

Codes ISO-2

Countries

CodesISO-2

EAEU

BRICS

Armenia

AM

Brazil

BR

Belarus

BY

Russia

RU

Kazakhstan

KZ

India

IN

Russia

RU

China

CN

SCO

South Africa

ZA

India

IN

USSR

Kazakhstan

KZ

Armenia

AM

Kyrgyzstan

KG

Belarus

BY

China

CN

Kazakhstan

KZ

Pakistan

PK

Kyrgyzstan

KG

Russia

RU

Russia

RU

Tajikistan

TJ

Tajikistan

TJ

Uzbekistan

UZ

Uzbekistan

UZ

Table 2 presents a list of the countries participating in the sample, as well as their main economic indicators in 2018. Despite the eightfold excess of China's GDP over that of the Russian Federation, the trade surplus of the Russian Federation is more by 60 billion dollars than that of China.

Table 2. List of the countries participating in the sample, as well as their main economic indicators in 2018.

Source: Authors' calculations based on the World Bank data [9]

i

Страны

ISO2

GDP, billion USD

GDP per capita,USD

Export, billion USD

Import,billion USD

Balance, billion USD

1

Russia

RU

1657,6

11729,1

509,6

344,3

165,3

2

Brazil

BR

1868,6

11026,2

276,7

266,8

9,9

3

China

CN

13608,2

7755,0

2655,6

2549,0

106,6

4

India

IN

2726,3

2104,2

537,0

638,8

-101,8

5

South Africa

ZA

368,3

7439,9

110,1

108,9

1,2

6

Armenia

AM

12,4

4406,7

4,7

6,6

-1,9

7

Belarus

BY

59,7

6744,5

41,9

41,3

0,6

8

Kazakhstan

KZ

170,5

11165,5

56,0*

42,8*

13,2

9

Kyrgyzstan

KG

8,1

1087,2

2,6

5,5

-2,9

10

Pakistan

PK

312,6

1196,6

26,7

60,8

-34,1

11

Tajikistan

TJ

7,5

1073,0

1,1*

2,9*

-1,8

12

Uzbekistan

UZ

50,5

2026,5

14,7

19,6

-4,9

* Export and import data of 2018 For Kazakhstan and Tajikistan are not available, so the data of 2017 are provided.

Export-import relationship of the Russian Federation

within the listed blocks

Table 3 provides the data on the trade openness of the economies of the countries listed in Table 2 for 2014 - 2018. The trade openness index is calculated using the formula:

, (1)

where i is the index of the country, the correspondence of the indices and countries is indicated in Table 2;

EXPi - gross export of the country;

IMPi is the country's gross import;

GDPi is the country's gross domestic product.

The high economic openness of Belarus and Kyrgyzstan, where the total value of exports and imports exceeds GDP should be noted. To the greatest extent, the trade openness index decreased among the groups of the SCO and BRICS countries. These groups comprise China, whose trade activity index has decreased by 15% over 5 years. Since China's exports and imports have not declined over the period, this drop in trade activity is explained by increased domestic consumption. In the Russian Federation, the index of trading activity increased by 7.8%. In fact, the trade among the former USSR countries had remained unchanged over the given period.

Table 3. Countries trade openness index of trade partners of the Russian Federation and amalgamation with the participation of the Russian Federation.

Source: Authors' calculations based on the World Bank data [9]

