Trade Flows of South Korea with MENA Countries

Abir Abdelli

International Trade and Commerce, Hankuk University of Foreign Studies, Seoul, South Korea.

DOI: 10.4236/oalib.1111411                

Abstract

The purpose of this paper is to empirically analyze the trade flows of South Korea with the Middle East and North African MENA countries, based on the gravity model, and to suggest possible techniques to increase Korean trade by pointing out the essential variables determining trade flows. The gravity model presumes that trade flows between two countries are positively linked to their population and economic size. We analyze pooled Ordinary Least Squares OLS, Fixed effects FE, and Random effects RE estimation methods to examine selected variables’ role in Korean-MENA trade growth. The findings show that distance does diminish trade. By analyzing a modified gravity equation of panel data estimations for Korean trade with the MENA region over 28 years, we find that for some regressions, the variables of oil rents of GDP and trade openness have a significant impact on Korean-MENA trade flows.

Keywords

International Relations, International Trade, Gravity Model, Gravity Equation, Panel Estimation Data, Pooled Ordinary Least Squares OLS, Fixed Effects FE, Random Effects RE, South Korea, MENA Countries

1. Introduction

Trade flows have known significant attention in the literature because of their vital contribution to the overall economy; they impact most countries worldwide [1] . The rapid economic growth in Asia has promoted the narrowing of trade growth between countries. Thus, South Korea has witnessed rapid economic growth since the 60s, which lead to a closer relationship with Middle Eastern countries [2] . In 2011, Korean companies had many construction projects in the MENA region. However, in Tunisia, Egypt, and Libya, the revolution so-called “Arab Spring” has challenged South Korean and MENA trade [2] . The Arab uprisings were at first started in Tunisia, followed by Egypt, Libya, and Syria, where the governments in many of these countries tried to sustain the new political position but in some other countries, the conflicts among the opposition and the ruling authorities carry on destabilizing the state, limit trade with South Korea.

The MENA countries have faced many challenges starting with the Arab Spring, which impacted the economy and population growth; many countries were affected by this phenomenon [3] . The differentiating attribute of the Arab Spring is its regional nature. During a few weeks of the first protests of Tunisia’s movement, other nations were overpowered in speed sequence: Egypt, Bahrain, Syria, Yemen, and Libya. Five countries representing close to 50 percent of MENA’s total population were entirely implicated in the phenomenon. And other countries were more marginally touched, like Jordan and Oman. Moreover, the Arab Spring has not been confined to the MENA region. But, its effects have gone global.

Moreover, in 2014, the global oil price decline negatively impacted the Gulf Cooperative Council (GCC) countries’ economies. The oil export revenues of four major GCC countries dropped by 46% in 2015. Yet, this decline led to a more economic partnership between South Korean and Middle East countries. Besides, in solving slow economic growth, GCC governments set up economic development plans and established policies to support and promote enterprises as key for diversification [4] .

The economic situation between Korea and the Arab countries has expanded in recent years for different reasons. South Korea saw the potential represented by the MENA countries [5] . “As of 2014, more than 80% of South Korea’s imports from the Organization for Economic Cooperation and Development (OPEC) are related to energy resources like crude oil and natural gas. And when it comes to the manufacturing of chemicals and petroleum is considered the second largest sector in the same year.” [6] During the Coronavirus pandemic investment growth pursued in the MENA region, 44 start-ups had raised more than $175 million in April. Given this, we can certainly say that the MENA region is important from a Korean perspective.

Therefore, this study aims to contribute to the emerging discussion of the main factors of trade flows of South Korea with the MENA countries and to complement the findings of the existing empirical studies. Recently, there are considerable challenges in the volume of trade between these countries. And this needs attention from an economic perspective. Thus, this research attempts to explain how trade flows within these countries justify their evolution by providing a clear picture of the intensity of trade flows and a better understanding of different economic growth indicators. In doing so, ultimately, this paper hopes to foster more dynamic trade relations between these countries. The relationship in terms of trade between these countries has fluctuated over the years. South Korea has developed strategies to gain growth in the MENA region, giving importance to the oil-producing countries and the population volume’s reputation.

2. Literature Review

South Korea-Arab countries’ relationships have been an oversight in the literature over the years, mainly due to South Korean ties with other countries and the importance of intra-trade. Despite Korea’s gradually increasing diplomatic role and military engagement in the MENA, the economic relationship has still been highly beneficial for these countries.

In 2010, investment from Korea into the MENA region was just $330 million, barely 1.42 percent of all Korean investment overseas. Relatively 39.1 percent of assets were invested in the petroleum and petroleum product industries, and nations like Iraq (27.22 percent), Oman (21.62 percent), UAE (19.37 percent), and Bahrain (18.5 percent) were the leading locations. Investment from the MENA to Korea was lower, counting to only $171 million, 90 percent derived from Saudi Arabia. One of the significant increases behind investment and exports in the Middle East is EPC contracting. Korean contractors first came to the region’s heavy civil engineering industry in the 70s around the oil boom. But currently, the MENA region is Korea’s most extensive EPC market. As an outcome, South Korea is one of the highest contractors in the area. South Korea’s market share in EPC contracts in the GCC states hosts over 50 percent of the contracts in the MENA area. In 2010, the overall amount of Korean EPC contracts in the Middle East was $47.2 billion, which was over 66 percent of Korea’s foreign EPC contracts. The top three partner countries were Kuwait, Emirates, and Saudi Arabia [7] .

