A NEW LOOK AT THE GRAVITY MODEL.docx

Term paper of econometricsA NEW LOOK AT THE GRAVITY MODEL:A COMPARISON BETWEEN NATURE AND POLICY FACTORSI.Introduction Three hundred and twenty years ago, Sir Isaac Newton, the talented British mathematician and physicist, put forward the universal law of gravitation based on the Kepler Theorem and the Newton Second Law marking a milestone in nature science.The law states the following: Fg=GM1M2r2where Fg is the gravity between the masses ,G is the gravitational constant, M are the masses, and r is the distance between the centers of the masses.Thirty six years ago, Dutch physicist, economist Jan Tinbergen creatively introduced this model to trade field with some amendment. Instead of the mass of two objects, he used the GDP of each country and made the constant G and exponent of distance vary in different situation. The modified gravity model is written asTrade=AGDPiaGDPjbDistc Amazingly, the result of the regression is perfect, every variable being significant. Whats more, in elasticity regression model the coefficient of GDP is nearly 1, and that of distance is nearly -1. The gravity model of trade has empirically been quite successful, widely used to predict international trade since it was first introduced by Tinbergen (1964). In recent years, gravity models are applied in examining the determinants of bilateral trade flows and estimating trade potential of various trading blocs as well as in country cases in globe.Mahfuz Kabir and Ruhul Salim (2010) successfully explained the trading pattern of BIMSTEC by employing an augmented gravity model following Egger (2000, 2002), Baltagi et al (2003) and Serlenga and Shin (2007). The regression estimates follow the Linder hypothesis, support Heckscher-Ohlin-Samuelson prototype. Some factors are still reasonable and feasible when conducted in other economic blocs. Michael M Tansey and Alhagie Touray (2010) focused on trade of African countries which are quite close to each other in both distance and development level to find out if the gravity effects still persist and test if trade patterns still reflect the legacy of the colonial past. At last, they found developing countries can expect gravity effects to be at work if they are able to grow. Old trading relationships such as historical colonial ones can still be perceived even seventy years later, but statistically these relationships appear only weak. In the first stage, this investigation will appraise and compare the discrete influence of each countrys GDP on export and import respectively. Then, the main objective of the paper is presented by testing and comparing which kind of factor, the nature-driven or policy-driven one, has more intensive and powerful effect on total bilateral trade amounts. Based on the gained regression results, we will give some policy recommendations about how to maintain positive phenomenon and reduce or even eliminate trade barriers. To muffin noises originated from specific trade blocs, we choose sample countries around the world including both developing and developed countries.The rest of the paper is organized as follows. Section II discusses the econometric specification and data source. Analysis and results are presented in Section III followed by conclusive remarks in Section IV. Finally, Section V and VI provide references and appendices.II.Model and DataA.Econometric Specification Not long after gravity model was applied in trade field, derivations absorbing additional variables other than GDP or per capita GDP of each country and their mutual distance are widely used to explore the drives and potential of bilateral trade flow. However, we have not found any definitive evidence suggesting the respective impact of each country either on the export or on the import. Furthermore, we are to specify an augmented model to estimate and compare the effect of nature-driven factors and policy-driven ones on the bilateral trade. Specifically, our models are as following: lnTRij=0 +1 lnGDPi+2GDPj+ 3DISTij+u (+) (+) (-) lnTRji=0 +1 lnGDPi+2GDPj+ 3DISTij+u (+) (+) (-) lnTR=0 +1 lnGDPi+2GDPj+ 3DISTij+ 4lnTAR+5SRTA +6CL+7CB+u (+) (+) (-) (-) (+) (+) (+) where TRij denotes the trade flows from country i to country j and TRji denotes the trade flows from country j to country i; TR without any subscript is the sum of the trade amount of both two trading countries (note that each TR is measured by hundred million dollars); GDPi is the gross domestic production of country i and the similar meaning for the country with a subscript j also using hundred million dollars; DISTij means the mutual distance between country i and country j with kilometer as an measurement; TAR signifies tariff ; SRTA stands for the same regional trade area; CL indicates common official language; CB bespokes common border. As the original model is stated like TR=BGDPiGDPjDistn where exponent n and constant B vary with specific conditions, we must use logarithm to separate the every variable and to add new ones, which is why we use the log form instead of the level form. Also, the logarithm is applied to tariff due to the scatter plot revealing the relation between lnTR and TAR. You