i

Countries

Trade openness index, %

2014

2015

2016

2017

2018

Growth rate, %

1

Russia

47,78

49,35

46,30

46,76

51,51

7,81

2

Brazil

24,69

26,95

24,54

24,14

29,08

17,78

3

China

45,07

39,63

37,21

38,15

38,25

-15,13

4

India

48,92

41,92

40,16

40,77

43,13

-11,84

5

South Africa

64,43

61,62

60,64

57,97

59,47

-7,70

6

Armenia

75,61

71,59

75,92

86,83

90,40

19,56

7

Byelorussia

110,65

115,91

125,21

133,37

139,34

25,93

8

Kazakhstan

64,97

53,05

60,31

60,62

0,51*

9

Kyrgyzstan

125,13

110,96

105,82

100,62

101,12

-19,19

10

Pakistan

30,90

27,65

25,31

25,79

27,97

-9,48

11

Tajikistan

54,61

52,73

54,97

56,64

3,04*

12

Uzbekistan

35,95

30,44

29,75

45,68

67,85

88,73

EAEU

51,61

52,25

50,36

50,89

49,90

-3,31

SCO

46,02

40,77

38,41

39,42

39,72

-13,69

BRICS

43,35

39,76

37,37

38,08

39,53

-8,81

USSR

51,36

51,43

49,55

50,95

50,38

-1,91

* Export and import data of 2018 For Kazakhstan and Tajikistan are not available, so the data for 2014 - 2017 are provided.

In 2014, the Russian Federation underwent the shock of sanctions. As a result, the process of reorientation of the Russian economy towards eastern markets started. Table 4 illustrates this process as well as demonstrates the export volume of Russian GDP with the listed trade partners and amalgamations in Table 2. The growth leaders are Pakistan, Armenia, and India.

Table 4. Export volume of Russian GDP with the given trade partners and amalgamations.

Source: Authors' calculations based on the World Bank data [9]

Russian trade partners

Export as a percent of Russian GDP, %

2014

2015

2016

2017

2018

Growth rate

Brazil

0,111

0,141

0,139

0,129

0,156

40,28

China

1,816

2,078

2,185

2,377

3,381

86,15

India

0,213

0,334

0,414

0,344

0,468

119,18

South Africa

0,014

0,020

0,015

0,013

0,017

24,29

Armenia

0,026

0,037

0,075

0,055

0,081

211,69

Byelorussia

0,803

0,911

1,095

0,984

1,316

63,95

Kazakhstan

0,673

0,755

0,735

0,755

0,780

15,86

Kyrgyzstan

0,084

0,095

0,080

0,088

0,099

16,97

Pakistan

0,007

0,007

0,010

0,016

0,025

263,80

Tajikistan

0,043

0,056

0,052

0,044

0,051

18,51

Uzbekistan

0,151

0,163

0,153

0,166

0,200

32,55

EAEU

1,502

1,704

1,905

1,795

2,177

44,96

SCO

2,988

3,487

3,629

3,790

5,004

67,45

BRICS

2,155

2,573

2,753

2,862

4,022

86,65

USSR

1,781

2,017

2,190

2,092

2,527

41,94

Fig. 1 shows the way the share of exports with amalgamations of countries in the Russian Federation grows. Russia has the largest volume of the share of exports with the SCO countries. as well as the highest growth rates are detected with the BRICS countries.

Figure 1. Export as a percent of Russian GDP in trade with given amalgamations, %

The gravity model of the given countries in the post-crisis period

for the Russian Federation

Walter Isard applied the model for the first time in 1954. It may be called a theoretical model at the primary level for the purposes of trade between a couple of countries. [2] Then the model was used by Tinbergen in 1962 [10], and Poyhonen in 1963 [5] based on the notion that bilateral trade flows between two countries directly depends on national incomes and indirectly links with bilateral distance [6]. This model of the relationship between bilateral trade, a country's economic position, and distance can help analyze bilateral or multilateral economic integration between two or more countries.

There are various options for gravity models, in which the variables are used indicators such as the population, the area of countries, the length of the border, as well as dummy variables responsible for social and political, climatic and other differences. Thus, gravity models determine the dependence of a unidirectional foreign trade flow on the parameters of the internal economic state as a country exporter and importing country. [1]

The influence of these factors is estimated on the basis of the data on the actual size of trade among countries using regression analysis. The obtained parameters of the model are elastic and demonstrate to which percentage the trade among countries can increase if the corresponding factor rises by 1%. Usually, this model is represented either in a power-law or in a linear-logarithmic form. [11]

To conduct the research, bilateral models of trade integration between Russia and China both in multilateral and bilateral formats, for the period from 2014 to 2018 should be analyzed by using panel data that make up the basis of the gravity model. In doing this, it should be noted that the contribution of the BRICS countries to world economic growth over the past decade has exceeded almost 50%. According to many forecasts, the economic performance of these countries and the growth rates will be higher than in developed countries and other emerging economies by 2030 - 2050.