South Korea’s economy keeps stable, increasingly interdependent ties with the GCC countries that go back to at least the 70s. At the time South Korean companies first received contracts for some of the Gulf’s first infrastructure companies in the period of the “First Oil Boom.” The economic start-out encountered by both areas has approved a new synergy to economic development, mainly premised on the twin pillars of technology transfer and energy. Exports from Korea to the GCC attained $17.8 billion in 2013, representing 3.2 percent of the country’s exports. 50% of the exports to the GCC went to Saudi Arabia, and a third to the UAE. Main export goods count steel products, machinery, electronics, and automobiles [8] .

The economic situation between Korea and the Arab countries has expanded in recent years for different reasons. South Korea saw the potential represented by the MENA countries. The importance of imports between these countries; “As of 2014, more than 80% of South Korea’s imports from the Organization for Economic Cooperation and Development (OPEC) are related to energy resources like crude oil and natural gas. And when it comes to the manufacturing of chemicals and petroleum is considered the second largest sector in the same year.” [5]

The concern arguably addressed by the occurrence in 2014 and yet extended till the end of 2017 vary from the relapsing encounter of the last in being severe to menace the existence of the six-country bloc (UAE, Saudi Arabia, Qatar, Kuwait, Bahrain, and Oman). These countries are unified by solid cultural, historical, and social ties, resembling many cultural aspects and religious traditions, and deep-rooted economic and political cooperation. Still, they stand as different groups with exclusive individual particularities.

In preserving good relationships globally, South Korea has strengthened its bilateral and multilateral trade with other countries. In March 2012; South Korea conducted eight FTAs with 45 countries, counting the United States, Singapore, Chile, the European Free Trade Association (EFTA), the Association of Southeast Asian Nations (ASEAN), India, the European Union (EU), and Peru. Besides, Korea has signed FTAs with Columbia and Turkey, and these are waiting for effectuation. The nation is promised in FTA-related agreements with New Zealand, Canada, Australia, Mexico, and GCC [9] .

Even in periods of the unstable situation of wars and domestic disorder in the area, the growth of the South Korean economy augmented the Middle East’s significance to Seoul due to the rising demand for oil and gas imports from the Gulf. Furthermore, the importance of the MENA economy is also shown in the August 2013 agreement between South Korea and Bahrain to inaugurate a joint committee for encouraging bilateral economic cooperation. The upgrade of economic relations applies as another pace in institutionalizing economic ties between South Korea and the MENA region. It started with Seoul’s decision to analyze an FTA between Korea and the UAE [10] . Moreover, the visit of the Korean president in 2018 to reinforce the bilateral trade relation between Korea and the Emirates showed the importance of MENA’s economy to South Korea, and the prime minister’s visit to the Maghreb countries to promote further collaboration in the region [11] . Moreover, Korea successfully secured 58 B2B memorandums of understanding (MOUs) and agreements with Saudi Arabia and the United Arab Emirates (UAE), surpassing USD 35 billion, as well as UAE’s commitment to $30 billion in investment [12] .

Table 1 explains the trade between South Korea and the MENA countries from 2014 to 2018.

With the economic problems regarding the recession of oil prices in 2014 that GCC countries are facing, there are affecting the economic cooperation of these countries with South Korea. Since Korea imports over 60% of its oil from GCC countries. More collaboration in different files should be appointed. As pointed out by some authors, economic partnership is an important factor of growth between these countries. In the second half of 2014, the oil export revenues of

Table 1. Korea-MENA Trade Statistics 2014-2018 Unit: $100,000,000.

Source: The Korea International Trade Association (KITA, https://www.kita.net/).

four major GCC countries, Kuwait, Qatar, Saudi Arabia, and the UAE, declined. This downfall drove economic affiliation between South Korean and Middle East countries to boost and maintain growth. However, as shown in Table 1, the trade volume between South Korea and the MENA region decreased from 2014 to 2018, facing a serious challenge. But the MENA countries are working hard to improve their conditions. In 2016, Some MENA countries created Major Economic Development Plans to improve their economic growth, to maintain good economic relations with Korea [8] .

3. Research Methodology

To demonstrate the above causal relationship, this research collects information on various factors related to trade between MENA countries and South Korea as GDP, population growth, exchange rate, FDI, trade openness, and oil rents (% of GDP). And this collected information allows this research to conduct a statistical analysis of the causal relationship. More specifically, this study uses panel data covering 1990 to 2018 (although with many missing observations in the MENA region). The Panel data observed over many periods conclude in more suitable information than cross-section data alone. The purposes of this data are: first, panels can apprehend the suitable relationships between determinants over time; and second, panels can examine indistinct trading-partner-pairs’ impacts [13] . When individual results associate with the regressors, OLS evaluates that omitting individual results could be biased, thus, the use of the panel data method for examining the gravity model of trade.

3.1. Analysis Method

The gravity model has been repeatedly used to analyze trade flows since Tinbergen, followed by several works [14] . “Gravity models are estimated in terms of natural logarithms (denoted ‘ln’) using trade flows or exports from country x to country y.”

In this paper, we will be using the augmented model, other economically controlled variables such as exchange rate, trade openness, FDI, and oil rents of GDP will be used as well. As referred that the gravity equation is the workhorse model of trade [15] . It has been long recognized for its steady empirical success in identifying many different types of flows and determining the trade between countries [16] .

