ECON4018 International Trade

Introduction

Economists frequently use the gravity model to understand Trade in a globalized world. Jan Tinbergen first presented the model in 1962 that suggested bilateral trade size flowing between two nations can be estimated using gravity equation based on Newton's theory of gravity (Chaney 2018, p. 160). The countries are attracted to each other in international trade by size and proximity (Baldwin and Harrigan 2011, p. 3). The gravity model was first presented as an intuitive way to understand trade flow. The basic form of gravity model can be written as log Xij = c + b1 log GDPi + b2 log GDPj + b3 log τij + eij (1a), log τij = log (distanceij) (1b), log Xij = c + b1 log, GDPi + b2 log GDPj + b3 log τij + eij (1a), log τij = log (distanceij). Xij shows the exports from a country i to country j with respective GDPs ij. The imports and export in countries are directly proportional to GDP and inverse to the distance between them (Shepherd 2013, p. 4). Therefore, this theory implies that large countries will pair to trade more, and the countries that are far away trade less due to higher transportation costs. This paper explains why even though two nations can have the same populations, trade as a GDP ratio can vary using the U.K. and Tukey.

The U.K. economy depends on foreign trade. The regime backs free and unconstrained trade, making the U.K. support international trade organizations such as E.U. and World Trade Organization. Due to U.K.'s economy reliance on foreign trade and investment, it has less foreign trade restrictions. The U.K. has the 500 largest companies, and 60 of them are American. EU is the U.K.'s leading trading partner. The U.K.'s largest single market is the U.S. that accounts for 13% of its imports. As a joined group, the EU, Germany, France, and the Netherlands provide 53%, 13%, 9%, and 7% of British imports, respectively (Baier, Kerr, and Yotov 2018). Additionally, the U.K. has trade treaties with more 90 different countries.

The international trade did not play a major role in the Turkish economy before 1980. Following financial modifications that encouraged the liberalization of foreign trade, international trade in Turkey has grown rapidly. These reforms aimed to eliminate price controls, reduce subsidies, decrease tariffs, and enhance exports. Apart from a rapid increase in imports and exports, the reforms led to a change in the foreign trade structure to the dominance of agricultural products and shifted concentration to industrial products. Turkey is a member of the WTO, and it has signed treaties with other nations to increase its presence on a global stage.

Gravity models of Trade

The macroeconomic theories of international trade emphasize volume, such as value in dollars, when discussing trade between two nations. Economic literature has perceived trade volumes as more informative than topology in ITN; the diversity of trade size exists between various pairs of nations. It is not entirely explained by dual explanation where all networks have the same weight. More stress is on elucidating the trade size expected between two nations, provided particular dual and nation-particular macroeconomic features based on this contention.  The Gravity Model's goal is to deduce trade volume based on Gross Domestic Product (GDP) knowledge, geographical distance between countries, and additional dual factors of macroeconomic significance.

The G.M. forecast that if i and j denote two nations (i, j=1,…N and N represents all nations in the world), the trade volume expected from i and j is given by ⟨wij⟩=c GDPαi GDPβjR−γij  c,α,β,γ>0,    (1). The GDPk represents the GDP of nation k, Rij is the physical space between the nations i and j. The direction does not matter in trade between these two countries. Equation 1 becomes more complicated when additional factors covered in the related free parameters favor or restrict trade. As geographical distance and GDP, the factors can be specific to a nation, such as a country's population or dyadic trade agreements, same currency, common language, and shared borders. In the recently suggested Radiation Model (RM), it covers demographical and geographical variables. In addition to trade networks, the R.M. and G.M. have successfully used other systems such as communication networks, traffic and mobility flows, and migration patterns.

The trade volumes assumed by G.M. are in the basic form and expressed by equation 1are in a positive agreement with products flow in the two nations. One of the limitations of the G.M. is that in its basic applications, it cannot yield nil volumes; hence, forecasting a linked system. The G.M. can only be fixed to non-zero weights, the volumes between the connected nations. If applied this way, the approach successfully neglects the practical configuration of the system as an input and output (Almog, Bird, and Garlaschelli 2019, p. 55). Operatively, G.M. is applicable only after independently establishing the trade link. When zero flow is omitted, it implies that data is misplaced on the reasons for low trade as any fit to only positive flows would substantially undervalue the impact of aspects that weaken trade. This issue is critical as the potential half of the probable links are not outlined in the International Trade Network (ITN). A similar issue is associated with R.M.