The model performed in the given research is as follows:

, (2)

where EXPORTijt – exports from country i to country j for year t;

GDPit and GDPjt – GDP of the exporting country i and the importing country j for year t;

GDPPCit and GDPPCjt – GDP per capita of the exporting country i and importing country j for year t;

TOit and TOjt – trade openness index of the exporting country i and the importing country j for year t;

DEXRit and DEXRjt – the growth rate of the exchange rate of the importing country i and the exporting country j;

CAPDISTij – distance between the capitals of the importing country j and the exporting country i;

EAESi, SHOSi, BRICSi, USSRi – variables that take on the value 1 if the exporting country i is included in the corresponding amalgamation, and 0 if the country is not included in this amalgamation;

– random effects among countries;

uijt – random errors of the model.

The second column of Table 5 gives the estimates of the parameters that fully correspond to equation (2). A detailed description of the characteristics of the performed model is in Appendix 1. The coefficient β_1 in equation (2) indicates the elasticity of exports to changes in GDP of both the exporter and the importer. The value of β_1 close to 1 is obtained.

The coefficients β_2 and β_3 show the way the per capita income of the exporting and importing countries influences on the value of exports. The obtained values indicate that with an increase in per capita income of an exporter by a thousand dollars, exports increase by 16.5%. If the per capita income of the importer increases by a thousand dollars, then exports to this country increase by 10.5%.

The trade openness index also positively correlates with the exports value. One additional percentage point of exporter's trade openness provides an average of 1.17% growth in exports. One additional percentage point of importer's trade openness increases exports to the country by 1.87% on average.

The coefficients β_6 and β_7 in equation (2) demonstrate the way the relative change in the exchange rate of the national currency against the dollar affects the export of a given country. One percent gain in the currency of the importing country increases exports to that country by 0.41%. In fact, this means that the weakening of the dollar of a trading partner increases exports to this country. It is likely to be related to the increase of effective demand. As for the exporter, the strengthening of the national currency by 1% causes the growth of exports by 0.51% with a lag of one period.

Increasing the distance among countries predictably reduces the amount of exports among them. The name of the model, i.e. the gravity model is valid. The value of the coefficient β_8 = - 2.27 indicates that an increase in the distance to the capital of a trading partner's country by 1% results in a decrease in exports to this country by 2.27%.

Coefficients β_ (9-12) present the way the belonging to definite amalgamation influences the export value. Membership in the EAEU did not show a considerable impact on the export of the member country of this amalgamation. Belonging to BRICS demonstrates a noticeable increase in exports equal to 167.2%. This is explained by the fact that the countries with significant GDP and with large global export opportunities belong to the BRICS. Belonging to the former USSR shows an increase of exports by 85.3%.

Table 5. Equation (2) parameters estimates.

Dependent Variable: LOG(EXPORT_IJT)

lag DEXR_IT

lag DEXR_IT without Russia

lag DEXR_IT without China

sample from 2014

k

LOG(GDP_IT*GDP_JT)

1.048378***

1.083114***

1.051590***

0.904208***

1

GDPPC_IT

0.000165^

0.000153*

0.000188**

0.000173**

2

GDPPC_JT

0.000105***

0.000106*

0.000130**

0.0000952*

3

TO_IT

0.011684***

0.010934**

0.010548**

0.013988***

4

TO_JT

0.018690***

0.019598***

0.019924***

0.013197***

5

DEXR_IT

-0.001116

DEXR_IT(-1)