As mentioned Tinbergen’s gravity equation below [17] :

$\text{TA,B}\propto \frac{\left(\text{GDPA}\right)\alpha \left(\text{GDPB}\right)\beta }{\left(\text{DistAB}\right)\zeta }$

The essential terms of Equation (1) can be expressed in logarithmic form as follows:

$In\left(I_{ijt}\right)=\alpha +\beta 1\text{Ln}\left(Y_{it}\cdot Y_{jt}\right)+\beta 2\left(D_{ijt}\right)+U_{ijt}$ (1)

In this equation (Equation (1)), “I_ijt” is the import volume of “i” from “j” duringthe period of “t”; “Y_it” and “Y_jt” are considered economic sizes of “i” and “j” in terms of GDP; (D_ijt) is the geographical distance between the two countries. The economic growth of one country will positively influence the bilateral trade between the two. The above equation can again be rewritten into the log-linear form so that it coordinates with the usual regression analysis.

$\text{Ln}{\left(\text{EX}\right)}_{ij}=\complement +\alpha \text{Ln}{\left(\text{GDP}\right)}_i+\beta \text{Ln}{\left(\text{GDP}\right)}_j+\gamma \text{Ln}{\left(\text{Distw}\right)}_{ij}+\epsilon ij$ (2)

Equation (2): “Ln(EX)_ij” is the log of export volume of “i” to “j”, “Ln(GDP)_i” and “Ln(GDP)_j” are the GDP of the two countries, and Ln(Distw)_ij is the log of the geographical distance. Although many studies have utilized the gravity model, only a few used it to explain the importance of other variables, such as trade openness, governance, and institutions. This study, however, will utilize one of these uncommon variables since, in addition to the essential gravity equation variables, it uses openness, economic growth, population size, FDI, exchange rates, and oil rents of GDP to see how they perform in the gravity equation. Even though studies using such variables do exist, none used them in the same sampling countries for this research. By referring to some equations used before such as: $\text{Ln}{\left(\text{EX}\right)}_{ij}=\complement +\alpha \text{Ln}{\left(\text{GDP}\right)}_i+\beta \text{Ln}{\left(\text{GDP}\right)}_j+\gamma \text{Ln}{\left(\text{Distw}\right)}_{ij}+\epsilon ij$ [18] .

As the econometric description of the gravity model in this study is an augmented gravity model for exports and imports of Korea with the MENA countries, the main equations are as follows:

$\begin{array}{c}\text{Ln}\left(\text{ExportKjt}\right)=\alpha 0+\alpha \text{1RealGDPKt}+\alpha \text{2RealGDPjt}\\ +\alpha \text{3PopulationgrowthKt}+\alpha \text{4Populationgrowthjt}\\ +\alpha \text{5DistanceKj}+\alpha \text{6Oilrent}+\alpha \text{7Exchangerate}\\ +\alpha \text{8FDI}+\alpha \text{9Tradeopenness}+{\epsilon }_{\text{ijt}}\end{array}$ (1)

The explanatory variables of Equation (1) are defined as:

Ln(Export Kjt) = Logarithm of Korean exports to recipient country j at year t

Real GDP Kt = Real GDP growth of Korea at year t

Real GDP jt = Real GDP growth of recipient country j at year t

Population growth Kt = Korean population growth at year t

Population growth jt = Population growth of recipient country j at year t

Distance Kj = Distance between Korea (Capital) and recipient country j (Capitals)

Oil rent = Oil rents percent of GDP of MENA countries

Exchange rate = Exchange rate: local currency units per U.S. dollar

FDI = Net inflows in the reporting economy from foreign investors, and is divided by GDP

Trade openness = Trade openness index for MENA countries affecting Korean trade

α0 - 9 = Parameter values

ε^ijt = Error term

Also, the econometric specification of the gravity model for import volumes is written as follows:

$\begin{array}{c}\text{Ln}\left(\text{ImportKjt}\right)=\alpha 0+\alpha \text{1RealGDPKt}+\alpha \text{2RealGDPjt}\\ +\alpha \text{3PopulationgrowthKt}+\alpha \text{4Populationgrowthjt}\\ +\alpha \text{5DistanceKj}+\alpha \text{6OilRent}+\alpha \text{7Exchangerate}\\ +\alpha \text{8FDI}+\alpha \text{9Tradeopenness}+{\epsilon }^{ijt}\end{array}$ (2)

where: Ln(Import Kjt) = Logarithm of Korean imports to recipient country j at year t

Real GDP Kt = Real GDP growth of Korea at year t

Real GDP jt = Real GDP growth of recipient country j at year t

Population growth Kt = Korean population growth at year t

Population growth jt = Population growth of recipient country j at year t

Distance Kj = Distance between Korea (capital) and recipient country (capitals) j

Oil rent = Oil rents percent of GDP of MENA countries

Exchange rate = Exchange rate: local currency units per U.S. dollar

FDI = Net inflows in the reporting economy from foreign investors, and is divided by GDP

Trade openness = Trade openness index for MENA countries affecting Korean trade

α0 - 9 = Parameter values

ε^ijt = Error term

The gravity models have been employed in economics with different methods. In analyzing the trade coalition among the GCC states within the intra-Arab area and the states outside it by employing a gravity model [19] . Some variables were added to the standard gravity model like the area, language, common territories, trade openness, and the Arab nations. These authors demonstrated that the variable GDP was significant and positively connected to the intra-trade between the Arab areas. In contrast, the bilateral distance between countries has a negative influence on their economic size.

Moreover, by using a gravity model called Stochastic Frontier Gravity model (SFGM) to analyze intra-Arab trade performance for 1998-2015 [20] . The impact of some selected variables on bilateral trade showed that the main determinants of the gravity model, such as real GDP, population size, and distance, have a significant impact on trade volumes among Arab countries.

