Abstract
This article aims to identify the main determinants of annual export flow among BRICS countries through the estimation of panel data from 1992 to 2018. The estimated results suggest that gross domestic product (GDP) and trade openness among other factors can explain export flow among BRICS countries. The most important finding of the study is that the formation of BRICS has exercised a negative and significant impact on bilateral trade among member countries. This study also found that the intra-industry trade dominates the intra-BRICS trade. Finally, the study found that the geographical distance between countries might be a factor for impeding trade among member countries. Thus, this study highlights the importance of increasing economic cooperation among these countries in terms of developing infrastructure, signing of free trade agreement (FTA) and increasing people-to-people contacts.
Introduction
In recent years, emerging market economies have created a strong counterweight to developed economies (United States, EU, Japan) both in economic and political areas. This has been possible due to the reallocation of global consumption and economic activities to emerging markets. Among the emerging economies, many countries, including China, India, Brazil, Russia, South Africa and so on, are playing an important role in the economic affairs of their respective regions. Recognising their potential, O’Neill (2001) coined the term BRIC which refers to China, India, Brazil and Russia to predict global economic trends in the next half-century. The author concluded that BRIC countries are expected to play an important role in the global economy in the future. In Sachs’s papers, it was predicted that in the next 50 years, only United States and Japan would be among the six large economies of the world and BRIC countries would play an important role in world economic affairs (Government of India, 2012). Wilson and Purushothaman (2003) have used the latest demographic and capital accumulation and productivity growth to predict that over the next 50 years, Brazil, China, Russia and India could become a larger economic force in the world economy. The study concludes that a high real growth rate and currency appreciation could contribute about two-thirds of the increase in the gross domestic product (GDP) of BRIC countries. The key assumption for this projection to be true is that these countries develop institutions and formulate such policies that are necessary for growth. Similarly, Nayyar (2010) were of the view that Brazil, China, India, and South Africa have considerable potential for carrying forward a collective voice in the world forums. They can exercise considerable influence on multilateral institutions if they can cooperate with each other, which till date is missing.
In 2010, South Africa joined the group to make it BRICS, which has the potential to increase global trade rapidly. These countries constitute about 41% of the world’s population, 27% of the land area and generate about 25% of the world GDP (Kubendran, 2020). Regarding the role of BRICS countries, de Castro (2013) showed that the share of BRICS countries in world exports has recorded an increasing trend in recent years which reflects their importance in world exports. However, the trade indicators show low average intensity of trade in the intra-BRICS trade. However, trade of these countries with the rest of the world, indicates lack of cooperation on the economic front that needs to be addressed.
In the past few years, BRICS countries have emerged as production hub of goods and services, potential consumer markets, hub of capital flow from other countries. These countries are playing an important role in world economic affairs. After the financial crises, these countries played a formidable role in formulating macroeconomic policy even in the G-20 forum. Highlighting the importance of BRICS countries as rising economic powers, Radulescu et al. (2014) stated that the financial crisis has not affected the BRICS countries intensively and they have recovered quickly than developed countries due to enormous resources, population and increased supply of factor inputs. The BRICS countries have huge land and natural resources with Russia second biggest country in the world in terms of land followed by China (third), South Africa (fifth) and India (seventh). Russia had huge reserves of oil and gas, around 20% of the world’s reserves, and China has 12% of the world mineral resources. While Brazil is rich in agriculture and natural resources like coffee, sugar cane, soybean, iron ore and crude oil, India has a rising manufacturing base and is a strong service provider (The BRICS Report). The economic indicators of the last decade show the prediction to be proving true with BRICS (including South Africa) share of world GDP and trade increasing from a mere 10% and 4% in 1990 to 25% and 15% in 2010 and 18.3% in 2016. In the sixth summit held in Fortaleza, Brazil, in July 2014, the BRICS leaders highlighted the importance of international trade and called for an action plan to advance its work on trade and investment. These countries constitute 20% of global trade and generate more than 40% of global economic growth. The leaders also decided to form the New Development Bank (NDB) to finance infrastructure and sustainable development projects with initial capital of $100 billion and a contingency reserves arrangement worth $100 billion (Singh et al. 2013). According to Liu (2016), the establishment of NDB is a healthy sign for future cooperation and economic development of BRICS countries.
The BRICS summit (2017) focused on increasing trade cooperation among involved countries. All these countries are now playing an important role in world affairs and are active members of regional and international organisations including the UN, WTO, G-20 and UN convention framework on climate change among others (Singh & Dube, 2013). In a recently held summit in Johannesburg, South Africa (2018), attended by heads of five countries, it was decided to increase partnership to maximise the gains arising from the fourth industrial revolution. Hopewell (2017) found that the formation of BRICS was not an illusion, and these countries along with other developing countries have been successful in changing the structure of power within WTO and influencing the Doha round. The emerging countries, particularly India, China and Brazil, showed cooperation to a great extent and challenged the traditional powers which were dominating the world trade system.
