Bilateral Trade in the Economies of Various Countries - Essay Example

Abstract The aim of this paper is to analyze the US bilateral trade in terms of the GDP and exports of the trading countries for the two periods, including 1990 and 2011, using panel econometric techniques. In order to achieve this, gravity model with bilateral and time impacts is estimated by SPSS software, on bilateral trade between USA and its major trade partners for two periods of 1990 and 2011. The outcomes show that USA enjoyed a comparative advantage that promoted its exports; however, this seems to have changed after two decades. This is consistent with theoretical dimensions and globalization trends that comparative and competitive advantages in terms of exports are shifting to emerging economies such as East Asia and Africa. Trade flows from traditional partners is becoming complicated. This means that distance is also an important factor in bilateral trade. 1. Introduction Bilateral trade plays an important role in the economies of various countries. However, a country’s trade with another or others depends on a number of factors. Due to the developments in bilateral trade such as regional integration are major factors (Brun, Carrere and Guillaumont 99). These coupled with distance between trading partners they impact on bilateral trade. These forces countries to find viable options that help attract trade flow. This study analyses USA’s situation with relation to its traditional trade partners from Europe, Asia, Africa and South America. The countries used in this study were selected on the basis of their GDP, exports volume and traditional trading activities with the USA. As a result, bilateral trade flows’ gravity model is estimated between USA and its trading partners for the periods 1990 and 2011. The general hypothesis is: H0: The general GDP and comparative advantage in factor endowments promoting exports as standard factors of determining trade flow have an impact on the USA bilateral trade. 2. Literature review The gravity equation is considered probably the most applied empirical trade device for more than a decade now. Its main advantage is that when it is applied to an extensive variety factors and goods moving across national and regional borders under varying circumstances, it always produces a good fit (Anderson and Eric 170). This makes it an ideal device in econometric methods of measuring trade. Nonetheless, with the emergence of new economic theory, advancement in international trade, and globalization, a unique empirical application of the model with respect to a country’s GDP and export capability in bilateral trade is required. The Gravity trade model helps in predicting the flows of the bilateral trade and these predictions are with the distance between units in their respective economic directions as the basis of prediction. The model can be applied in various circumstances to provide an explanation in the flow of commodities on the international scale (Anderson 106). As a result, a number of theoretical and empirical studies have been done to offer an explanation for gravity equation model as applied to commodities. In one attempt to provide a theoretical explanation, expenditure systems’ properties with a maintained hypothesis of similar homothetic preferences across areas (regions) were used. Products are differentiated via their place of region. The gravity model restraints the pure expenditure system via specifying that the portion of national expenditure that went to tradable (openness to trade) refers to a stable unrecognized reduced-form function of population and income (Anderson 107-110). The portion of total tradable commodities expenditure accounted for by every tradable commodity class across regions is a recognized (via preference) transit cost variables function. Partial recognition is attained (Bjelic and Mitrovic 267). It was concluded one can derive the gravity equation from expenditure systems’ properties. However, the method applied in that study, is at the extensive limited to the countries whose structure of traded-commodities preference is very identical and subsidiary, in countries whose transport cost and trade tax structures are identical. As much as this attempt sounds correct empirical generation of the model is required especially for estimating efficiency. The developments in the research into the usefulness of the gravity equation of trade show that it can be estimated for a sample of reference nations and its parameters employed in predicting the expected trade flows amid the CMEA member countries and western Europe. In searching for the potential to real trade ratios far in beyond unity, these early studies arrived at a conclusion in support of a large expansion of the future trade of CEE-EU (Waterlund and Wilhelmsson 641). Particularly, the standard gravity model customary determinacy explains bilateral trade flows via the economic size of two nations and the distance between them. For instance the break-up of the Soviet Union created the necessity to use the gravity model (Silva and Tenereyro 641-2). A number of studies have concluded that regional economic integrations and trade agreements have a strong positive impact on bilateral trade flows (Serlenga and Shin 361-4). More studies still analyze the effects characterizing bilateral trade agreements or international trade regimes falling under the WTO auspices (Rose 98-9). 