In another paper the extended gravity model of trade with governance factors observed for the exports of some MENA states, with their 189 trading partners and for all exporters from 1996 to 2013. It pointed out that the findings showed ameliorations in five of the six governance variables that rise exports from MENA countries. But, each of the six governance determinants employed has a significant impact on bilateral trade for the exporters. Also, they indicated the implications of country-pair similitude in governance factors suggesting that a resemblance level of regulatory quality and the rule of law in exporting and importing countries boosts exports from the MENA region [21] .

By using the gravity model, [22] it showed that during the period of 2000-2018 the main factors affecting the Egyptian bilateral trade are Egypt’s gross domestic product, importer’s gross domestic product, the border variable, and the distance between Egypt to the main trading partners.

In analyzing the trade between China and 97 countries, [23] the paper explained that the impact of ASEAN and APEC member countries on Chinese trade. By using an extended gravity model, the findings show the importance of GDP and population growth.

In examining the main patterns of international trade in the countries of Central and Eastern Europe by investigating the gravity model of trade, the results demonstrated that distance between countries and their gross domestic products are still crucial factors that determine trade flows between them [16] .

3.2. Data

This research utilized primary data for this study. The primary sources of data extracted are from World Development Indicators (World Bank), International Monetary Fund (IMF), Korea Customs and Trade Development Institute (KCTDI), Trading Economics (TE), and The Global Economy.com from 1990 to 2018 due to lack of data of the MENA countries in recent years. Other sources used in this research are articles, journals from the internet, libraries, and statistical bases of data. The distinct theoretical and empirical reviews on openness in different MENA countries were used to facilitate this study’s successful finalization. Independent variables were often found in mainstream theories of the gravity model mainly. In contrast, the dependent variables were taken from variables that have mostly been utilized in empirical studies of international trade flows.

This research is mainly based on understating the selected indicators affecting South Korean trade flows with MENA countries based on a quantitative approach. And the contribution is to collect more information about the MENA region countries interconnecting with South Korea regarding trade and other factors related to the study (GDP, population growth, exchange rate, and openness). The software program for obtaining regressions is known as GRETL. ¹And the research methodology aims to explore trade volumes based on four-panel estimations for selected countries in this study. It is applied in fixed effects (FE), Random effects (RE), and pooled ordinary least squares (OLS) techniques.

Furthermore, in this study, the sample is South Korea and its trading partners from the MENA region (Algeria, Egypt, Jordan, Lebanon, Libya, Saudi Arabia, Tunisia, Yemen, Oman, Qatar, United Arab Emirates UAE, Morocco, Iran, Bahrain, Iraq, Kuwait, Sudan, and Syria). South Sudan, Djibouti, and Mauritania were excluded from this study for lack of data (See Table 2).

Table 2. Data description and sources.

4. Findings and Discussion

4.1. Findings

The regression Table 3 on the dependent variable Exports of Korea to the MENA region’s countries shows that the controlled variables real GDP growth of Korea and the Korean population growth are statistically significant at a 1% level. Also, for the explanatory variable, oil rents of GDP have a positive sign and are statistically significant at a 5% level, and the variable distance as expected has a negative sign, which means affect negatively Korean exports to the MENA region. The results are consistent with previous studies [24] .

The R² is .46: about 46% of the dependent variable log Exports’ total variability is explained by the explanatory variables considered in this model. Besides, the explanatory variable, oil rents of GDP, has a positive coefficient. It indicates that as the value of the variable oil rents of GDP of MENA countries increases by 2%, the mean of the dependent variable Korean exports to these countries also tends to increase. Also, the coefficient of the MENA countries’ population growth, as noted in this regression table (Population growth j), increases by 5%, which means an increase in Korean exports to the MENA countries. On the other hand, the controlled variables’ negative coefficients, as shown, reduce the dependent variable.

In the regression Table 4, the explanatory variable, Korean population growth, is statistically significant at a 1% level. The other controlled variables population growth of MENA countries and the real GDP of Korea are significant at a 5% level. The coefficient of the variable, oil rents of GDP, has a positive sign and is statistically significant at the 10% level. And as predicted for the variable, distance, it has a negative coefficient confirming the gravity theory [25] . The R² is 0.58: the explanatory variables explain about 58% of the total variability of the dependent variable log Imports.

The coefficient of the controlled variable, population growth, in the MENA countries (Population growth j) is positive 0.11, which means an 11% increase in Korea’s imports from these countries. And as shown in this table, the explanatory variable, oil rents of GDP, increase by 6% in the Korean imports from these countries.

Table 5 shows that the controlled variables, Korean real GDP, Korean population growth, and exchange rate ER, are statistically significant at a 1% level. Also, trade openness is statistically significant at the 5% level. And for MENA countries, population growth is significant at the 10% level. The variable distance is omitted in this model explaining that the issue faced by a FE model is that it cannot estimate variables that do not vary across time, such as distance [26] . The positive coefficients of the controlled variables of the MENA countries’ population growth, trade openness, and oil rents of GDP increase Korea’s exports to these countries. However, the real GDP growth of Korean and MENA countries has negative coefficients, which means decreasing Korean exports with these countries.

When an F-test² is performed, we get an F-statistic of 45, which means we can reject the null hypothesis noting that all intercepts are not the same. Since F-test is supposed to be equal to zero, rejecting this hypothesis implies that the fixed effects are non-zero. Also, as observed in the table, the p-value is very small, which means the groups do not have a common intercept [27] .

This regression Table 6 shows that the explanatory variable, Korean population growth, is statistically significant at a 1% level. And the variable distance is omitted. Also, the controlled variable population growth of the MENA countries has a positive sign of 7%, which increases the dependent variable imports of Korea from these countries, and this variable is statistically significant at a 5% level. Moreover, the variable, oil rents of GDP, increase by 2% in Korea’s imports from the MENA countries. Besides, trade openness as well increases by 1% in Korean imports.