0.005050***

0.006290**

0.005477**

6

DEXR_JT

0.004144***

0.004293*

0.003930*

0.004509**

7

LOG(CAPDIST_IJ)

-2.272103***

-2.317837***

-2.324079***

-2.096904***

8

EAES_I

-0.094825

-0.103772

-0.294821

0.184009

9

SHOS_I

-0.681953*

-0.803031

-0.981422*

-0.203052

10

BRICS_I

1.671805**

1.676277*

1.415229**

1.887951***

11

USSR_I

0.853088**

0.957536

1.126112*

0.365977

12

C

-29.27515***

-30.65195***

-29.19053***

-23.17843***

0

R2

0.386655

0.360223

0.375565

0.319733

DW

1.805328

1.816908

1.819800

1.670782

Obs

486

444

444

618

Cross

132

121

121

132

Periods

4

4

4

5

*** significance level less than 1%; ** significance level is less than 5%; * the level of significance is less than 10%; ^ one-sided significance level less than 10%.

In order to demonstrate the stability of the obtained parameter estimates to data changes, the third, fourth and fifth columns of Table 5 were calculated. Russia is a member of all amalgamations the trade relationships of which are studied in the given research. In the case of instability of the performed model to a change in the initial data, the exclusion of Russia in estimating the parameters of equation (2) should cause a change in the estimates of the parameters.

In the third column of Table. 5, the parameters of equation (2) in which the Russian Federation is excluded from the list of countries are calculated. Parameter estimates that assess the impact of the SCO and the USSR, where the Russian Federation is a key player, become insignificant. Estimates of the remaining parameters are changed insignificantly.

The fourth column gives estimates of the parameters of equation (2) in which China is excluded from the list of countries. China is the largest economy in the world, in terms of GDP, it noticeably exceeds the rest of the participants of the research in question. All parameter estimates changed insignificantly compared to estimates obtained for the entire sample.

The inclusion of a lag in the relative change in the exporter's exchange rate resulted in the data of 2014 being excluded from the sample. The fifth column of Table. 5 provides the estimates of the model parameters comprising the present (at time t) value of the relative change in the exchange rate of the exporter's currency. The estimates of the parameters that do not concern the amalgamations have remained practically unchanged. It is the BRICS that has a significant influence among the amalgamations.

CONCLUSION

The author comes to the following conclusions on the basis of the conducted research. In the case of an increase in per capita income of an exporter by a thousand dollars, exports increase by 16.5%. If the per capita income of the importer increases by a thousand dollars, then exports to this country increase by 10.5%. Since at present per capita income in China is increasing, the given fact should be taken into account in order to expand foreign trade cooperation.

The trade openness index presented in the model is positively correlated with the export value. One additional percentage point of exporter's trade openness provides an average of 1.17% growth in exports. One additional percentage point of importer's trade openness increases exports to the country by 1.87% on average. This result confirms the conclusion about the reorientation of the Russian Federation to Asian markets, in particular, to the Chinese market and an increase in Russian exports to China.

The relative change in the exchange rate of the national currency against the dollar influences the export of a given country. One percent gain in the currency of the importing country increases exports to the country by 0.41%. In fact, it means that the weakening of the dollar of a trading partner increases exports to this country. It is likely to be related to an increase of effective demand. As for the exporter, the strengthening of the national currency by 1% causes the growth of exports by 0.51% with a lag of one period.

Increasing the distance among countries predictably reduces the amount of exports among them. Thus, the mutual attraction is weakened. According to the results of the given model, an increase in the distance to the capital of a trading partner country by 1% results in a decrease of exports to the country by 2.27%.

Membership in the EAEU did not show a considerable impact on the export of the member country of this amalgamation. Belonging to BRICS demonstrates a noticeable increase in exports equal to 167.2%. This is explained by the fact that the countries with significant GDP and with large global export opportunities belong to the BRICS.