Although there are extensions and variants of G.M. that generate nil weights and an accurate connection density, these variants steadily do not reproduce the observed network in the trade structure. Meaning that even though these theories yield a precise link, they tend to place the latter in the right system. Even in the general forms, the G.M. largely forecasts a consistent system while the observed structure of ITN is more homogenous and intricate (Almog, Bird and Garlaschelli 2019, p. 55). Conventional experimental signs of this heterogeneity entail wide dissemination of the extent and strength of nations, the rich-club phenomenon, countries with good-connected linked to each other.

Distance and International Trade

Le 2017 conducted a study to examine the impact of commercial distance between nations on two-sided foreign trade and investment from foreign nations with Vietnam (Osakwe et al., 2018, p. 25). The variation in per-capita GDP was utilized as representation for Vietnam with her trading partners. The modified gravity model was appraised using the steps of panel-corrected ordinary errors. Findings showed feedback and a significant positive association (Le 2017). Also, it was found that the geographical space between Vietnam and its business associates had a noteworthy positive impact on the nation's joint business and FDI inflows.

Two possible businesses exist between two sets of nations, areas, or commercial blogs. The prevailing trade utilizes present relative benefits in the two nations or regions, and the second is new trade that defines trade, which did not exist in the past. The critical factors in the new trade include input sharing, the economics of scale, technology transfer, distance, and imitation. Theoretically, economic distance retards trade in two different ways. The Linder effect, the larger the economic distance between the trading nations, can deter joint business as a greater commercial space means larger dissimilarities in demand structure. Nations with dissimilar demand configurations import and export less horizontally segregated goods. Therefore, Le (2017) asserts that bilateral trade volume decreases with an increase in economic distance. Contrary, nations tend to increase their mutual trade if they have the same per-capita incomes as the demand structure match. Compared to the U.K. and Turkey based on their GDP per capita on 2017 data, Turkey had $27000 while the U.K. had $44300. The U.K.'s GDP per capita is twice as much as that of Turkey, and this can explain why the two nations with approximately the same population, trade as a proportion of GDP, are different.

Contrary, based on the Heckscher-Ohlin impact, where differences between two nations represent factor scarcity by per-capita revenue variations, the greater fiscal space can boost trade between trading nations. It is argued that high-income people tend to purchase quality products. Therefore, when a nation has a relative benefit in manufacturing quality goods that can be exported to meet the needs of affluent consumers to any part of the world. On the other hand, inferior goods are shipped by the latter nation to meet the demands of low socioeconomic status consumers abroad. In this model, a positive association exists between vertical intra-industry trade and variations in per capita income. Increases in GDP per capita are substitutes for the variations in comparative income (Wood et al., 2018, p. 560). When the adverse effects outweigh positive effects, the economic distance between trading nations harms trade. The contrary can occur to others, and in such a case, economic distance can impact trade positively. This applies to the case of the U.K. and Turkey. For example, U.K.'s main export to Turkey is iron and steel, and it imports vehicles.

In their argument, Berthelon and Freund (2016), the increase in the effect of distance on trade had grown since the expansion of the world trade as twice starting from the 1980s. It would be that nations are trading large volumes of products that are highly sensitive to distance. Based on highly disaggregated bilateral trade data, it was found that changes in trade composition did not affect which distance influences trade. Contrary, for 25% of the industries, distance is a significant factor (Berthelon and Freund 2016). This shows that higher distance sensitivity of trade is due to the changes in relative trade cost that impact several industries contrary to a shift to more distance-sensitive goods. On the other hand, Cairncross disputed the aspect of distance in international trade (Jordaan 2015, p. 6). The theory has shown an adverse effect of distance on trade. Still, empirical evidence is undecided on these findings as it has shown both positive and negative impacts of distance on international trade.