When an F-test is performed, we get an F-statistic of 47, which shows that we can reject the null hypothesis that groups have a common intercept. We also employed FE models controlling for time-fixed effects for the dependent variables of Korean exports and imports with the MENA countries, as referred by some studies [28] .

In this regression Table 7, we controlled by time fixed effects, it shows that the controlled variable real GDP growth of Korea is statistically significant at a 1% level. Also, the MENA countries’ variable, real GDP growth, has a positive sign and is statistically significant at a 5% level. Moreover, the controlled variables, coefficients of real GDP growth and population growth of the MENA countries, are positive, which means that the dependent variable, exports of Korea, increases when these variables increase. And when the negative coefficients of the controlled variables rise, it decreases the dependent variable, exports of Korea to these MENA countries.

We get an F-statistic of 179, which means that we can reject the null hypothesis. Since the p-value is very small, according to Keum, the FE model can evaluate individual and time-specific effects from time- and individual-variant determinants. However, it cannot identify the individual-specific impacts concerning the individual-variant but time-invariant variables, such as the distance variable between two trading countries in the gravity model [29] .

In Table 8, we controlled by time-fixed effects, and the explanatory variable, Korean population growth, is statistically significant at a 1% level. Also, the MENA countries’ variable, real GDP growth, has a positive sign and is statistically significant at a 5% level. As mentioned in this regression table, the explanatory variables, Korean real GDP growth, real GDP growth, population growth, trade openness, and oil rents of GDP of MENA countries, have a positive sign, which means, increasing the dependent variable, imports of Korea from these MENA countries. And the negative coefficients indicate that as the independent variables grow, the dependent variable, imports, tends to decrease. We get an F-statistic of 59, which means that we can reject the null hypothesis that the groups have a common intercept.

The GLS regression Table 9 shows that the controlled variables, Korean population growth, Korean real GDP growth, and the exchange rate of MENA countries, are statistically significant at a 1% level. And the explanatory variable, trade openness, is significant at a 5% level. Also, the MENA countries controlled variables, real GDP growth, and population growth, are significant at a 10% level. As expected for the variable distance, the coefficient has a negative sign. Moreover, the controlled variables with positive coefficients, as noted in this table, increase Korea’s exports with the MENA region. For instance, the controlled variable, population growth of the MENA countries (Population growth j), is 0.06, which means a 6% increase in the dependent variable, exports. However, for the variables with negative coefficient signs, the real GDPs of MENA countries decrease exports of Korea with these countries.

When we conduct a Breusch-Pagan test, we get a Chi^-squared3 of 1974.51 to reject the null hypothesis, which stated that the Variance of the unit-specific error = 0. This test’s degrees of freedom reflect that the contrast is based on a comparison of the regression coefficients other than the intercept. Therefore, it is evident that there are some random effects present. However, Breusch-Pagan test has a p-value below 0.05, then the null hypothesis of homoskedasticity is rejected, and heteroskedasticity is assumed. The Hausman test indicates that the RE is not correlated with the other regressors. The resulting chi² is high, which means there is no high correlation between data. And it suggests that the RE estimator is not consistent. Also, the p-value is very significant and small, which means we also reject the hypothesis that GLS estimates are consistent.

This regression Table 10 indicates that the independent variable, Korean population growth, is statistically significant at a 1% level. And most of the controlled variables are statistically significant. However, the variable, distance, has a negative sign, but it is statistically significant. The coefficient of the variable, population growth of the MENA countries, is positive, which means a 6% increase in this variable; population growth positively affects the dependent variable imports of Korea. Also, the variable, trade openness of the MENA countries, has a positive sign; a 1% increase in this variable increases Korean imports. And the same goes for the coefficient of the explanatory variable oil rents of GDP, which is a 3% increase in Korea’s imports as well. When we conduct a Breusch-Pagan test, we get a Chi² of 1903.68 to reject the null hypothesis. The Hausman test indicates that the random effect is not correlated with the other regressors. The resulting chi² is high, resulting in that GLS estimates are not consistent.

Table 3. Dependent variable: exports of Korea to the MENA countries Pooled OLS.

Note: (1) ***p < 0.01, **p < 0.05, and *p < 0.10. (2) Time dummies are not reported.

Table 4. Dependent variable: imports of Korea from the MENA Countries Pooled OLS.

Note: (1) ***p < 0.01, **p < 0.05, and *p < 0.10. (2) Time dummies are not reported.

Table 5. Dependent variable: exports of Korea to the MENA Countries FE.

Joint test on named regressors - Test statistic: F(8, 14) = 11000.2 with p-value = P(F(8, 14) > 11000. 2) = 3.09015e−025 Robust test for differing group intercepts - Null hypothesis: The groups have a common intercept Test statistic: Welch F(14, 151.1) = 45.2053 with p-value = P(F(14, 151.1) > 45.2053) = 8.98742e−047 Note: (1) *** p < 0.01, ** p < 0.05, and * p < 0.10. (2) Time dummies are not reported.

Table 6. Dependent variable: imports of Korea from the MENA Countries FE.

Joint test on named regressors - Test statistic: F(8, 14) = 34359.9 with p-value = P(F(8, 14) > 34359.9) = 1.06628e−028 Robust test for differing group intercepts - Null hypothesis: The groups have a common intercept Test statistic: Welch F(14, 150.7) = 47.2151with p-value = P(F(14, 150.7) > 47.2151) = 7.71887e−048 Note: (1) *** p < 0.01, ** p < 0.05, and * p < 0.10. (2) Time dummies are not reported.