Given the resource base and economic performance of these countries from the last couple of decades, the present study makes an attempt to analyse the export flow among BRICS countries over the period 1992–2018. To analyse the export flow, the gravity model of trade has been utilised. Furthermore, the study investigates the creation of trade opportunities among BRICS countries after the formation of the group in 2009. Following Lohani (2020) and Mishra et al. (2015), time-invariant variables like distance, common language, common border and multilateral resistance term have been included to achieve the said objective.
There are few recent studies, like (Lohani, 2020; Mishra et al., 2015; Rasoulinezhad & Jabalameli, 2018) about the economic cooperation of BRICS countries. However, none of these studies have attempted to examine intra-BRICS export determinants which have been discussed in the present study. The study follows the approach adopted by Kumar and Ahmed (2015) to examine the economic performance of BRICS countries.
The rest of the article is organised in the following manner. Economic performance is discussed in Section 2. Section 3 discusses previously available literature. Similarly, Section 4 includes the model, methodology and data source. The empirical results are discussed in Section 5, followed by the summary and conclusion in Section 6.
Economic Performance of BRICS Countries
The performance of different economic indicators in BRICS countries has been presented in Table 1. About these countries, many predictions were made at the beginning of the present century that they would not only improve their own economic performance but may also play an important role in world economic affairs. Though these countries have performed well on the domestic economic front but have not been able to play their role in international forums as was predicted earlier. The BRICS as an economic unit has also failed to improve their mutual cooperation and trade as no multilateral trade agreement till date was signed. There are many reasons for poor economic cooperation in the group, including the domestic economic performance of these countries during this period, particularly after the financial crisis of 2008. Though India and China have led the world to overcome from the economic recession of 2008, it is China that has emerged as a leader in the world trade system. A look at Table 1 shows that China has emerged as the top export and import partner of other countries in the group. About India and China, Purushothaman (2004) is of the view that if India could match China in education and infrastructure development, then it could be a bigger growth story in the long run as it has achieved remarkable progress on many macroeconomic indicators.
Economic Indicators of BRICS Countries (Year 2017).
Economic Indicators of BRICS Countries (Year 2017).
Theoretical Framework: Intuitive Gravity Model
It is the universal gravitation law of Newton (1687) from which the gravity model of trade was derived. The law states that it is the gravitational force that attracts two points and is positively related to the mass of objects and negatively with the square of the distance between them. In Equation (1), Xij is a dependent variable which can be exports, imports or total trade depending on the nature of the study. The independent variables include the economic size of countries and the geographical distance between them. In simple form, the model explains the flow of trade between Country i and j with the economic size of partner countries (often GDP is taken as a proxy for economic size) and distance between two capitals. In addition, the Equation includes constant G, which captures country independent effect like world liberalization.
Tinbergen (1962) and Poyhonen (1963) applied gravity model on trade flow analysis between countries. The model came to know as ‘workhorse’ of international trade due to its successful application in analyzing the effect of various policy variables on trade cooperation between countries.
There is a plethora of literature on the gravity model of trade. Applying the gravity model and using both cross-sectional and panel data, Hassan (2001) found opportunities for trade creation among South Asian Association for Regional Cooperation (SAARC) countries without any evidence of trade diversion with other countries of the world. Another study by Martinez-Zarzoso (2003) applied the gravity model and examined the intra-block effects of the EU, NAFTA and Centro-American Common Market. The study concluded that the income elasticity of the exporter was higher than the income elasticity of the importer and argued for signing of a new preferential trade agreement among sample countries. Similarly, Rahman et al. (2006) applied the gravity model of trade and found significant intra-regional trade creation in SAARC Preferential Trading Arrangement (SAPTA).
Giving a theoretical justification for the application of the gravity model, Mishra et al. (2015) found that there is a positive relation between the GDP of India and its trade volume with the outside world. Finally, Sahu et al. (2017) utilised the augmented gravity model to examine exports of India with the top 50 trade partners. The study concludes that GDP, distance, population and real exchange rate are the main factors that play an important role in exports to India.