To create a sustainable strategic financial and economic cooperation, a strategy of soft balancing and the establishment of beneficial bilateral economic ties are desirable. At the same time, soft balancing implies recognition of the geo-economics leadership of a less dependent state while preventing its hegemony. The given conditions can be implemented through diversification of the partnership, which will promote the benefits of this partnership to its members due to economies of scale; developing institutions for collective bargaining advantages and the creation of multilateral organizations that also include other advanced economies to maintain the internal balance.

Thus, one should take into account the obtained results of the gravity model parameters in Russia's cooperation both on a bilateral basis and in a multilateral format within the EAEU, BRICS, and SCO.

ACKNOWLEDGMENTS

The article was prepared under the scientific supervision of Viktor Y. Pishchik, Doctor of Economics, Professor, Scientific Advisor of the Department of World Finance of Financial University under the Government of the Russian Federation.

Appendix 1.

Detailed description of the performed model characteristics

Dependent Variable: LOG(EXPORT_IJT)

Method: Panel EGLS (Cross-section random effects)

Sample (adjusted): 1915 1918

Periods included: 4

Cross-sections included: 132

Total panel (unbalanced) observations: 486

Swamy and Arora estimator of component variances

White cross-section standard errors & covariance (d.f. corrected)

Variable

Coefficient

Std. Error

t-Statistic

Prob.

LOG(GDP_IT*GDP_JT)

1.048378

0.084805

12.36225

0.0000

GDPPC_IT

0.000165

0.000113

1.456157

0.1460

GDPPC_JT

0.000105

2.59E-05

4.045995

0.0001

TO_IT

0.011684

0.004512

2.589344

0.0099

TO_JT

0.018690

0.002785

6.711053

0.0000

DEXR_IT(-1)

0.005050

0.001182

4.272918

0.0000

DEXR_JT

0.004144

0.001115

3.717389

0.0002

LOG(CAPDIST_IJ)

-2.272103

0.269394

-8.434137

0.0000

EAES_I

-0.094825

0.717991

-0.132071

0.8950

SHOS_I

-0.681953

0.407836

-1.672125

0.0952

BRICS_I

1.671805

0.709031

2.357873

0.0188

USSR_I

0.853088

0.377309

2.260981

0.0242

C

-29.27515

3.424975

-8.547552

0.0000

Effects Specification

S.D.

Rho

Cross-section random

2.162925

0.8961

Idiosyncratic random

0.736510

0.1039

Weighted Statistics

R-squared

0.386655

Mean dependent var

1.891011

Adjusted R-squared

0.371094

S.D. dependent var

0.927611

S.E. of regression

0.738560

Sum squared resid

258.0076

F-statistic

24.84841

Durbin-Watson stat

1.805328

Prob(F-statistic)

0.000000

Unweighted Statistics

R-squared

0.681403

Mean dependent var

10.87668

Sum squared resid

2386.813

Durbin-Watson stat

0.195151

Dependent Variable: LOG(EXPORT_IJT)

Method: Panel EGLS (Cross-section random effects)

Sample: 1914 1918 IF I<>"RU"

Periods included: 4

Cross-sections included: 121

Total panel (unbalanced) observations: 444

Swamy and Arora estimator of component variances

Variable

Coefficient

Std. Error

t-Statistic

Prob.

LOG(GDP_IT*GDP_JT)

1.083114

0.110056

9.841514

0.0000

GDPPC_IT

0.000153

8.37E-05

1.822996

0.0690

GDPPC_JT

0.000106

6.28E-05

1.684406

0.0928

TO_IT

0.010934

0.005526

1.978537

0.0485

TO_JT

0.019598

0.005431

3.608552

0.0003

DEXR_IT(-1)

0.006290

0.002960

2.125080

0.0341

DEXR_JT

0.004293

0.002266

1.894671

0.0588

LOG(CAPDIST_IJ)

-2.317837

0.278519

-8.322012

0.0000

EAES_I

-0.103772

0.807731

-0.128474

0.8978

SHOS_I

-0.803031

0.634169

-1.266273

0.2061

BRICS_I

1.676277

0.935376

1.792088

0.0738

USSR_I

0.957536

0.866671

1.104843

0.2698

C

-30.65195

5.609907

-5.463897

0.0000

Effects Specification

S.D.