3. Methodology To ascertain the study claim we used the gravity model. It forecasts bilateral trade flows according to the economic magnitudes of (always GDP measurements) and distance amid two units (Fidrmuc 436). The fundamental theoretical model the trade amid two countries is written as. Here (Trade flow), (Constant), (Country i and j), (distance between units), and (Economic mass = the GDP). The model has been employed in international relations to assess the impact of treaties as well as alliances on trade, patterns of trade and testing the effectiveness of trade organizations and agreements (the world trade Organizations (WTO) and North American Free Trade Agreement (NAFTA)) (Papazoglou, Pentecost and Marques 1077-9). The modified linear form (Y=a +bX) employable in econometric analysis in logarithms is: ln(Bilateral Trade Flow)=a + ßln(Gross Domestic Product of Country1) +ßln(Gross Domestic Product of Country2) -ßln(Distance) +e. From an empirical perspective the Gravity Model of Trade has been successful. Nonetheless, some reservations have been raised concerning the theoretical justifications which have been advanced in favor of the model (Evenett and Keller 281-4). As a beginning point we used the World Trade Organization report, in approximating the GDP, exports and distance relating to each country involved in the study. Using the World Trade Organization’s report and the data base of the US bureau of statics on bilateral trade including records of annual trade activities from 25 countries, we continued to extend these outcomes to the USA. Using the calculated figures in these records we were able to generate respective GDP, exports and distance between units. The estimates were not adjusted. These set of data gave the dependent and independent variables. 4. Data and Analysis Correlation and multiple regression analyses The key statistical measure employed is the correlation coefficient. Fundamentally, correlation analysis involves finding out the presence or lack of relationship and with determining respective magnitude and direction (Saunders, Lewis and Thornhill 112-3). So as to identify the largely contributing of this relationship amongst the variables, the study employed multiple regressions. Multiple regressions refer to a statistical technique for predicting the variance in one dependent variable caused by two or above independent variables. It means that correlation demonstrates the existence of the relationship between the variables whilst the multiple regressions show the most crucial variables for this relationship. Hypothesis Testing The outcome of SPSS involved two coefficient tables, ANOVAs and model summaries. The main hypothesis proposed for answering study questions was tested by both multiple regressions and correlation. Regression output: 1990 Table 1: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .827a .684 .655 412698409.847 a. Predictors: (Constant), Exports ($’000’)-1990, Distance from USA capital The overall regression statistics in Table 1: confirm/demonstrates the quality of the regression estimates and the model. Multiple R2=0.827: there is a correlation and is of high intensity between trade flows. Keeping the hypothesis of the study, correlation analysis results show that the variable has a positive correlation and is greatly correlated with GDP. The 68.4% of the GDP growth rate variation is explained by the difference of the exported goods in a trade flow. This demonstrates that the variable has a strong influence on GDP. Therefore, the correlation analysis outcome supported the hypothesized relationship which was developed under this research. The results of the correlation indicated the existence of the relationship among the variables; however, it did not identify the most crucial variable relationship. Multiple regression analysis was used to determine the importance of the independent variable and what it contributes to the mathematical model as shown in 1990 tables 2 and 3. Table 2: ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 8105543270916580400.000 2 4052771635458290200.000 23.795 .000b Residual 3747039504794250200.000 22 170319977490647744.000 Total 11852582775710830000.000 24 a. Dependent Variable: GDP ($ ‘000’) -1990 b. Predictors: (Constant), Exports ($’000’)-1990, Distance from USA capital Table 2 consists of a panel of analysis of the related regression estimates up to significance 1%. Table 2 shows the value measuring significance as far as the general model is (significance F=0.000), showed that the model is statistically significant. Table 3: Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 248728660.467 187057593.772 1.330 .197 Distance from USA capital -28728.895 29391.612 -.117 -.977 .339 Exports ($’000’)-1990 5.606 .823 .816 6.808 .000 a. Dependent Variable: GDP ($ ‘000’) -1990 From the table 3 the standard error is 187057593.772 the estimated coefficient, 29391.612 for the constant value and 0.823 for b. According to the contribution exports has a strong contribution of (Beta=0.816; sig. =0.000; R2 =0.684) which explains 68.4 percent of the dependent variable (GDP). Regression output: 2011 Table 4: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .314a .099 .013 52084154350.858 a. Predictors: (Constant), Exports ($’000’)-2011, Distance from USA capital The overall regression statistics from table 4 confirm/demonstrates the low quality of the regression estimates and the model. Multiple R2=0.314: there