Using the data for 26 years, Kaya (2014) examined the relationship between GDP, nominal exchange rate and exports for BRICS countries from 1985 to 2011. The study concludes that there is statistically no significant relationship between exchange rates and exports. In another study, Kumar and Sehgal Arora (2015) have found that India’s exports as well as imports from other BRICS countries have increased in the first decade of the present century, particularly with China followed by South Africa. However, the imports have increased more rapidly, resulting in an increasing trade deficit of India with these countries. Similarly, Raghuramapatruni (2015) found that there is great potential for intra-BRICS trade as these countries are complementary to each other and have the opportunity to increase trade with each other in a number of product categories. Later on, Mathur et al. (2016) investigated the impact on India aligning with Regional Comprehensive Economic Partnership (RCEP) and BRICS and examined the gains and losses from intra-regional trade. The study concludes that Free Trade Agreement (FTA) in merchandise goods is beneficial for India with RECP countries. In the case of BRICS, India should negotiate for the entry of goods with comparative advantage on a reciprocal basis. Furthermore, Rasoulinezhad and Jabalameli (2018) are of the view that BRICS countries follow a different pattern of trade and distance has a negligible effect on the trade flow of India and China. Finally, Lohani (2020) applied the gravity model to examine the trade flow of India to other BRICS countries from 2001 to 2016. The study found that distance, common language and common border play an important role in the trade relationship of India with these countries. The study concludes that the government of India should negotiate with these countries for the removal of trade barriers to enhance trade cooperation with these countries. Finally, there are many studies where the gravity model has been utilised; however, only a few are related to BRICS countries.
Data Source
To achieve the said objective, annual data from different sources were collected. Data for exports among BRICS countries was collected from IMF (DOTS) database for the period 1992–2018. Data for GDP, GDP per capita and trade openness were extracted from World Development Indicators (WDI) World Bank. Besides, data for dummy variables which include common border and common language were extracted from Centre d’Etudes Prospectives et d’Information Internationales (CEPII).
Formulation of the Gravity Model
Table 2 presents a summary of statistics of variables used in the study. Mean and standard deviation are presented for determining range and coverage and to get an overview of the data. Similarly, Table 3 displays the coefficient of correlation. According to Kennedy (1986), analysis of correlation should be below 0.80%–0.90%, which is usually accepted to specify the multicollinearity of the series. It should be noted that the gravity model is estimated in logarithmic form and the subscript ‘Ln’ has been included in the variables. The logarithmic form helps to reduce heteroscedasticity in the data set and the coefficients estimated are interpreted as elasticity. Thus, Equation (1) is transformed into a log-linear form.
Descriptive Statistics.
Descriptive Statistics.
Coefficient of Correlation.
Econometric Specification of the Model
For estimation purpose, the above equation is transformed into the log form, so it confirms to usual regression analysis
In Equation (2),
In the traditional gravity model, cross-sectional data were used to estimate the trade relationship between countries for a particular year. However, cross-section data collected for several time periods (panel data) result in more useful information. The advantage of using panel data is that it captures the relevant relationship among variables and can monitor ‘unobservable trading-partner-pairs’ individual effect (Kumar & Ahmed, 2015). Matyas (1997) is of the view that the gravity model should be used with exporter, importer and time effect as cross-section is affected by misspecification. Egger (2000) believes that for disentangling time-invariant and country-specific effects, panel data methods are most appropriate.
In the present study, following (Brodzicki, 2009; Marques, 2008; Papazoglou et al., 2006), Prais-Winsten Regression with Panel Corrected Standard Errors (PCSE) with country and year-effect has been utilised. The PCSE assumes that the disturbances are heteroskedastic (each country has its own variance) and contemporaneously correlated across countries (each part of countries has its own covariance), Brodzicki (2009). Following Papazoglou et al. (2006), the study uses the following augmented form of the gravity model.
The next model, Equation (4), takes account of time along with country-pair effects. This accounts for Multilateral Resistance Terms (MTR) which include the country-pair effect as suggested by Anderson and van Wincoop (2003). The country-pair effect controls country-pair heterogeneity (Egger & Pfaffermayr, 2003), whereas time-effect controls time-specific shocks (Gaurav & Bharti, 2019).
In Equation (4), i, j and t stand for exporting Country, importing Country and time, respectively. Given the resource base and economic performance of BRICS countries from the last couple of decades, the present study makes an attempt to analyse the export flow among these countries over the period 1992–2018. Thus, the dependent variable 
Finally, to account for functional misspecification, the heteroscedasticity robust regression specification error test (RESET) and Wald test was used for OLS and PCSE, respectively. In RESET, rejection of the null hypothesis indicates that the model has a misspecification problem.
The results are presented in Table 4. To check the coherence of results, four models are estimated. Column (1) indicates OLS followed by PCSE in Column (2). These two models were estimated without taking into account MTR and time-effect. Columns (3) and (4) include both country-pair to control MTR and time effects.
Gravity Model of Exports.
Gravity Model of Exports.
The results presented in Table 4 indicates that in addition to basic variables, other variables are also statistically significant and have expected sign. The basic variables of the model, including the GDP of partner countries and distance have expected signs and are statistically significant at 1 and 5% level. The coefficient of GDP of reporting Country indicates that with an increase in GDP by 1%, exports between partner countries increase by 1.546% (Column 4). Similarly, with an increase in the GDP of the partner country by 1%, exports enhance by 0.897%. These results suggest that with an increase in the size of the economy, bilateral trade may also enhance.