Gravity Model based on Particular Country Groups

Apart from the GDP and geographical distance that the G.M. model is based on in accounting for the differences in trade between trading nations, other particular factors among nations boost or hinder trade volume. Some of these factors include culture, common language, free trade agreements, and historical ties. Factors such as historic enmities or war hinder trade. In table 2, it shows how Turkey trades with other nations (Beverelli et al. 2018). Equally, if the dummy coefficient is negative and significant, slow business exists between Turkey and that particular nation. The imports and exports of Turkey in the following country group were observed. The first group is the European Union, with countries such as Germany, France, UK, Denmark, and Belgium.

The second group comprises ExSoviet with nations such as Russia, Moldova, Latvia, Georgia, Estonia, Lithuania, Latvia, and Belarus. The third category is Islamic nations with Oman, Malaysia, Libya, Saudi Arabia, Morocco, Palestine, Albania, Egypt, and Indonesia. The last group is C.A. for Central Asian Turkish nations that comprise Uzbekistan, Kazakhstan, Tajikistan, and Azerbaijan.

Table 1: Country Group Analysis

Based on the results in the above table, out of the four group countries, Turkey only has a significantly higher trade volume in both ends with C.A. nations as opposed to the prediction of the model. Turkey has strong bonds with C.A. nations due to the shared history. Turkish people originates from the region; hence, share a common culture (Gencer 2012, p. 3). These results are surprising as Turkey does not have significant trade with E.U. nations despite Turkey joining Customs Union with E.U. members in 1995. However, this is not an implication that Turkey does not transact with E.U members. In absolute terms, Turkey has the most significant trade volume with E.U. nations. The trade ties between Turkey and E.U. nations are high and on the rise due to the close geographical distance between Turkey and these E.U. nations and rising GDP levels.

Relationship between GDP and Trade

Smith (2016) holds that the relationship between trade and GDP for particular nations is not clear. This study examined 15 nations, with 12 of them being advanced economies while three were emerging economies from a period of 1972 to 2014. In table 1 shows the increases in GDP, and nations are ranked based on their percentage increase. The figures in the table were taken as a percentage of GDP for the given period and percentage change in the years computed. Based on the share of the Trade in the economy, it indicates that trade has expanded as a share of the economy in the examined nations apart from Norway. However, the degree of change in the share differs significantly between nations, and it is not linked in any evident way with an increase in GDP (Åžeker 2020, P. 140). For example, trade as a share of the economy in Italy is almost the same as that of France and the U.K., although Italy's GDP has risen far less compared to that of the U.K. in 42 years.

Other factors that explain trade as a proportion of GDP to differ in these two countries are language, currency, and the rich-club phenomenon (Jean, Martin and Sapir 2018, p. 6). For example, Turkish is the language used in Tukey, while the U.K. uses English (Britannica). This can explain why trade between Turkey and the C.A. region is higher than E.U. nations that speak different languages and tend to have a different culture. Turkey's Turkish language is spoken in countries such as Cyprus, Middle East, and Azerbaijani. Another factor is the currency where Turkey uses Turkish Lira. The same currency is used in Northern Cyprus (Akinci 2012). On the other hand, the official currency of the U.K. is the British Pound that is used in Great Britain and other regions (OANDA). Another factor is the rich club phenomenon. This is where the rich nations tend to trade with other wealthy countries.

In 2020, the U.K. sent goods around the world that are worth $ 401.9 billion. The value of these products in 2020 reflects a 2.35 drop since 2016 and a 14.2% drop from 2019 (Workman 2020). Using a continental lens, 54.1% of the exports from the U.K. by value ended in other European nations compared to 46.1% that landed in the E.U. members. 21.2% of the U.K. exports ended in Asia. 16.5% of the goods from the U.K. were shipped to North America. Regions such as Africa and Latin America accounted for 2.1% and 1.3% respectively of the U.K. exports (Workman 2020). The rich-club phenomenon is a crucial factor in how nations trade. For example, a large percentage of the U.K. exports were sent to the U.S. at 14.3%, followed by Germany at 10.2%, and Turkey was at number 14 with 1.6% of the U.K.'s exports. Therefore, big countries in terms of GDP tend to trade among themselves compared to how they trade with other countries with relatively small GDPs.