is a correlation and is of low intensity. Keeping the hypothesis of the study, correlation analysis results show that the variable has a positive correlation and is greatly correlated with GDP. 9.9% of the GDP growth rate variation is explained by the difference of the exported goods in a trade flow. This demonstrates that the variable has an influence on GDP. Therefore, the correlation analysis outcome supported the hypothesized relationship which was developed under this research. As above multiple regression analysis was used to determine the importance of the independent variable and what it contributes to the mathematical model as shown in 2011 tables 5and 6. Table 5: ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 6238142612215890000000.000 2 3119071306107945000000.000 1.150 .336b Residual 56967941823324530000000.000 21 2712759134444025000000.000 Total 63206084435540420000000.000 23 a. Dependent Variable: GDP ($ ‘000’) -2011 b. Predictors: (Constant), Exports ($’000’)-2011, Distance from USA capital Table 5 consists of a panel of analysis of the related regression estimates up to significance 5% or 95% confidence level. Table 5 shows the value measuring significance as far as the general model is (significance F=0.336), showed that the model is not statistically significant. Table 6: Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -14305286148.393 25787692044.120 -.555 .585 Distance from USA capital 5495327.868 3868580.733 .295 1.421 .170 Exports ($’000’)-2011 -13.828 31.821 -.090 -.435 .668 a. Dependent Variable: GDP ($ ‘000’) -2011 From table 6 the standard error is 25787692044.120 the estimated coefficient, 3868580.733 for the constant value and 31.821 for b. According to the contribution exports have a weak contribution of (Beta=-0.090; sig. =0.668; R2 =0.099) which explains 9.9 percent of the dependent variable (GDP). 5. Discussion and conclusion It was concluded that the amount of exports in a trade flow influences bilateral trade. There exists a positive relationship between the dependent and independent variables. Increased amounts of exports result in the growth of trade flow as measured by the change in GDP. Considering the results of the hypothesis show T-value, significance level, the beta-value, and the decision, it indicates that exports have on a significant direct influence or impact on the GDP. However, our hypothesis asserts and supports that there is a strong trend amongst countries in the sample for impact or influence of exports (Deardorff 7-8). This implies that the independent variable in our study has significant impact on the GDP of a country and hence its trade flow. Flow of trade between countries is affected by various factors but the Gravity Model for trade flows confirms the dimension the GDP as explained by exports on countryi and countryj. Word Count=2089 Works Cited Anderson, James E. ""A Theoretical Foundation for the Gravity Equation"." American Economic Review (1979): 106-116. Print. Anderson, James E and van Wincoop Eric. ""Gravity with Gravitas: A solution to the Border Puzzle"." American Economic Review (2003): 170-192. Print. Bjelic, Predrag and Dragutinovic Radmila Mitrovic. "The effects of competing trade regimes on bilateral trade flows: case of Serbia." Zb. rad. Ekon. fak. Rij. (2012): 267-294. Print. Brun, J, et al. ""Has Distance died? Evidence from a panel gravity model"." World Bank Economic Review (2005): 99-120. Print. Deardorff, A. Determinants of Bilateral Trade: Does Gravity Work in a Neoclassical world?" In: J.A. Frankel, ed., The reorganization of the world economy. Chicago: niversity of Chicago Press, 1998. Pp. 7-22. Print. Evenett, S and W Keller. "On theories explaining the success of the gravity equation." Journal of Political Economy (2002): 281-316. Print. Fidrmuc, J. 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Appendix 1 Data   Country Exports ($’000’) GDP ($ ‘000’) Distance from USA capital     1990 2011 1990 2011 km 1 Australia 8240657 27512235 311,393,612 1,386,889,147 7130 2 Denmark 37,461,225 112,783,917 135,838,154 333,616,014 6535 3 Finland 27109779 78794204 138,883,532 262,054,166 6934 4 France 211075257 581541871 1,244,163,956 2,779,719,500 6194 5 Germany 408688919 1482202284   1,714,470,068 3,624,861,111 6739 6 Greece 8576811 31711070 93,345,420 289,627,362 8244 7 Hungary 10690182 115052599 33,056,134 137,448,696 7368 8 Israel 19047405 67861836 52,490,325 258,216,880,939 9449 9 Italy 166961471 523175093 1,138,091,138 2,195,014,082 7273 10 Japan 286949597 823292454   3,103,698,099 5,896,794,887 10960 11 Korea 98188623 555275148 263,776,986 1,114,471,962 11218 12 Mexico 27473834 349569057 262,709,785 1,159,889,566 3024 13 Netherlands 145279255 530575753 294,871,112 832,009,722 6198 14 New Zealand 9272744 37633151 45,043,399 162,634,645 14058 15 Norway 35615867 159360549 117,623,688 162,634,645 6260 16 Poland 13181208 183291968 64,549,596 515,666,869 6599 17 Spain 57832461 247565556 520,968,164 1,453,231,944 6105 18 Sweden 58870965 187179008 248,424,511 536,293,220 6647 19 turkey 13221802 134888284 150,676,291 774,775,177 8782 20 United Kingdom 22284238 55939446 1,019,307,528 2,478,930,645 5932 21 China 4232965 103878414 356,936,901 7,321,935,025 9338 22 Malaysia 3175420 14217348 44,023,808   15350 23 Saudi Arabia 3950055 13819521 116,778,111 289,258,937 10899 24 India 2210518 21627594 326,608,014 1,872,840,195 12106 25 Singapore 7728442 31373730 36,092,962 245,024,318 15572 Read More