can refer to the scatter plots in Appendices. Plus, three binary variables are defined as: SRTA =1 if the two countries trading with each other belong to the same regional trade area =0 if otherwise CL =1 if the two countries trading with each other share the same official language =0 if otherwise CB =1 if the two countries trading with each other share a period of common border =0 if otherwise As the sign under each coefficient indicates, we have already predicted the relationship between that explanatory variable and the explained variable. Higher GDP is likely to have a positive effect on trade amount because it is closely related to higher average per capita income, which indicates a higher level of economic development, which increases the demand for differentiated products and increases the intra-industry trade (Bergstrand, 1990). Due to transportation cost, the larger the distance, the less likely bilateral trade is to occur. Tariff imposed is to incur a higher price, thus decreasing the consumption demand. As a result, tariff is negatively related to trade amount.As mentioned above, these three binary variables, membership of the same regional trade area The reason why we put emphasis on regional association instead of global one like WTO is that we want to differentiate data collected from those most of which are members of WTO., shared border and common language are supposed to increase bilateral trade amount. Two countries belonging to the same trade area enjoy some beneficial trade agreement and thus lower the cost. Shared border also cut the transportation cost and make the access to markets easier. Common language eliminates the barriers by smoothing communication difficulties.B.Data SourceWe start with selection of sample countries scattering around the world including both developed and developing countries in order to embrace comprehensible performances and to conclude more objectively. Considering that we are studying the bilateral trade amount, we only choose 15 sample countries each of which can be rearranged with every one of the rest. In this way can we obtain 105 observations (including some unavailable one later should be expelled). Data on bilateral trade amounts of sample countries are gathered from the UN Database. Data on all countries GDP and tariff rate in 2008 are collected from the World Trade Organization. Data of mutual distance between two countries are come from an internet site (for example, /distance/from/China/to/Mexico, can be used to find the distance between China and Mexico). Information needed to define the three binary variables is all gathered from Wikipedia.III.Analyses and Results I began the empirical estimation of the model by performing the Breusch-Pagan test, in which I tested for heteroscedasticity in all the regressions, and concluded that the error terms of each regression were correlated with the regressors. I, therefore, corrected the error term of all regressions for heteroscedasticity using Whites procedure. Hence the “t” and the “F-statistics” are asymptotically valid here. In the first case, we want to see whether the standard gravity model still holds in the condition we set and whether the coefficients on GDP of import and export countries are the same which is assumed in some gravity model studies (Yamarik and Ghosh, 2005, Lee and Park, 2005). We begin by estimating the standard model separately for the import amount (denoted as country i), the export amount (defined as country j) and the total trade amount between i and j. This was motivated by the observation from the discussion on the trade indices that the pattern of imports and exports differs. Table 1 reports the OLS estimates of the standard gravity equation using all the countries in the sample. The first column contains the results for imports, as we can see in the table, all variables have the right signs as predicted and are statistically significant under 1% level. The second and third columns report the result for exports and total trade respectively. All variables attest our expectation about their signs and are both practically and statistically significant. Overall, the results support the predictions of the gravity model. Based on the R2, the standard gravity model can explain the variations in trade amount by more than 70%. Then we perform the Wald coefficient test to see whether two trading partners GDP have the same effect. In the import regression, with the F statistic of 7.282 and p-value of 0.0082 we have a strong evidence to reject the null hypothesis of equality of the GDP coefficients between import and export countries are the same, but in the export regression, with the p-value of 0.6779, we fail to reject the null hypothesis, thus the conclusion about the equality cannot be made. In the second case, the equation is estimated by adding two natural factors, common language (CL) and common border (CB), into the original model to see how much the two matter. As indicated, the coefficients of GDPi, GDPj and distance do not change much with their p-value remaining zero, and CL is both practically and statistically significant meaning that if two counties do share a