Based on the gravity model of world trade, two factors: geographical distance and GDP, significantly affect bilateral trade between countries. Based on the G.M., nations that are geographically close to each other and have a relatively high GDP tend to engage more in trade activities. Therefore, countries that are favored by factors in G.M have a high tendency to do bilateral trade. However, other scholars have disputed these relationships, arguing that sometimes geographical distance does not affect international trade. Other factors such as culture, language, and currency have also been found to play a crucial role in determining how the nations trades. These factors can boost or retard trade between countries. It was found that countries that share the same currency, culture, and language tend to have a high volume of trade. The differences in international trade between U.K. and Turkey can be explained by factors such as language, culture, and currency, as Turkey trade more with nations that it shares the same culture than countries in E.U.

List of References

Akinci, O., 2012. Modeling the demand for currency issued in Turkey. Central Bank Review, 3(1), pp.1-25.

Almog, A., Bird, R. and Garlaschelli, D., 2019. The enhanced gravity model of Trade: reconciling macroeconomic and network models. Frontiers in Physics, 7, p.55.

Baier, S.L., Kerr, A. and Yotov, Y.V., 2018. Gravity, distance, and international Trade. In Handbook of International Trade and transportation. Edward Elgar Publishing.

Baldwin, R. and Harrigan, J., 2011. Zeros, quality, and space: Trade theory and trade evidence. American Economic Journal: Microeconomics, 3(2), pp.60-88.

Berthelon, M. and Freund, C., 2016. On the conservation of distance in international Trade. Journal of International Economics, 75(2), pp.310-320.

Beverelli, C., Keck, A., Larch, M., and Yotov, Y., 2018. Institutions, Trade, and development: A quantitative analysis.

Britannica, T. Editors of Encyclopaedia (2020, August 11). Turkish language. Encyclopedia Britannica. https://www.britannica.com/topic/Turkish-language

Chaney, T., 2018. The gravity equation in international Trade: An explanation. Journal of Political Economy, 126(1), pp.150-177.

Gencer, A.H., 2012. Gravity modeling of Turkey's international Trade under globalization. International Trade, SESSION A, 2, pp.31-34.

Jean, S., Martin, P. and Sapir, A., 2018. International trade under attack: what strategy for Europe?. Notes du conseil danalyse economique, (1), pp.1-12.

Jordaan, A.C., 2015. The further the distance, the closer the ties. 1-12

Le, T.H., 2017. Does economic distance affect the flows of trade and foreign direct investment? Evidence from Vietnam. Cogent Economics & Finance, 5(1), p.1403108.

Liu, A., Lu, C. and Wang, Z., 2020. The roles of cultural and institutional distance in international Trade: Evidence from China's Trade with the Belt and Road countries. China Economic Review, 61, p.101234.

OANDA. United Kingdom Pound. https://www1.oanda.com/currency/iso-currency-codes/GBP

Osakwe, P.N., Santos-Paulino, A.U. and Dogan, B., 2018. Trade dependence, liberalization, and export diversification in developing countries. Journal of African Trade, 5(1-2), pp.19-34.

Åžeker, A., 2020. The Impacts of Liner Shipping Connectivity and Economic Growth on International Trade Case of European Countries and Turkey. In Handbook of Research on the Applications of International Transportation and Logistics for World Trade (pp. 139-150). IGI Global.

Shepherd, B., 2013. The gravity model of international Trade: A user guide.

Smith, J., (2016). The relationship between Trade and GDP? It isn't very easy. https://www.weforum.org/agenda/2016/10/the-relationship-between-trade-and-gdp-its-complicated/

Wood, R., Stadler, K., Simas, M., Bulavskaya, T., Giljum, S., Lutter, S. and Tukker, A., 2018. Growth in environmental footprints and environmental impacts embodied in Trade: resource efficiency indicators from EXIOBASE3. Journal of Industrial Ecology, 22(3), pp.553-564.

Workman, D.(2020). United Kingdom's Top Trading Partners. https://www.worldstopexports.com/united-kingdoms-top-import-partners/

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