Table 7. Dependent variable: exports of Korea to the MENA countries FE controlling for the time fixed effects.

Joint test on named regressors - Test statistic: F(8, 14) = 204.664 with p-value = P(F(8, 14) > 204.664) = 3.69466e−013 Robust test for differing group intercepts - Null hypothesis: The groups have a common intercept Test statistic: Welch F(14, 150.7) = 179.173 with p-value = P(F(14, 150.7) > 179.173) = 2.71757e−086 Note: (1) *** p < 0.01, ** p < 0.05, and * p < 0.10. (2) Time effects reported as dummy variables.

Table 8. Dependent variable: imports of Korea from the MENA countries FE controlling for time fixed effects.

Joint test on named regressors - Test statistic: F(8, 14) = 14.2667 with p-value = P(F(8, 14) > 14.2667) = 1.65098e−005 Robust test for differing group intercepts - Null hypothesis: The groups have a common intercept Test statistic: Welch F(14, 150.7) = 58.6742 with p-value = P(F(14, 150.7) > 58.6742) = 1.21911e−053 Note: (1) *** p < 0.01, ** p < 0.05, and * p < 0.10. (2) Time effects reported as dummy variables.

Table 9. Dependent variable: exports of Korea to the MENA countries RE-GLS.

“Between” variance = 1.65328 “Within” variance = 0.424121mean theta = 0.903847 Joint test on named regressors - Asymptotic test statistic: Chi-square (9) = 83950.8 with p-value = 0 Breusch-Pagan test - Null hypothesis: Variance of the unit-specific error = 0 Asymptotic test statistic: Chi-square (1) = 1974.51 with p-value = 0 Hausman test - Null hypothesis: GLS estimates are consistent Asymptotic test statistic: Chi-square (8)⁶ = 363.583 with p-value = 1.13935e−073 Note: (1) *** p < 0.01, ** p < 0.05, and * p < 0.10. (2) Time dummies are not reported.

Table 10. Dependent variable: imports of Korea from the MENA countries RE-GLS.

“Between” variance = 2.75076 “Within” variance = 1.10533mean theta = 0.879974 Joint test on named regressors - Asymptotic test statistic: Chi-square (9) = 4408.29 with p-value = 0 Breusch-Pagan test - Null hypothesis: Variance of the unit-specific error = 0 Asymptotic test statistic: Chi-square (1) = 1903.68 with p-value = 0 Hausman test - Null hypothesis: GLS estimates are consistent Asymptotic test statistic: Chi-square (8) = 290.698 with p-value = 3.92505e−058

Note: (1) *** p < 0.01, ** p < 0.05, and * p < 0.10. (2) Time dummies are not reported.

4.2. Discussion

In this study, the FE estimator presumes an unobserved heterogeneous factor that is constant across time and impacts each country/period of the panel like the variable distance in our model. On the other hand, the RE model appoints no correlation between the individual impacts and the regressors, supposing that the unobserved heterogeneous unit is precisely exogenous. When the null hypothesis has zero correlation, the RE model is more consistent. Albeit, if the null is rejected, only the FE model offers accurate estimators [24] .

The FE model is selected if there is a correlation between individual effects and explanatory variables [30] . And the regression model can control the impact of time effects from controlled variables to estimate the net impact of explanatory variables on trade flows’ dependent variables. Therefore, two tests are looked at after running a RE model: The Breush-Pagan Test for heteroskedasticity of the unit-specific errors and the Hausman Test for consistency of the GLS estimates. The Breusch Pagan test analyzes whether pooled OLS estimation is better or the Random/Fixed Effect technique. The null hypothesis is that pooled estimates are acceptable, and the alternative hypothesis is that random effect technique estimates are appropriate. If the p-value is low, an alternative hypothesis is accepted [31] .

The Hausman test presents a method for testing the RE model’s adequacy [32] . If the null hypothesis is rejected, the model is inconsistent. But, it is essential to mention that this outcome does not require that the FE model is appropriate. So, If the Hausman test cannot demonstrate a considerable divergence (p > 0.05), it does not accordingly show that the RE estimator is free from bias. Usually, in many studies, the correlation between the covariates and unit effects is not precisely zero. This paper has employed the Hausman test to check whether fixed or random effects are more applied to evaluation [33] . And other studies tried to choose between fixed or random effect models [34] .

The results of the three methods employed in this research show that some of the controlled variables are correlated and positively affect Korea’s trade volumes with MENA countries. As indicated by the gravity theory, the GDP growth and population size are significant and affect trade flows, and distance in all cases is negative. Therefore, the hypotheses in the gravity model are used to support the evidence of the trade volumes relationship of the MENA countries with Korea, driven mostly by the real GDP and the distance. The results found by using the method pooled OLS shows the importance of population growth of the MENA countries’ interacting positively with Korean trade flows. For instance, for the dependent variables, the controlled variable, Korean population growth, is statistically significant. The variable, distance, does not turn out to be significant as expected, which is to confirm the hypotheses of the gravity model. However, the first hypothesis in this study, Korean and the MENA countries’ GDPs have a significant impact on Korean trade flows with the MENA region, is rejected.

The FE model without time-fixed effects designates that the determinants of Korean real GDP, Korean population growth, and the exchange rate of MENA countries interact positively with Korean exports. And the method FE with controlling for time-fixed effects shows the importance of Korea’s real GDP growth for exports.

Moreover, for the RE technique used in this study GLS, shows that the controlled variables of Korean population growth, real GDP growth of Korea, and the exchange rate are statistically significant. And for most controlled variables used in this method, they are significant. The variable distance has a negative coefficient sign as confirmed in different studies. The result of the Hausman test in the GLS models shows that the null hypothesis is rejected, which aims that the FE model can be accepted as an alternative, persistent with earlier works of utilizing fixed effects [35] .