Rho

Cross-section random

2.227902

0.8938

Idiosyncratic random

0.768131

0.1062

Weighted Statistics

R-squared

0.360223

Mean dependent var

1.855289

Adjusted R-squared

0.342411

S.D. dependent var

0.947696

S.E. of regression

0.770491

Sum squared resid

255.8659

F-statistic

20.22272

Durbin-Watson stat

1.816908

Prob(F-statistic)

0.000000

Unweighted Statistics

R-squared

0.658152

Mean dependent var

10.52446

Sum squared resid

2304.674

Durbin-Watson stat

0.201714

Dependent Variable: LOG(EXPORT_IJT)

Method: Panel EGLS (Cross-section random effects)

Date: 01/08/20 Time: 12:58

Periods included: 4

Cross-sections included: 121

Total panel (unbalanced) observations: 444

Swamy and Arora estimator of component variances

Variable

Coefficient

Std. Error

t-Statistic

Prob.

LOG(GDP_IT*GDP_JT)

1.051590

0.106248

9.897533

0.0000

GDPPC_IT

0.000188

8.30E-05

2.268286

0.0238

GDPPC_JT

0.000130

6.06E-05

2.141705

0.0328

TO_IT

0.010548

0.005324

1.981017

0.0482

TO_JT

0.019924

0.005349

3.725101

0.0002

DEXR_IT(-1)

0.005477

0.002513

2.179073

0.0299

DEXR_JT

0.003930

0.002276

1.727061

0.0849

LOG(CAPDIST_IJ)

-2.324079

0.264123

-8.799222

0.0000

EAES_I

-0.294821

0.718499

-0.410329

0.6818

SHOS_I

-0.981422

0.579839

-1.692578

0.0913

BRICS_I

1.415229

0.683943

2.069220

0.0391

USSR_I

1.126112

0.757419

1.486775

0.1378

C

-29.19053

5.405518

-5.400136

0.0000

Effects Specification

S.D.

Rho

Cross-section random

2.136618

0.8865

Idiosyncratic random

0.764544

0.1135

Weighted Statistics

R-squared

0.375565

Mean dependent var

1.918049

Adjusted R-squared

0.358179

S.D. dependent var

0.954998

S.E. of regression

0.767065

Sum squared resid

253.5958

F-statistic

21.60199

Durbin-Watson stat

1.819800

Prob(F-statistic)

0.000000

Unweighted Statistics

R-squared

0.668344

Mean dependent var

10.49597

Sum squared resid

2134.737

Durbin-Watson stat

0.216183

Dependent Variable: LOG(EXPORT_IJT)

Method: Panel EGLS (Cross-section random effects)

Sample: 1914 1918

Periods included: 5

Cross-sections included: 132

Total panel (unbalanced) observations: 618

Swamy and Arora estimator of component variances

Variable

Coefficient

Std. Error

t-Statistic

Prob.

LOG(GDP_IT*GDP_JT)

0.904208

0.092099

9.817766

0.0000

GDPPC_IT

0.000173

7.31E-05

2.370733

0.0181

GDPPC_JT

9.52E-05

5.46E-05

1.744362

0.0816

TO_IT

0.013988

0.004377

3.195693

0.0015

TO_JT

0.013197

0.004268

3.092174

0.0021

DEXR_IT

-0.001116

0.002076

-0.537604

0.5910

DEXR_JT

0.004509

0.002065

2.183437

0.0294

LOG(CAPDIST_IJ)

-2.096904

0.257550

-8.141725

0.0000

EAES_I

0.184009

0.689100

0.267028

0.7895

SHOS_I

-0.203052

0.539728

-0.376212

0.7069

BRICS_I

1.887951

0.658241

2.868175

0.0043

USSR_I

0.365977

0.730228

0.501182

0.6164

C

-23.17843

4.728267

-4.902097

0.0000

Effects Specification

S.D.