The coefficient of distance is negative as expected, and significant which indicates that countries are expected to trade more with nearby counties. The coefficient on distance is −0.368 which shows that with an increase in distance by 1%, bilateral trade declines by −0.368%. This highlights the role distance plays in intra-BRICS trade.
The coefficient of other explanatory variables carries the expected sign and is statistically significant (Table 4, Column 4). According to Heckscher-Ohlin theory, countries trade with each other on the basis of factor endowment, which leads to comparative advantage in respective countries. The difference in factor endowment in different countries leads to more inter-industry trade. However, in present times, even countries with the same factor endowment trade with each other, which leads to intra-industry trade. The RFE focuses on the difference in factor endowment for a pair of countries. This theory is popular among economists due to its explanation of inter-industry trade (Wang et al., 2010). Inter-industry trade among the countries is likely to be large when there are differences in factor endowment. In contrast, among the countries, that enjoy a similar level of development and factor endowment, intra-industry trade dominates. The results in the present study though insignificant, but with a positive sign, indicates the presence of intra-industry trade among BRICS countries. 1
According to Frankel and Rose (2000), FTA between partner countries leads to an increase in bilateral trade by a multiplicative coefficient nearly equal to three, while Head (2003) is of the view that FTAs lead to an increase in trade by nearly 50% on average as established by gravity model. Despite expectations about the emergence of BRICS countries on the economic front, the FTA dummy indicates that the formation of the BRICS block has not contributed to the enhancement of bilateral trade among member countries. 2 It is important to mention here that BRICS is a political block that was formulated to increase political cooperation among member countries. The aim of the FTA dummy in the present study was to quantify the impact of this political cooperation on trade enhancement among member countries. However, the results indicate that the formation of this group has not significantly impacted trade cooperation between member countries. There are many reasons for poor economic cooperation in the group, including the domestic economic performance of these countries during this period, particularly after the financial crisis of 2008. In addition, these countries have not focused on increasing economic cooperation during all these years. Though India and China have led the world to overcome from the economic recession of 2008, it is China that has emerged as a leader in the world trade system and has emerged as the top export and import partner of other countries in the group as is clear from Table 1. According to Lohani (2020), trade of BRICS countries with non-members has increased at a higher pace as compared to trade with group members. However, the trade openness of both reporter and partner countries indicates that opening the domestic economy to the outside world enhances the trade volume of partner countries. This highlights the importance of converting BRICS into an economic block that can create enormous trade opportunities among member countries. The above analysis highlights the importance of economic cooperation among BRICS countries which may help to realise the potential of trade among them.
In addition to these factors, common language and common border also positively contributes to the enhancement of trade among member countries. Common language has a coefficient of 0.464, which indicates that trade increases by 0.464% between countries who share a common official language. Similarly, countries who share common border trade more by 0.543% than geographically distant countries.
The present study examines the nature of export flows among BRCIS countries within the gravity model framework. The panel data framework has been utilised for the period 1992–2018. To check the coherence of results, in addition to the OLS approach, which was used in the traditional gravity framework, the PCSE model was utilised. The results are in line with the theoretical background of variables and have expected signs. In addition to other variables, the main aim of the present study was to examine whether the formation of BRICS has a positive contribution to the enhancement of trade among member countries or not. To achieve the said objective, the dummy variable with the value of 0 before and 1 after the formation of BRICS in 2009 was set. However, the results reveal that the formation of the BRICS block has exercised a negative and significant impact on economic cooperation among member countries in line with Lohani (2020), which concludes trade diversion among BRICS countries. In addition, trade costs continue to be the biggest concern for participating countries which highlights the importance of improving infrastructure in member countries. On the other hand, the economic size of countries continues to positively contribute to trade opportunities for participating countries. Similarly, cultural factors, common border and trade openness enhances bilateral trade volume among these countries.
Thus, to further enhance trade among member countries, the BRICS block needs to be transformed into an economic block and the reduction of trade barriers in terms of tariffs is to be given priority. Moreover, given the role of distance, countries need to invest in infrastructure and improve logistic facilities. Finally, given the nature of trade, the basket of goods is to be diversified, which may reduce the uncertainty of global fluctuations on these countries in the future.
Scope for Further Research
This empirical work can be extended in different ways. The panel data can be extended to include other major trade partners of BRICS to get a better understanding of the determinants of trade. Moreover, the flow of imports among BRICS countries can also be examined. In addition, trade potential among these countries can be predicted which is important for further enhancement of trade volume.
Footnotes
Acknowledgement
The author is grateful to the anonymous referees of the Indian Economic Journal for their extremely useful suggestions to improve the quality of the article.
Declaration of Conflicting Interest
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author received no financial support for the research, authorship and/or publication of this article.