In the RE specification, there is also bias within the coefficient to evaluate if the covariates are correlated with the unit effects. It doesn’t show any correlation among the covariates and the unit effects explain that FE should be preferred. However, in a study, the authors preferred to employ the RE model to the FE model following previous works [36] .

5. Conclusions

In the age of globalization, international trade plays a vital role in any country’s development process. In this regard, existing studies have widely examined the bilateral trade relationship between many countries. Some scholars have provided substantial theoretical support for the gravity model of trade. Regardless of all the existing and extensively accepted modifications of the gravity model, we should choose an appropriate model for this study. This paper focused on South Korea, which is considered one of the Asian Tigers, and its trading partners in the MENA region. Meanwhile, Korea has faced many opportunities as well as challenges when participating in global trade. Moreover, this study tried to establish a research framework for a comparative study on the impact of Korea’s intensification of economic linkage with the MENA countries.

The trade structure of Korea is very different from the MENA countries. To better understand the relationship of Korean trade flows with these countries, this study goes beyond previous literature by using MENA region states’ interactions with Korea. As a result, we found that the distance between Korea and its trading partners in the MENA region does affect the bilateral trade flows. As shown in the regression models, the variable distance is negative and decreases trade flows, which is consistent with what was found in other studies [25] . Moreover, according to this study, the population size of MENA countries explains the growth of trade volumes. Other controlled variables seem to affect trade flows as well. However, this study failed to identify that the GDP growth of Korea and MENA countries increases Korean trade flows.

The new economic alliance between South Korea and the MENA countries should be considered durable and from a long-term perspective. Therefore, it can create solid, stable, and sustained mutual economic gains. Korea-MENA economic cooperation should seek regions that engage in the future potential of both economies. This work can be followed up by other researchers who wish to examine further study in this matter.

Based on all estimation results, this study would first suggest that the policymakers emphasize the importance of bilateral trade volumes of Korea with the MENA countries, which could increase the economic growth across all countries. Nevertheless, the research results on MENA countries suggest that we need further studies to find the potential variables that improve trade volume growth. Hence, researchers who aim to analyze trade growth in this area are suggested to classify countries by similar income per capita to find significant findings in these countries or add more countries. Also, adding variables such as culture, language, corruption, and degree of democracy would give an interesting result to better understand trade patterns. This study also suggests that academic researchers who aim to follow these techniques should take coherent and consistent data for analysis to include more countries. If there were more consistent data on the MENA countries, the estimation results of this study could have been improved. Such problems were especially common for some of the MENA countries, where inadequate social infrastructure, political instability, and such problems prevent the collection and reporting of such statistics which incites a need for future researchers to revise the specific method for gathering data.

Acknowledgements

The author would like to thank the anonymous reviewers for their valuable comments on this paper.

NOTES

¹Gretl “Gnu Regression, Econometrics and Time-series Library”. It is an open-source statistical package. http://gretl.sourceforge.net/.

²F statistic test: F-value is for the F-test that comapres whether statistical models that have been fitted to data set are appropriate.

³A chi-squared (χ²) statistic is a test that measures how expectations compare to actual observed data.

⁴Real Gross Domestic Product of the MENA countries.

⁵Real Gross Domestic Product of Korea.

⁶The degrees of freedom (often abbreviated as df or d) tell you how many numbers in your grid are actually independent.

Conflicts of Interest

The author declares no conflicts of interest.