Rho

Cross-section random

2.166811

0.8932

Idiosyncratic random

0.749097

0.1068

Weighted Statistics

R-squared

0.319733

Mean dependent var

1.711373

Adjusted R-squared

0.306240

S.D. dependent var

0.907856

S.E. of regression

0.758878

Sum squared resid

348.4174

F-statistic

23.69638

Durbin-Watson stat

1.670782

Prob(F-statistic)

0.000000

Unweighted Statistics

R-squared

0.668896

Mean dependent var

10.88485

Sum squared resid

3173.453

Durbin-Watson stat

0.183437

Библиография
1.
Abakumova Y. G., Pavlovskaya S.V. Matrix modeling of bilateral trade relations of countries // Vectors of foreign economic activity / Minsk: Institute of Economics of the National Academy of Sciences of Belarus. – 2010. – p. 378
2.
Isard, W Location Theory and Trade Theory: Short-Run Analysis. // Quarterly Journal of Economics. ¬ No. 68 (2). – 1954 – p.305
3.
Kireyev A. International microeconomics / Moscow. – 2015 – p. 356
4.
Ozarnov R. The peculiarities of Russia’s foreign trade with BRICS countries // Theoretical and Applied Economics. – 2018. – № 3. – P. 181-192. DOI: 10.25136/2409-8647.2018.3.27092 URL: https://en.nbpublish.com/library_read_article.php?id=27092
5.
Poyhonen, P. A Tentative Model for the Volume of Trade Between Countries. // Weltwirtschaftliches Archiv. – 1963 – p.93
6.
Rasoulinezhad, E. Investigation of Sanctions and Oil Price Effects on the Iran-Russia Trade by Using the Gravity Model. // Vestnik of St Petersburg University. Series 5. – 2016 p.68
7.
The International Trade Center URL: https://www.trademap.org/
8.
The “FINAM” investment company URL: https://www.finam.ru/
9.
The World Bank Group URL: https://data.worldbank.org
10.
Tinbergen, T. Shaping the World Economy: Suggestions for an International Economic Policy. / New York: The Twentieth Century Fund, 1962. – 215 р.
11.
Uskova T.V., Asanovich V. Ya., Dedkov S.M., Selimenkov R. Yu. Foreign economic activity of the regions of the NWFD and the Republic of Belarus: state and methodological aspects of modeling. Economic and social changes: facts, trends, forecast. – 2010. No. 4 (12). – p. 124
References (transliterated)
1.
Abakumova Y. G., Pavlovskaya S.V. Matrix modeling of bilateral trade relations of countries // Vectors of foreign economic activity / Minsk: Institute of Economics of the National Academy of Sciences of Belarus. – 2010. – p. 378
2.
Isard, W Location Theory and Trade Theory: Short-Run Analysis. // Quarterly Journal of Economics. ¬ No. 68 (2). – 1954 – p.305
3.
Kireyev A. International microeconomics / Moscow. – 2015 – p. 356
4.
Ozarnov R. The peculiarities of Russia’s foreign trade with BRICS countries // Theoretical and Applied Economics. – 2018. – № 3. – P. 181-192. DOI: 10.25136/2409-8647.2018.3.27092 URL: https://en.nbpublish.com/library_read_article.php?id=27092
5.
Poyhonen, P. A Tentative Model for the Volume of Trade Between Countries. // Weltwirtschaftliches Archiv. – 1963 – p.93
6.
Rasoulinezhad, E. Investigation of Sanctions and Oil Price Effects on the Iran-Russia Trade by Using the Gravity Model. // Vestnik of St Petersburg University. Series 5. – 2016 p.68
7.
The International Trade Center URL: https://www.trademap.org/
8.
The “FINAM” investment company URL: https://www.finam.ru/
9.
The World Bank Group URL: https://data.worldbank.org
10.
Tinbergen, T. Shaping the World Economy: Suggestions for an International Economic Policy. / New York: The Twentieth Century Fund, 1962. – 215 r.
11.
Uskova T.V., Asanovich V. Ya., Dedkov S.M., Selimenkov R. Yu. Foreign economic activity of the regions of the NWFD and the Republic of Belarus: state and methodological aspects of modeling. Economic and social changes: facts, trends, forecast. – 2010. No. 4 (12). – p. 124