common language, their bilateral trade is to increase by 53.7% keeping other variables unchanged, which verifies our initial hypothesis. However, CB is statistically insignificant with a p-value higher than 25% against our prediction in spite of its large coefficient. This is a strong evidence for us to accept the null hypothesis and then drop the variable after which the model provides a more meaningful outcome in which CL is associated with both a large coefficient and a trivial p-value. Then we should estimate the policy-driven gravity model with additional tariff and a binary variable whether or not two countries belong to the same regional trade area.The negative sign of tariff is in accordance with our expectation, revealing that as tariff rises by 1%, the total trade will decrease by about 0.53%, other things remaining the same. As it is, the elasticity of trade flow on tariff appears trivial, but notice that we use a unit of hundred million, i.e. when tariff goes up by 1%, trade flow between the two countries is very likely to decline about 53 million. Another newly added variable SRTA is found to be practically and statistically significant with a 5% significance level. The outcome indicates that the trade amount is to hike up by 41% due to the communal membership of one regional trade area. Meanwhile, other original variables stay statistically significant whereas the magnitude of their coefficients drop taking into account the explanatory roles by those two extra policy-driven factors. Table.1 In the third case, we shall put those two sorts of elements into the preliminary gravity model in order to compare which one takes the lead in determining and so activating bilateral trade amount. As it appears, all variables except for CL and CB turn out to be significant coinciding with our previous findings when a 5% significance level is applied. CL, the nature-driven factor, is surprisingly found on the brink of insignificance with a p-value around 15%. CB is apparently insignificant, its t statistic being 1.447. We then run a LM statistic to test the joint hypothesis that both coefficients on CL and CB equal zero. The result shows that, the null hypothesis cannot be rejected, which means the natural factors are not significant. Holding other factors constant, the estimated equation indicates a 40.40% increase to trade flow will occur if the two countries belong to the same regional trade area, and a 0.511% decrease if the tariff increases by 1% which as effect of the policy-driven elements exceeds the influence of nature-driven factors. Explained Variable: lnTRA Explanatory Variable Case 1 Case 2 Case 3 constant -3.942 -1.580 -2.410 (1.404)* (1.54) (-1.582) lnGDPi 0.889 0.717 0.754 (0.072)* (0.088)* (0.090)* lnGDPj 0.687 0.609 0.592 (0.056)* (0.054)* (0.056)* lnDIST -0.716 -0.617 -0.558 (0.119)* (0.107)* (0.119)* lnTAR -0.530 -0.511 (0.126)* (0.128)* SRTA 0.414 0.380 (0.158)* (0.157)* CL 0.537 0.225 (0.175)* (0.168) CB 0.588 0.689 (0.517) (0.476) R-square 0.793 0.788 0.798 Adjusted R-square 0.783 0.775 0.781 F-statistic 76.093* 63.300* 46.971* N= 105 91 91 * Indicates statistical significance at the 0.01 level. * Indicates statistical significance at the 0.05 level. * Indicates statistical significance at the 0.10 level. Table.2 IV.ConclusionThis paper presents a model to test whether the standard gravity model still holds nowadays and compare the extent to which natural and political factors can influence bilateral trade amount.The main findings from the empirical analysis can be summarized as follows. First, the gravity model still persists, and conclusion about whether coefficients on the GDP of import and export countries are the same remains implicit. This result does not accord that well with the findings in the gravity model and the test for the regional integration effect: the case of Tanzania, which said the coefficients are not equal. Second, the nature and policy factors are both proved to have effect on the trade. However, political effects are more intensive, proping our initial hypotheses. Based on the fact that policy-driven factors matter much more than nature-driven ones, we have suggestions aiming at activating and maintaining larger trade amount in developing countries. For these countries, they can take advantage of its labor source or mine source to satisfy the large demand in developed countries by means like producing manufactured goods transferred from advanced world. Also, that trading with less distant nations can cut the cost and establish easier access to others market, thus picking up momentums to trade more serves as a indispensible reason to encourage frontier trade . Moreover, since tariff plays a negative role in our model, to promote the trade amount entails low tariff which can be achieved by forming or joining the same trade association whether regional or global.V.BibliographyAlexander, B.D. (2009), “The Gravity Model And The Test For The Regional Integration Effect: The Case Of Tanzania”, The Journal of Developing Areas, 43(1), Fall 2009; p.p25-44.Mahfuz, K., Ruhul, S. (2010), “Can Gravity Model Explain BIMSTECs Trade?”, Journal of Economic Integration, 25(1), March