References

[1]	Avetisyan, M. and Hertel, T. (2021) Impacts of trade facilitation on modal choice and international trade flows. Economics of Transportation, 28, Article ID: 100236. https://doi.org/10.1016/j.ecotra.2021.100236
[2]	Levkowitz, A. (2012) Korea and the Middle East Turmoil: A Reassessment of South Korea-Middle East Relations. The Korean Journal of Defense Analysis, 24, 225-238.
[3]	Luciani, G. (2017) Oil Rent and Regional Economic Development in MENA. International Development Policy, 8, 211-212. https://doi.org/10.1163/9789004336452_012
[4]	Lee, K.H., et al. (2017) Sustainable Economic Cooperation between Korea and the Middle East in Times of Lower Oil Prices. KIEP Research Paper World Economy Brief 17-7, 2-4.
[5]	Rasoulinezhad, E. and Kang, G.S. (2016) A Panel Data Analysis of South Korea’s Trade with OPEC Member Countries: The Gravity Model Approach. Iranian Economic Review, 20, 203-224.
[6]	Wamda (2021) Mena Startups Raised $175 Million in April 2021. https://www.wamda.com/2021/05/mena-startups-raised-175-million-april-2021
[7]	Baran, H. (2011) Economic Implications for South Korea of the Current Transformation in the Middle East. Korean Economy, 27, 52-53.
[8]	Lee, K.H. (2014) Korea and the GCC: Reaching a Sustainable Economic Partnership. Middle East Institute, Washington DC. https://www.mei.edu/publications/korea-and-gcc-reaching-sustainable-economic-partnership
[9]	Nguyen, V. (2014) South Korea and Its International Trade. Policies Lead to the Miracle Development. 21.
[10]	Levkowitz, A. (2013) South Korea’s Middle East Policy. Mideast Security and Policy Studies 106, The Begin-Sadat Center for Strategic Studies, Ramat Gan.
[11]	Ministry of Foreign Affairs. http://www.mofa.go.kr/eng/wpge/m_4911/contents.do
[12]	Korea to Boost Economic Ties with Middle East (2023) Korea Development Institute KDI. https://eiec.kdi.re.kr/publish/ebView.do?cidx=1224&ccode=000100000100003&pp=20&pg=1&sel_year=2014&sel_month=05
[13]	Martínez-Zarzoso, I. and Nowak-Lehmann, F. (2003) Augmented Gravity Model: An Empirical Application to Mercosur-European Union Trade Flows. Journal of Applied Economics, 6, 291-316. https://doi.org/10.1080/15140326.2003.12040596
[14]	Anderson, J.E. (2011) The Gravity Model. Annual Review of Economics, 3, 133-160. https://doi.org/10.1146/annurev-economics-111809-125114
[15]	Yotov, Y.V. (2022) On the Role of Domestic Trade Flows for Estimating the Gravity Model of Trade. Contemporary Economic Policy, Western Economic Association International, 40, 526-540. https://doi.org/10.1111/coep.12567
[16]	Lypko, N. (2022) The Gravity Model of Trade: The Case of Central and Eastern Europe. Lexonomica, 14, 187-212. https://doi.org/10.18690/lexonomica.14.2.187-212.2022
[17]	Chaney, T. (2018) The Gravity Equation in International Trade: An Explanation. Journal of Political Economy, 126, 150-177. https://doi.org/10.1086/694292
[18]	Burger, M., Van Oort, F. and Linders, G. (2009) On the Specification of the Gravity Model of Trade: Zeros, Excess Zeros, and Zero-Inflated Estimation. Spatial Economic Analysis Journal, 4, 167-190. https://doi.org/10.1080/17421770902834327
[19]	Waheed, A. and Abbas, S. (2015) Potential Export Markets for Bahrain: A Panel Data Analysis. International Journal of Trade, Economics and Finance, 6, 165-169. https://doi.org/10.7763/IJTEF.2015.V6.463
[20]	Ebaidalla, E.M. and Ali, M.E.M. (2018) Assessing the Intra-Arab Trade Integration and Potential: Evidence from Stochastic Frontier Gravity Model. Economic Research Forum Working Papers, No. 1247.
[21]	Martínez-Zarzoso, I. and Márquez-Ramos, L. (2019) Exports and Governance: Is the Middle East and North Africa Region Different? The World Economy, 42, 143-174. https://doi.org/10.1111/twec.12633
[22]	Sohail, F., Sohail, A., Wang, W. and He, X. (2021) An Empirical Study of Bilateral Trade in Egypt: Gravity Model Approach (2000-2018). Open Journal of Business and Management, 9, 912-927. https://doi.org/10.4236/ojbm.2021.92049
[23]	Emikönel, M. (2022) The Impact of International Organizations on Chinese Trade as the Determiner of Trade: The Gravity Model Approach. The Chinese Economy, 55, 26-40. https://doi.org/10.1080/10971475.2021.1892920
[24]	Gómez-Herrera, E. (2013) Comparing Alternative Methods to Estimate Gravity Models of Bilateral Trade. Empirical Economics, 44, 1087-1111. https://doi.org/10.1007/s00181-012-0576-2
[25]	Ekanayake, E.M., Mukherjee, A. and Veeramacheneni, B. (2010) Trade Blocks and the Gravity Model: A Study of Economic Integration among Asian Developing Countries. Journal of Economic Integration, 25, 627-643. https://doi.org/10.11130/jei.2010.25.4.627
[26]	Javed, I., Ashfaq, M., Adil, A. and Khuda, B. (2016) Analysis of Agricultural Trade between Pakistan and United Arab Emirates: An Application of Gravity Model. Journal of Agricultural Research, 54, 787-799.
[27]	Wooldridge, M. (2016) Introductory Econometrics: A Modern Approach. Cengage Learning, Boston, 493.
[28]	Prehn, S., Brümmer, B. and Glauben, T. (2016) Gravity Model Estimation: Fixed Effects vs. Random Intercept Poisson Pseudo-Maximum Likelihood. Applied Economic Letters, 23, 761-764. https://doi.org/10.1080/13504851.2015.1105916
[29]	Keum, K. (2010) Tourism Flows and Trade Theory: A Panel Data Analysis with the Gravity Model. The Annals of Regional Science, 44, 541-557. https://doi.org/10.1007/s00168-008-0275-2
[30]	Gujarati, D. (2003) Basic Econometrics. 5th Edition, McGraw-Hill, New York.
[31]	Halunga, A.G., Orme, C.D. and Yamagata, T. (2017) A Heteroskedasticity Robust Breusch-Pagan Test for Contemporaneous Correlation in Dynamic Panel Data Models. Journal of Econometrics, 198, 209-230. https://doi.org/10.1016/j.jeconom.2016.12.005
[32]	Clark, S. and Linzer, D.A. (2012) Should I Use Fixed or Random Effects? Emory University, Atlanta, 10-11.
[33]	Baltagi, H. (2008) Econometric Analysis of Panel Data. 3rd Edition, John Wiley & Sons, Hoboken.
[34]	Frondel, M. and Vance, C. (2010) Fixed, Random, or Something in between? A Variant of Hausman’s Specification Test for Panel Data Estimators. Economics Letters, 107, 327-329. https://doi.org/10.1016/j.econlet.2010.02.007
[35]	Egger, P., Larch, M., Staub, K.E. and Winkelmann, R. (2011) The Trade Effects of Endogenous Preferential Trade Agreements. American Economic Journal: Economic Policy, 3, 113-43. https://doi.org/10.1257/pol.3.3.113
[36]	Sultan, M. and Munir, K. (2015) Export, Import and Total Trade Potential of Pakistan: A Gravity Model Approach. 13.