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Предметом исследования статьи является изучение финансовой и экономической кооперации между странами, входящими в такие организации экономического сотрудничества, как ЕАЭС, ШОС и БРИКС, в условиях, когда мировая финансовая и экономическая система переживает глубокий кризис, когда вопросы открытости международной торговли ставятся под вопрос, а многие страны вводят протекционистские режимы для защиты внутреннего производства. В фокусе исследования находится внешняя торговля Российской Федерации, которая в результате введенных санкций в значительной степени переориентировала свой экспорт и импорт на восточные страны, в частности Китай, Индию, Казахстан и др. В качестве модели исследования автором выбрана классическая модель «взаимного тяготения», разработанная У.Изгардом, которая отличается достаточной простотой в использовании и позволяет проводить достаточно глубокий анализ влияния макроэкономических показателей страны на ее внешнеэкономическую деятельность. Кроме этого, автор анализирует динамику ключевых макроэкономических показателей стран, входящих в ЕАЭС, ШОС и БРИКС, такие как ВВП, ВВП на душу населения, долю экспорта в ВВП, индекс открытости экономики и ряд других показателей. В качестве статистической выборки используются показатели за период 2014-2018 годы. Предложенный подход представляется весьма актуальным, особенно в свете последних событий, связанных с пандемией, когда мировая торговля оказалась под ударом от значительного сокращения мировых товарных, финансовых и туристических потоков. Хотя данные события не вошли в корпус анализа статьи, используемая модель может быть масштабирована под учет влияния подобных пандемий на внешнюю торговлю внутри группы стран. Кроме того, сравнение предсказанных моделью показателей внешней торговли с реальными данными позволяет выявить те направления, которые могут стимулировать наращивание экспорта и импорта анализируемой национальной экономики. Важно отметить, что это особенно актуально для условий Российской Федерации, поскольку модель позволяет выявить наиболее узкие места во внешней торговле, оказавшиеся под наибольшим ударом от введенных санкций, и выработать меры по их преодолению. Расчеты авторы выявляет целый ряд важных закономерностей, взаимосвязей между макроэкономическими параметрами, показателями обменных курсов, удаленности стран друг от друга и величиной экспорта и импорта. В частности, выявлено позитивное влияние роста доходов на душу населения на величину экспорта; показано, что уровень открытости экономики позитивно коррелирует с ростом экспорта и импорта; отмечено, что ослабление курса доллара приводит к росту экспорта в данную страну. Интересно отметить, что уровень внешней торговли существенно стимулируется членством страны в БРИКС, а кроме этого – принадлежностью торговых партнеров к странам бывшего СССР. Статья хорошо структурирована, написана лаконично, посылки и выводы четко сформулированы, модель описана достаточно полно. Прилагаемые к статье расчетные данные позволяют верифицировать сделанные автором выводы. Библиографический список содержит 11 источников, которые дают достаточное представление об источниках данных и использованной в работе модели. В целом статья представляет существенный интерес, учитывая сложившуюся ситуацию в мировой торговле, а также последствия пандемии. Автор аргументированно доказывает важность расширения внешней торговли как источника улучшения экономической и социальной ситуации внутри страны. Выявленные закономерности могут быть использованы в работе государственных органов, занимающихся вопросами стимулирования экспорта.
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